Urmson, C.,
Whittaker, W.,
Harbaugh, S.,
Clark, M.,
Koon, P.,
Testing Driver Skill for High-Speed Autonomous Vehicles,
Computer(39), No. 12, December 2006, pp. 48-51.
IEEE DOI
0612
BibRef
Miller, I.,
Garcia, E.,
Campbell, M.,
To Drive Is Human,
Computer(39), No. 12, December 2006, pp. 52-56.
IEEE DOI
0612
BibRef
Hu, J.S.,
Cheng, C.C.,
Liu, W.H.,
Robust Speaker's Location Detection in a Vehicle Environment Using GMM
Models,
SMC-B(36), No. 2, April 2006, pp. 403-412.
IEEE DOI
0604
BibRef
Zhang, Y.[Yilu],
Lin, W.C.,
Chin, Y.K.S.,
A Pattern-Recognition Approach for Driving Skill Characterization,
ITS(11), No. 4, December 2010, pp. 905-916.
IEEE DOI
1101
BibRef
Riener, A.,
Subliminal Persuasion and Its Potential for Driver Behavior Adaptation,
ITS(13), No. 1, March 2012, pp. 71-80.
IEEE DOI
1203
BibRef
Takeda, K.,
Miyajima, C.,
Suzuki, T.,
Angkititrakul, P.,
Kurumida, K.,
Kuroyanagi, Y.,
Ishikawa, H.,
Terashima, R.,
Wakita, T.,
Oikawa, M.,
Komada, Y.,
Self-Coaching System Based on Recorded Driving Data:
Learning From One's Experiences,
ITS(13), No. 4, December 2012, pp. 1821-1831.
IEEE DOI
1212
BibRef
Windridge, D.,
Shaukat, A.,
Hollnagel, E.,
Characterizing Driver Intention via Hierarchical Perception-Action
Modeling,
HMS(43), No. 1, January 2013, pp. 17-31.
IEEE DOI
1301
BibRef
Okuda, H.,
Ikami, N.,
Suzuki, T.,
Tazaki, Y.,
Takeda, K.,
Modeling and Analysis of Driving Behavior Based on a
Probability-Weighted ARX Model,
ITS(14), No. 1, March 2013, pp. 98-112.
IEEE DOI
1303
BibRef
Malikopoulos, A.A.,
Aguilar, J.P.,
An Optimization Framework for Driver Feedback Systems,
ITS(14), No. 2, 2013, pp. 955-964.
IEEE DOI
1307
automotive electronics; fuel economy; road traffic
BibRef
Schiebi, C.,
Fricke, N.,
Staubach, M.,
Identification and analysis of motives for eco-friendly driving
within the eco-move project,
IET-ITS(7), No. 1, 2013, pp. 46-54.
DOI Link
1307
BibRef
Moreno, A.T.,
Garcia, A.,
Camacho-Torregrosa, F.J.,
Llorca, C.,
Influence of highway three-dimensional coordination on drivers
perception of horizontal curvature and available sight distance,
IET-ITS(7), No. 2, 2013, pp. -.
DOI Link
1307
BibRef
Xiong, C.,
Zhang, L.,
A Descriptive Bayesian Approach to Modeling and Calibrating Drivers'
En Route Diversion Behavior,
ITS(14), No. 4, 2013, pp. 1817-1824.
IEEE DOI
1312
Bayes methods
BibRef
Geyer, S.,
Baltzer, M.,
Franz, B.,
Hakuli, S.,
Kauer, M.,
Kienle, M.,
Meier, S.,
Weissgerber, T.,
Bengler, K.,
Bruder, R.,
Flemisch, F.,
Winner, H.,
Concept and development of a unified ontology for generating test and
use-case catalogues for assisted and automated vehicle guidance,
IET-ITS(8), No. 3, May 2014, pp. 183-189.
DOI Link
1407
BibRef
Ho, C.,
Gray, R.,
Spence, C.,
To What Extent do the Findings of Laboratory-Based Spatial Attention
Research Apply to the Real-World Setting of Driving?,
HMS(44), No. 4, August 2014, pp. 524-530.
IEEE DOI
1407
Accidents
BibRef
Birrell, S.A.,
Fowkes, M.,
Jennings, P.A.,
Effect of Using an In-Vehicle Smart Driving Aid on Real-World Driver
Performance,
ITS(15), No. 4, August 2014, pp. 1801-1810.
IEEE DOI
1410
driver information systems
BibRef
Stahl, P.,
Donmez, B.,
Jamieson, G.A.,
Anticipation in Driving:
The Role of Experience in the Efficacy of Pre-event Conflict Cues,
HMS(44), No. 5, October 2014, pp. 603-613.
IEEE DOI
1411
biomedical measurement
BibRef
Qu, T.,
Chen, H.,
Cao, D.,
Guo, H.,
Gao, B.,
Switching-Based Stochastic Model Predictive Control Approach for
Modeling Driver Steering Skill,
ITS(16), No. 1, February 2015, pp. 365-375.
IEEE DOI
1502
Force
BibRef
Fussl, E.,
Oberlader, M.,
Beanland, V.,
Spyropoulou, I.,
Lenne, M.G.,
Joshi, S.,
Roßger, L.,
Leden, L.,
Underwood, G.,
Carvalhais, J.,
Methodological development of a specific tool for assessing
acceptability of assistive systems of powered two-wheeler-riders,
IET-ITS(9), No. 1, 2015, pp. 12-21.
DOI Link
1503
driver information systems
BibRef
Guo, W.H.[Wei-Hong],
Blythe, P.T.,
Edwards, S.,
Pavkova, K.,
Brennan, D.,
Effect of intelligent speed adaptation technology on older drivers:
driving performance,
IET-ITS(9), No. 3, 2015, pp. 343-350.
DOI Link
1506
automated highways
BibRef
Spyropoulou, I.,
Antoniou, C.,
Determinants of driver response to variable message sign information
in Athens,
IET-ITS(9), No. 4, 2015, pp. 453-466.
DOI Link
1506
behavioural sciences computing
BibRef
He, Y.[Yi],
Yan, X.P.[Xin-Ping],
Wu, C.Z.[Chao-Zhong],
Zhong, M.[Ming],
Chu, D.F.[Duan-Feng],
Huang, Z.[Zhen],
Wang, X.[Xu],
Evaluation of the effectiveness of auditory speeding warnings for
commercial passenger vehicles: A field study in Wuhan, China,
IET-ITS(9), No. 4, 2015, pp. 467-476.
DOI Link
1506
alarm systems
BibRef
Yang, Y.[Yan],
Wong, A.,
McDonald, M.,
Does gender make a difference to performing in-vehicle tasks?,
IET-ITS(9), No. 4, 2015, pp. 359-365.
DOI Link
1506
Driver evaluation.
behavioural sciences computing
BibRef
Wang, J.Q.[Jian-Qiang],
Xiong, C.[Chenfeng],
Lu, M.[Meng],
Li, K.Q.[Ke-Qiang],
Longitudinal driving behaviour on different roadway categories:
An instrumented-vehicle experiment, data collection and case study in
China,
IET-ITS(9), No. 5, 2015, pp. 555-563.
DOI Link
1507
driver information systems
BibRef
Malik, H.,
Larue, G.S.,
Rakotonirainy, A.,
Maire, F.,
Fuzzy Logic to Evaluate Driving Maneuvers:
An Integrated Approach to Improve Training,
ITS(16), No. 4, August 2015, pp. 1728-1735.
IEEE DOI
1508
Computer crashes
BibRef
Haupt, J.,
Kahvežic-Seljubac, A.,
Risser, R.,
Role of driver assistance experience, system functionality, gender,
age and sensation seeking in attitudes towards the safety of driver
assistance systems,
IET-ITS(9), No. 7, 2015, pp. 716-726.
DOI Link
1509
driver information systems
BibRef
Schindhelm, R.,
Schmidt, E.,
Evaluation of the tactile detection response task in a laboratory
test using a surrogate driving set-up,
IET-ITS(9), No. 7, 2015, pp. 683-689.
DOI Link
1509
haptic interfaces
BibRef
Eyssartier, C.,
Acceptability of driving an equipped vehicle with drive recorder:
The impact of the context,
IET-ITS(9), No. 7, 2015, pp. 710-715.
DOI Link
1509
data recording
BibRef
Xu, L.[Li],
Hu, J.[Jie],
Jiang, H.[Hong],
Meng, W.[Wuqiang],
Establishing Style-Oriented Driver Models by Imitating Human Driving
Behaviors,
ITS(16), No. 5, October 2015, pp. 2522-2530.
IEEE DOI
1511
automobile industry
BibRef
Oorni, R.,
Demand for intelligent vehicle safety systems in Europe,
IET-ITS(9), No. 10, 2015, pp. 916-923.
DOI Link
1512
intelligent transportation systems
BibRef
Shi, B.,
Xu, L.,
Hu, J.,
Tang, Y.,
Jiang, H.,
Meng, W.,
Liu, H.,
Evaluating Driving Styles by Normalizing Driving Behavior Based on
Personalized Driver Modeling,
SMCS(45), No. 12, December 2015, pp. 1502-1508.
IEEE DOI
1512
Adaptation models
BibRef
Tang, K.,
Zhu, S.,
Xu, Y.,
Wang, F.,
Modeling Drivers' Dynamic Decision-Making Behavior During the Phase
Transition Period: An Analytical Approach Based on Hidden Markov
Model Theory,
ITS(17), No. 1, January 2016, pp. 206-214.
IEEE DOI
1601
Acceleration
BibRef
Dey, K.C.,
Yan, L.,
Wang, X.,
Wang, Y.,
Shen, H.,
Chowdhury, M.,
Yu, L.,
Qiu, C.,
Soundararaj, V.,
A Review of Communication, Driver Characteristics, and Controls
Aspects of Cooperative Adaptive Cruise Control (CACC),
ITS(17), No. 2, February 2016, pp. 491-509.
IEEE DOI
1602
Sardis Award, Survey. Delays
BibRef
Castaldo, F.,
Palmieri, F.A.N.,
Regazzoni, C.S.,
Bayesian Analysis of Behaviors and Interactions for Situation
Awareness in Transportation Systems,
ITS(17), No. 2, February 2016, pp. 313-322.
IEEE DOI
1602
Analytical models
BibRef
Qi, G.,
Du, Y.,
Wu, J.,
Hounsell, N.,
Jia, Y.,
What is the Appropriate Temporal Distance Range for Driving Style
Analysis?,
ITS(17), No. 5, May 2016, pp. 1393-1403.
IEEE DOI
1605
Analytical models
BibRef
Bender, A.,
Ward, J.R.,
Worrall, S.,
Moreyra, M.L.,
Konrad, S.G.[S. Gerling],
Masson, F.,
Nebot, E.M.,
A Flexible System Architecture for Acquisition and Storage of
Naturalistic Driving Data,
ITS(17), No. 6, June 2016, pp. 1748-1761.
IEEE DOI
1606
Computer architecture
BibRef
Zeng, W.,
Miwa, T.,
Morikawa, T.,
Application of hyperpath strategy and driving experience to
risk-averse navigation,
IET-ITS(10), No. 5, 2016, pp. 338-346.
DOI Link
1608
navigation
BibRef
Wang, D.,
Ma, X.,
Ma, D.,
Jin, S.,
A Novel Speed-Density Relationship Model Based on the Energy
Conservation Concept,
ITS(18), No. 5, May 2017, pp. 1179-1189.
IEEE DOI
1705
Automobiles, Data models, Energy conservation, Mathematical model,
Psychology, Driver's psychological field,
energy conservation concept, fundamental diagram, speed-density, relationship
BibRef
Hansen, J.H.L.,
Busso, C.,
Zheng, Y.,
Sathyanarayana, A.,
Driver Modeling for Detection and Assessment of Driver Distraction:
Examples from the UTDrive Test Bed,
SPMag(34), No. 4, July 2017, pp. 130-142.
IEEE DOI
1708
Autonomous vehicles, Hidden Markov models, Intelligent vehicles,
Sensors, Vehicle safety, Vehicular ad hoc networks, Visualization
BibRef
Zeng, X.,
Wang, J.,
A Stochastic Driver Pedal Behavior Model Incorporating Road
Information,
HMS(47), No. 5, October 2017, pp. 614-624.
IEEE DOI
1709
Hidden Markov models, Mechanical power transmission,
Predictive models, Roads, Sensors, Torque, Vehicles,
Driver attention and cognition,
driver modeling and state detection, hidden Markov models,
intelligent vehicles, stochastic, systems
BibRef
Schnelle, S.,
Wang, J.,
Su, H.J.,
Jagacinski, R.,
A Personalizable Driver Steering Model Capable of Predicting Driver
Behaviors in Vehicle Collision Avoidance Maneuvers,
HMS(47), No. 5, October 2017, pp. 625-635.
IEEE DOI
1709
Feedforward neural networks, Mathematical model,
Predictive models, Road transportation, Trajectory, Vehicles, Wheels,
Collision avoidance, driver modeling and state detection,
driver-automation collaboration, steering, model
BibRef
Wang, W.,
Xi, J.,
Chong, A.,
Li, L.,
Driving Style Classification Using a Semisupervised Support Vector
Machine,
HMS(47), No. 5, October 2017, pp. 650-660.
IEEE DOI
1709
Hidden Markov models, Kernel, Labeling, Optimization,
Support vector machines, Training data, Vehicles,
Driving style classification, longitudinal driving behavior,
nonconvex optimization, quasi-Newton (QN) methods,
semisupervised, support, vector, machine, (S3VM)
BibRef
You, C.,
Lu, J.,
Tsiotras, P.,
Nonlinear Driver Parameter Estimation and Driver Steering Behavior
Analysis for ADAS Using Field Test Data,
HMS(47), No. 5, October 2017, pp. 686-699.
IEEE DOI
1709
Data models, Hidden Markov models, Kalman filters, Roads, Vehicles,
Visualization, Wheels, Extended Kalman filter (EKF), field test,
parameter estimation, two-point visual driver model,
unscented Kalman filter (UKF), wavelet, signal, analysis
BibRef
Liu, H.,
Taniguchi, T.,
Tanaka, Y.,
Takenaka, K.,
Bando, T.,
Visualization of Driving Behavior Based on Hidden Feature Extraction
by Using Deep Learning,
ITS(18), No. 9, September 2017, pp. 2477-2489.
IEEE DOI
1709
driving behavior data
BibRef
Castignani, G.,
Derrmann, T.,
Frank, R.,
Engel, T.,
Smartphone-Based Adaptive Driving Maneuver Detection:
A Large-Scale Evaluation Study,
ITS(18), No. 9, September 2017, pp. 2330-2339.
IEEE DOI
1709
distributed telematics, supervised learning, Driving maneuver
detection, anomaly detection
BibRef
Eboli, L.[Laura],
Mazzulla, G.[Gabriella],
Pungillo, G.[Giuseppe],
Measuring the driver's perception error in the traffic accident risk
evaluation,
IET-ITS(11), No. 10, December 2017, pp. 659-666.
DOI Link
1711
BibRef
Ahlstrom, C.,
Kircher, K.,
A Generalized Method to Extract Visual Time-Sharing Sequences From
Naturalistic Driving Data,
ITS(18), No. 11, November 2017, pp. 2929-2938.
IEEE DOI
1711
Data mining, Gaze tracking, Mirrors, Roads, Uncertainty, Vehicles,
Visualization, Driver behaviour, glance analysis, visual, time-sharing
BibRef
Driggs-Campbell, K.,
Govindarajan, V.,
Bajcsy, R.,
Integrating Intuitive Driver Models in Autonomous Planning for
Interactive Maneuvers,
ITS(18), No. 12, December 2017, pp. 3461-3472.
IEEE DOI
1712
Adaptation models, Autonomous vehicles, Planning,
Predictive models, Roads, Trajectory, Autonomous vehicles,
trajectory optimization
BibRef
Wang, D.,
Pei, X.,
Li, L.,
Yao, D.,
Risky Driver Recognition Based on Vehicle Speed Time Series,
HMS(48), No. 1, February 2018, pp. 63-71.
IEEE DOI
1801
Acceleration, Accidents, Insurance, Man-machine systems,
Time series analysis, Vehicles, Velocity measurement,
risky driving
BibRef
Liu, Y.,
Liu, Q.,
Lv, C.,
Zheng, M.,
Ji, X.,
A Study on Objective Evaluation of Vehicle Steering Comfort Based on
Driver's Electromyogram and Movement Trajectory,
HMS(48), No. 1, February 2018, pp. 41-49.
IEEE DOI
1801
Electromyography, Muscles, Predictive models, Trajectory,
Vehicle dynamics, Vehicles, Electromyogram (EMG),
steering comfort
BibRef
Arbabzadeh, N.,
Jafari, M.,
A Data-Driven Approach for Driving Safety Risk Prediction Using
Driver Behavior and Roadway Information Data,
ITS(19), No. 2, February 2018, pp. 446-460.
IEEE DOI
1802
Computer crashes, Data models, Predictive models,
Real-time systems, Safety, Vehicle crash testing, Vehicles,
regularized multinomial logistic regression
BibRef
Alonso, I.P.[I. Parra],
Gonzalo, R.I.[R. Izquierdo],
Alonso, J.,
Garcia-Morcillo, Á.,
Fernandez-Llorca, D.,
Sotelo, M.Á.,
The Experience of DRIVERTIVE-DRIVERless cooperaTIve VEhicle-Team in
the 2016 GCDC,
ITS(19), No. 4, April 2018, pp. 1322-1334.
IEEE DOI
1804
Acceleration, Automation, Automobiles, Brakes, DC motors, Merging,
Cooperative systems, automatization, autonomous vehicles, control
BibRef
Dolk, V.,
Ouden, J.d.,
Steeghs, S.,
Devanesan, J.G.,
Badshah, I.,
Sudhakaran, A.,
Elferink, K.,
Chakraborty, D.,
Cooperative Automated Driving for Various Traffic Scenarios:
Experimental Validation in the GCDC 2016,
ITS(19), No. 4, April 2018, pp. 1308-1321.
IEEE DOI
1804
Computer architecture, Hardware, Merging, Protocols, Roads,
Cooperative automated driving, GCDC, automated highway systems,
vehicle platooning
BibRef
Benderius, O.,
Berger, C.,
Lundgren, V.M.[V. Malmsten],
The Best Rated Human-Machine Interface Design for Autonomous
Vehicles in the 2016 Grand Cooperative Driving Challenge,
ITS(19), No. 4, April 2018, pp. 1302-1307.
IEEE DOI
1804
Autonomous vehicles, Bars, Legislation, Roads, Safety, Wheels,
Human-machine interface, autonomous autonomous, external interface
BibRef
Kolekar, S.,
Mugge, W.,
Abbink, D.,
Modeling Intradriver Steering Variability Based on Sensorimotor
Control Theories,
HMS(48), No. 3, June 2018, pp. 291-303.
IEEE DOI
1805
Adaptation models, Mathematical model, Muscles, Roads, Task analysis,
Torque, Vehicles, Intradriver variability (IDV),
stochastic optimal feedback
BibRef
Tanaka, T.[Takahiro],
Fujikake, K.[Kazuhiro],
Yonekawa, T.[Takashi],
Yamagishi, M.[Misako],
Inagami, M.[Makoto],
Kinoshita, F.[Fumiya],
Aoki, H.[Hirofumi],
Kanamori, H.[Hitoshi],
Study on Driver Agent Based on Analysis of Driving Instruction Data:
Driver Agent for Encouraging Safe Driving Behavior (1),
IEICE(E101-D), No. 5, May 2018, pp. 1401-1409.
WWW Link.
1805
BibRef
Cunningham, M.L.[Mitchell L.],
Regan, M.A.[Michael A.],
Driver distraction and inattention in the realm of automated driving,
IET-ITS(12), No. 6, August 2018, pp. 407-413.
DOI Link
1807
BibRef
Large, D.R.[David R.],
Burnett, G.[Gary],
Antrobus, V.[Vicki],
Skrypchuk, L.[Lee],
Driven to discussion: engaging drivers in conversation with a digital
assistant as a countermeasure to passive task-related fatigue,
IET-ITS(12), No. 6, August 2018, pp. 420-426.
DOI Link
1807
BibRef
Eren, A.L.[Ayse Leyla],
Burnett, G.[Gary],
Large, D.R.[David R.],
Harvey, C.[Catherine],
Understanding the effects of peripheral vision and muscle memory on
in-vehicle touchscreen interactions,
IET-ITS(12), No. 6, August 2018, pp. 434-439.
DOI Link
1807
BibRef
Engström, J.[Johan],
Markkula, G.[Gustav],
Xue, Q.W.[Qing-Wan],
Merat, N.[Natasha],
Simulating the effect of cognitive load on braking responses in lead
vehicle braking scenarios,
IET-ITS(12), No. 6, August 2018, pp. 427-433.
DOI Link
1807
BibRef
Petzoldt, T.[Tibor],
Schleinitz, K.[Katja],
Banse, R.[Rainer],
Potential safety effects of a frontal brake light for motor vehicles,
IET-ITS(12), No. 6, August 2018, pp. 449-453.
DOI Link
1807
BibRef
Liu, L.[Lei],
Gao, Y.[Yan],
Wang, F.C.[Fu-Cheng],
Road safety analysis for high-speed vehicle in complex environments
based on the viability kernel,
IET-ITS(12), No. 6, August 2018, pp. 495-503.
DOI Link
1807
BibRef
da Lio, M.,
Mazzalai, A.,
Gurney, K.,
Saroldi, A.,
Biologically Guided Driver Modeling: the Stop Behavior of Human Car
Drivers,
ITS(19), No. 8, August 2018, pp. 2454-2469.
IEEE DOI
1808
Biological system modeling, Brakes, Hidden Markov models,
Automobiles, Cognitive science, Driver modeling,
motor primitives
BibRef
Li, Z.,
Bao, S.,
Kolmanovsky, I.V.,
Yin, X.,
Visual-Manual Distraction Detection Using Driving Performance
Indicators With Naturalistic Driving Data,
ITS(19), No. 8, August 2018, pp. 2528-2535.
IEEE DOI
1808
Vehicles, Measurement, Wheels, Entropy, Safety, Kinematics,
Distraction detection, driver modeling,
support vector machines
BibRef
Deng, T.,
Yan, H.,
Li, Y.,
Learning to Boost Bottom-Up Fixation Prediction in Driving
Environments via Random Forest,
ITS(19), No. 9, September 2018, pp. 3059-3067.
IEEE DOI
1809
Predictive models, Vehicles, Visualization, Computational modeling,
Feature extraction, Image color analysis, Vegetation,
traffic driving environments
BibRef
Liao, Y.[Yuan],
Wang, M.J.[Min-Juan],
Duan, L.[Lian],
Chen, F.[Fang],
Cross-regional driver-vehicle interaction design: an interview study on
driving risk perceptions, decisions, and ADAS function preferences,
IET-ITS(12), No. 8, October 2018, pp. 801-808.
DOI Link
1809
BibRef
Khan, W.[Waqar],
Klette, R.[Reinhard],
Accuracy of a Driver-Assistance System in a Collision Scenario,
ISVC18(251-263).
Springer DOI
1811
BibRef
Sun, D.H.[Di-Hua],
He, Y.C.[Yu-Chu],
Zhao, M.[Min],
Cheng, S.L.[Sen-Lin],
Cooperative driving modelling in the vicinity of traffic signals based
on intelligent driver model,
IET-ITS(12), No. 10, December 2018, pp. 1236-1242.
DOI Link
1812
BibRef
Zhu, B.,
Liu, Z.,
Zhao, J.,
Chen, Y.,
Deng, W.,
Driver Behavior Characteristics Identification Strategies Based on
Bionic Intelligent Algorithms,
HMS(48), No. 6, December 2018, pp. 572-581.
IEEE DOI
1812
backpropagation, behavioural sciences computing,
data acquisition, driver information systems, genetic algorithms,
particle swarm optimization (PSO)
BibRef
Christopoulos, S.G.,
Kanarachos, S.,
Chroneos, A.,
Learning Driver Braking Behavior Using Smartphones, Neural Networks
and the Sliding Correlation Coefficient: Road Anomaly Case Study,
ITS(20), No. 1, January 2019, pp. 65-74.
IEEE DOI
1901
Smart phones, Roads, Vehicles, Acceleration,
Global Positioning System, Accelerometers, Correlation,
road condition
BibRef
Yi, D.,
Su, J.,
Liu, C.,
Chen, W.,
Personalized Driver Workload Inference by Learning From Vehicle
Related Measurements,
SMCS(49), No. 1, January 2019, pp. 159-168.
IEEE DOI
1901
Vehicles, Clustering algorithms, Support vector machines, Safety,
Real-time systems, Adaptive systems, Temperature measurement,
workload recognition
BibRef
Yi, D.,
Su, J.,
Liu, C.,
Chen, W.,
New Driver Workload Prediction Using Clustering-Aided Approaches,
SMCS(49), No. 1, January 2019, pp. 64-70.
IEEE DOI
1901
Automobiles, Clustering algorithms, Prediction algorithms,
Predictive models, Data models, Analytical models,
workload inference
BibRef
Han, W.[Wei],
Wang, W.S.[Wen-Shuo],
Li, X.H.[Xiao-Han],
Xi, J.Q.A.[Jun-Qi-Ang],
Statistical-based approach for driving style recognition using Bayesian
probability with kernel density estimation,
IET-ITS(13), No. 1, January 2019, pp. 22-30.
DOI Link
1901
BibRef
Eriksson, A.,
Petermeijer, S.M.,
Zimmermann, M.,
de Winter, J.C.F.,
Bengler, K.J.,
Stanton, N.A.,
Rolling Out the Red (and Green) Carpet: Supporting Driver Decision
Making in Automation-to-Manual Transitions,
HMS(49), No. 1, February 2019, pp. 20-31.
IEEE DOI
1901
Vehicles, Automation, Visualization, Decision making,
Information analysis, Task analysis, Wheels, Augmented reality,
transitions of control
BibRef
Fugiglando, U.,
Massaro, E.,
Santi, P.,
Milardo, S.,
Abida, K.,
Stahlmann, R.,
Netter, F.,
Ratti, C.,
Driving Behavior Analysis through CAN Bus Data in an Uncontrolled
Environment,
ITS(20), No. 2, February 2019, pp. 737-748.
IEEE DOI
1902
Automobiles, Sensors, Real-time systems, Data collection, Accidents,
Global Positioning System, Driving behavior, CAN bus,
drivers segmentation
BibRef
Yu, S.[Shu],
Lü, L.[Lin],
Research on the influence factors of real driving cycle with
statistical analysis and dynamic time warping,
IET-ITS(13), No. 2, February 2019, pp. 286-292.
DOI Link
1902
BibRef
Deng, C.[Chao],
Cao, S.[Shi],
Wu, C.Z.[Chao-Zhong],
Lyu, N.C.[Neng-Chao],
Predicting drivers' direction sign reading reaction time using an
integrated cognitive architecture,
IET-ITS(13), No. 4, April 2019, pp. 622-627.
DOI Link
1903
BibRef
Kim, H.S.,
Yoon, D.,
Shin, H.S.,
Park, C.H.,
Predicting the EEG Level of a Driver Based on Driving Information,
ITS(20), No. 4, April 2019, pp. 1215-1225.
IEEE DOI
1904
Electroencephalography, Vehicles, Brain modeling, Task analysis,
Predictive models, Roads, Physiology, Cognitive workload,
support vector machine
BibRef
Jacob, J.[Johnny],
Rabha, P.[Pankaj],
Driving Data Collection Framework Using Low Cost Hardware,
AutoNUE18(V:617-625).
Springer DOI
1905
BibRef
Bouhoute, A.,
Oucheikh, R.,
Boubouh, K.,
Berrada, I.,
Advanced Driving Behavior Analytics for an Improved Safety Assessment
and Driver Fingerprinting,
ITS(20), No. 6, June 2019, pp. 2171-2184.
IEEE DOI
1906
Automobiles, Safety, Numerical models, Analytical models,
Hidden Markov models, Graphical models, Driving behavior,
graph-based analysis
BibRef
Aliakbarian, M.S.[Mohammad Sadegh],
Saleh, F.S.[Fatemeh Sadat],
Salzmann, M.[Mathieu],
Fernando, B.[Basura],
Petersson, L.[Lars],
Andersson, L.[Lars],
VIENA2: A Driving Anticipation Dataset,
ACCV18(I:449-466).
Springer DOI
1906
BibRef
He, X.[Xin],
Xu, L.[Li],
Zhang, Z.[Zhe],
Driving behaviour characterisation by using phase-space reconstruction
and pre-trained convolutional neural network,
IET-ITS(13), No. 7, July 2019, pp. 1173-1180.
DOI Link
1906
BibRef
Cheng, R.,
Yu, W.,
Song, Y.,
Chen, D.,
Ma, X.,
Cheng, Y.,
Intelligent Safe Driving Methods Based on Hybrid Automata and
Ensemble CART Algorithms for Multihigh-Speed Trains,
Cyber(49), No. 10, October 2019, pp. 3816-3826.
IEEE DOI
1907
Mathematical model, Optimization, Safety, Control systems,
Public transportation, Data mining, Rails,
system safety
BibRef
Wang, W.,
Xi, J.,
Zhao, D.,
Driving Style Analysis Using Primitive Driving Patterns With Bayesian
Nonparametric Approaches,
ITS(20), No. 8, August 2019, pp. 2986-2998.
IEEE DOI
1908
Hidden Markov models, Vehicles, Bayes methods, Vehicle dynamics,
Semantics, Standards, Mechanical engineering, Driving style,
behavioral semantics
BibRef
Figueredo, G.P.,
Agrawal, U.,
Mase, J.M.M.,
Mesgarpour, M.,
Wagner, C.,
Soria, D.,
Garibaldi, J.M.,
Siebers, P.,
John, R.I.,
Identifying Heavy Goods Vehicle Driving Styles in the United Kingdom,
ITS(20), No. 9, September 2019, pp. 3324-3336.
IEEE DOI
1909
Vehicles, Telematics, Roads, Companies, Acceleration, Brakes,
Meteorology, Driver profiling, driving pattern, driving habit,
big data analysis
BibRef
Rhie, Y.L.,
Lim, J.H.,
Yun, M.H.,
Queueing Network Based Driver Model for Varying Levels of Information
Processing,
HMS(49), No. 6, December 2019, pp. 508-517.
IEEE DOI
1912
Task analysis, Queueing analysis, Computational modeling,
Visualization, Information processing, Context-aware services,
levels of processing (LOP)
BibRef
Inder, K.[Katie],
de Silva, V.[Varuna],
Shi, X.[Xiyu],
Learning Control Policies of Driverless Vehicles from UAV Video
Streams in Complex Urban Environments,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link
1912
BibRef
Lu, M.Q.[Ming-Qi],
Hu, Y.C.[Yao-Cong],
Lu, X.B.[Xiao-Bo],
Dilated Light-Head R-CNN using tri-center loss for driving behavior
recognition,
IVC(90), 2019, pp. 103800.
Elsevier DOI
1912
Driving behavior, Dilated convolution, Tri-center loss,
Positive-sensitive RoI alignment
BibRef
Hong, S.,
Lu, J.,
Panigrahi, S.R.,
Scott, J.,
Filev, D.P.,
An Interacting Multiple-Model-Based Algorithm for Driver Behavior
Characterization Using Handling Risk,
ITS(20), No. 12, December 2019, pp. 4308-4317.
IEEE DOI
2001
Vehicles, Vehicle dynamics, Tires, Heuristic algorithms,
Acceleration, Dynamics, Control systems,
active safety
BibRef
Xu, L.,
Zhang, J.,
Shi, B.,
Meng, W.,
Automating Shift-Scheduling Calibration by Using Bionic Optimization
and Personalized Driver Models,
ITS(20), No. 12, December 2019, pp. 4367-4376.
IEEE DOI
2001
Calibration, Vehicles, Biological system modeling, Schedules,
Optimization, Robots, Fuel economy, Calibration,
shift-scheduling
BibRef
Makantasis, K.[Konstantinos],
Kontorinaki, M.[Maria],
Nikolos, I.[Ioannis],
Deep reinforcement-learning-based driving policy for autonomous road
vehicles,
IET-ITS(14), No. 1, January 2020, pp. 13-24.
DOI Link
2001
BibRef
Payalan, Y.F.,
Guvensan, M.A.,
Towards Next-Generation Vehicles Featuring the Vehicle Intelligence,
ITS(21), No. 1, January 2020, pp. 30-47.
IEEE DOI
2001
Next generation networking, Safety, Automobiles,
Autonomous vehicles, Accidents, Self-driving,
contexts and sensors for autonomous vehicle
BibRef
Chen, L.,
Wang, Q.,
Lu, X.,
Cao, D.,
Wang, F.,
Learning Driving Models From Parallel End-to-End Driving Data Set,
PIEEE(108), No. 2, February 2020, pp. 262-273.
IEEE DOI
2001
Data models, Training, Adaptation models, Task analysis,
Reinforcement learning, Decision making, Transforms, Data set,
parallel driving
BibRef
Morando, A.,
Victor, T.,
Dozza, M.,
A Bayesian Reference Model for Visual Time-Sharing Behaviour in
Manual and Automated Naturalistic Driving,
ITS(21), No. 2, February 2020, pp. 803-814.
IEEE DOI
2002
Vehicles, Task analysis, Bayes methods, Manuals, Measurement,
Biological system modeling, Visualization, ADAS, attention,
visual behavior
BibRef
Bejani, M.M.,
Ghatee, M.,
Convolutional Neural Network With Adaptive Regularization to Classify
Driving Styles on Smartphones,
ITS(21), No. 2, February 2020, pp. 543-552.
IEEE DOI
2002
Feature extraction, Smart phones, Acceleration, Training, Vehicles,
Data mining, Deep learning, driving style evaluation,
over-fitting
BibRef
Zhu, Q.,
Research on Road Traffic Situation Awareness System Based on Image
Big Data,
IEEE_Int_Sys(35), No. 1, January 2020, pp. 18-26.
IEEE DOI
2004
Human computer interaction, Convolutional neural networks,
Computational modeling, Task analysis, Big Data,
situational awarenesss
BibRef
van der El, K.,
Pool, D.M.,
van Paassen, M.R.M.,
Mulder, M.,
A Unifying Theory of Driver Perception and Steering Control on
Straight and Winding Roads,
HMS(50), No. 2, April 2020, pp. 165-175.
IEEE DOI
2004
Vehicles, Roads, Visualization, Control theory, Windings,
Vehicle dynamics, Task analysis, Driver steering,
visual perception
BibRef
Huang, X.,
Zhang, S.,
Peng, H.,
Developing Robot Driver Etiquette Based on Naturalistic Human Driving
Behavior,
ITS(21), No. 4, April 2020, pp. 1393-1403.
IEEE DOI
2004
Vehicles, Acceleration, Roads, Robots, Accidents, Safety,
Automated vehicles, human driving behavior, naturalistic driving data
BibRef
Peng, X.S.[Xi-Shuai],
Murphey, Y.L.[Yi Lu],
Liu, R.R.[Rui-Rui],
Li, Y.X.[Yuan-Xiang],
Driving maneuver early detection via sequence learning from vehicle
signals and video images,
PR(103), 2020, pp. 107276.
Elsevier DOI
2005
Driving maneuver early detection, Deep neural networks,
Sequence learning, Advanced driver assistance systems
BibRef
Peng, X.S.[Xi-Shuai],
Liu, R.R.[Rui-Rui],
Murphey, Y.L.[Yi Lu],
Stent, S.[Simon],
Li, Y.X.[Yuan-Xiang],
Driving Maneuver Detection via Sequence Learning from Vehicle Signals
and Video Images,
ICPR18(1265-1270)
IEEE DOI
1812
Vehicles, Feature extraction, Global Positioning System,
Trajectory, Hidden Markov models, Time-domain analysis, Meters
BibRef
Ouyang, Z.,
Niu, J.,
Liu, Y.,
Liu, X.,
An Ensemble Learning-Based Vehicle Steering Detector Using
Smartphones,
ITS(21), No. 5, May 2020, pp. 1964-1975.
IEEE DOI
2005
Driving behavior, ensemble learning, pattern recognition,
vehicle steering, smartphones
BibRef
Deng, T.,
Yan, H.,
Qin, L.,
Ngo, T.,
Manjunath, B.S.,
How Do Drivers Allocate Their Potential Attention? Driving Fixation
Prediction via Convolutional Neural Networks,
ITS(21), No. 5, May 2020, pp. 2146-2154.
IEEE DOI
2005
Videos, Predictive models, Gaze tracking, Visualization, Data models,
Automobiles, Fixation prediction, visual attention, eye tracking,
traffic driving
BibRef
Jia, S.[Shuo],
Hui, F.[Fei],
Li, S.N.[Shi-Ning],
Zhao, X.M.[Xiang-Mo],
Khattak, A.J.[Asad J.],
Long short-term memory and convolutional neural network for abnormal
driving behaviour recognition,
IET-ITS(14), No. 5, May 2020, pp. 306-312.
DOI Link
2005
BibRef
Duan, J.L.[Jing-Liang],
Li, S.E.[Shengbo Eben],
Guan, Y.[Yang],
Sun, Q.[Qi],
Cheng, B.[Bo],
Hierarchical reinforcement learning for self-driving decision-making
without reliance on labelled driving data,
IET-ITS(14), No. 5, May 2020, pp. 297-305.
DOI Link
2005
BibRef
Kashevnik, A.,
Lashkov, I.,
Gurtov, A.,
Methodology and Mobile Application for Driver Behavior Analysis and
Accident Prevention,
ITS(21), No. 6, June 2020, pp. 2427-2436.
IEEE DOI
2006
Vehicles, Sensors, Smart phones, Cameras, Mobile applications,
Monitoring, Safety, Driver behavior, smartphone,
mobile application
BibRef
Zhao, C.,
Wang, W.,
Li, S.,
Gong, J.,
Influence of Cut-In Maneuvers for an Autonomous Car on Surrounding
Drivers: Experiment and Analysis,
ITS(21), No. 6, June 2020, pp. 2266-2276.
IEEE DOI
2006
Autonomous vehicles, Safety, Decision making, Automobiles, Accidents,
Task analysis, Autonomous vehicle, cut-in behavior, human drivers,
driving comfort
BibRef
Pampel, S.M.[Sanna M.],
Southey, T.J.R.[Thomas J.R.],
Burnett, G.[Gary],
Understanding the distraction and behavioural adaptations of drivers
when experiencing failures of digital side mirrors,
IET-ITS(14), No. 7, July 2020, pp. 775-782.
DOI Link
2006
BibRef
Liao, L.C.[Lyu-Chao],
Chen, B.J.[Bi-Jun],
Zou, F.M.[Fu-Min],
Li, S.B.E.[Sheng-Bo Eben],
Liu, J.R.[Jie-Rui],
Wu, X.K.[Xin-Ke],
Dong, N.[Ni],
Hierarchical quantitative analysis to evaluate unsafe driving behaviour
from massive trajectory data,
IET-ITS(14), No. 8, August 2020, pp. 849-856.
DOI Link
2007
BibRef
Carlos, M.R.,
González, L.C.,
Wahlström, J.,
Ramírez, G.,
Martínez, F.,
Runger, G.,
How Smartphone Accelerometers Reveal Aggressive Driving Behavior?:
The Key is the Representation,
ITS(21), No. 8, August 2020, pp. 3377-3387.
IEEE DOI
2008
Accelerometers, Sensors, Acceleration, Vehicles, Machine learning,
Data models, Proposals, Driving analytics, aggressive driving,
insurance telematics
BibRef
Saleh, K.,
Hossny, M.,
Nahavandi, S.,
Contextual Recurrent Predictive Model for Long-Term Intent Prediction
of Vulnerable Road Users,
ITS(21), No. 8, August 2020, pp. 3398-3408.
IEEE DOI
2008
Trajectory, Predictive models, Roads, Reinforcement learning,
Recurrent neural networks, Forecasting, Kalman filters,
reinforcement learning (IRL)
BibRef
Tang, C.,
Xu, Z.,
Tomizuka, M.,
Disturbance-Observer-Based Tracking Controller for Neural Network
Driving Policy Transfer,
ITS(21), No. 9, September 2020, pp. 3961-3972.
IEEE DOI
2008
Robustness, Neural networks, Autonomous vehicles, Trajectory,
Task analysis, Adaptation models, Target tracking,
machine learning
BibRef
Li, Y.C.[Yao-Chen],
Wu, X.[Xiao],
Lu, D.H.[Dan-Hui],
Li, L.[Ling],
Liu, Y.H.[Yue-Hu],
Zhu, L.[Li],
Style Transfer of Urban Road Images Using Generative Adversarial
Networks With Structural Details,
MultMedMag(27), No. 3, July 2020, pp. 54-65.
IEEE DOI
2009
Driving style.
Semantics, Image segmentation, Generators, Roads, Unmanned vehicles,
Training data, Image reconstruction
BibRef
Loiseau, P.[Paul],
Boultifat, C.N.E.[Chaouki Nacer Eddine],
Chevrel, P.[Philippe],
Claveau, F.[Fabien],
Espié, S.[Stéphane],
Mars, F.[Franck],
Rider model identification: neural networks and quasi-LPV models,
IET-ITS(14), No. 10, October 2020, pp. 1259-1264.
DOI Link
2009
BibRef
Chan, T.K.,
Chin, C.S.,
Chen, H.,
Zhong, X.,
A Comprehensive Review of Driver Behavior Analysis Utilizing
Smartphones,
ITS(21), No. 10, October 2020, pp. 4444-4475.
IEEE DOI
2010
Vehicles, Smart phones, Sensors, Monitoring, Acceleration, Electrodes,
Electrocardiography, Driving behavior analysis,
smartphone telematics solution
BibRef
Cheng, X.[Xin],
Zhou, J.M.[Jing-Mei],
Zhao, X.M.[Xiang-Mo],
Safety assessment of vehicle behaviour based on the improved D-S
evidence theory,
IET-ITS(14), No. 11, November 2020, pp. 1396-1402.
DOI Link
2010
BibRef
El Khatib, A.,
Ou, C.,
Karray, F.,
Driver Inattention Detection in the Context of Next-Generation
Autonomous Vehicles Design: A Survey,
ITS(21), No. 11, November 2020, pp. 4483-4496.
IEEE DOI
2011
Vehicles, Automation, Task analysis, Accidents, Monitoring,
Visualization, Vehicle dynamics, Autonomous vehicles, vehicle safety
BibRef
Pozueco, L.,
Gupta, N.,
Pañeda, X.G.,
García, R.,
Tuero, A.G.,
Melendi, D.,
Rionda, A.,
Corcoba, V.,
Analysis of Driving Patterns and On-Board Feedback-Based Training for
Proactive Road Safety Monitoring,
HMS(50), No. 6, December 2020, pp. 529-537.
IEEE DOI
2011
Advanced driver assistance systems,
Intelligent transportation systems, Real-time systems,
sociodemographic influence
BibRef
Fernando, T.,
Denman, S.,
Sridharan, S.,
Fookes, C.,
Deep Inverse Reinforcement Learning for Behavior Prediction in
Autonomous Driving: Accurate Forecasts of Vehicle Motion,
SPMag(38), No. 1, January 2021, pp. 87-96.
IEEE DOI
2012
Deep learning, Navigation, Supervised learning, Decision making,
Focusing, Reinforcement learning, Autonomous vehicles
BibRef
Guo, Y.Q.[Yong-Qing],
Wang, X.Y.[Xiao-Yuan],
Yuan, Q.[Quan],
Liu, S.L.[Shan-Liang],
Liu, S.J.[Shi-Jie],
Transition characteristics of driver's intentions triggered by
emotional evolution in two-lane urban roads,
IET-ITS(14), No. 13, 15 December 2020, pp. 1788-1798.
DOI Link
2102
BibRef
Hong, S.,
Lu, J.,
Filev, D.P.,
Driving Behavior Evaluation for Future Mobility: Application of
Online Transition Probability Estimation,
ITS(22), No. 2, February 2021, pp. 782-791.
IEEE DOI
2102
Hidden Markov models, Vehicle dynamics, Vehicles, Estimation,
Heuristic algorithms, Safety, Control systems, Driving behavior,
future mobility
BibRef
Siami, M.,
Naderpour, M.,
Lu, J.,
A Mobile Telematics Pattern Recognition Framework for Driving
Behavior Extraction,
ITS(22), No. 3, March 2021, pp. 1459-1472.
IEEE DOI
2103
Telematics, Self-organizing feature maps, Clustering algorithms,
Feature extraction, Unsupervised learning,
unsupervised learning
BibRef
Hang, P.,
Lv, C.,
Xing, Y.,
Huang, C.,
Hu, Z.,
Human-Like Decision Making for Autonomous Driving:
A Noncooperative Game Theoretic Approach,
ITS(22), No. 4, April 2021, pp. 2076-2087.
IEEE DOI
2104
Decision making, Vehicles, Games, Safety, Planning, Predictive models,
Nash equilibrium, Decision making, human-like, autonomous vehicle,
model predictive control
BibRef
Nascimento, E.R.[Erickson R.],
Bajcsy, R.[Ruzena],
Gregor, M.[Michal],
Huang, I.[Isabella],
Villegas, I.[Ismael],
Kurillo, G.[Gregorij],
On the Development of an Acoustic-Driven Method to Improve Driver's
Comfort Based on Deep Reinforcement Learning,
ITS(22), No. 5, May 2021, pp. 2923-2932.
IEEE DOI
2105
Vehicles, Psychoacoustics, Psychoacoustic models, Measurement,
Safety, Reinforcement learning, Psychoacoustic metrics,
safety
BibRef
Zhong, S.[Shan],
Wei, M.[Meng],
Gong, S.R.[Sheng-Rong],
Xia, K.J.[Kai-Jian],
Fu, Y.C.[Yu-Chen],
Fu, Q.M.[Qi-Ming],
Yin, H.S.[Hong-Sheng],
Behavior Prediction for Unmanned Driving Based on Dual Fusions of
Feature and Decision,
ITS(22), No. 6, June 2021, pp. 3687-3696.
IEEE DOI
2106
Task analysis, Decision making, Trajectory, Visualization,
Deep learning, Data models, Computer science, Unmanned driving,
decision fusion
BibRef
Chang, X.[Xin],
Rong, J.[Jian],
Li, H.J.[Hai-Jian],
Wu, Y.P.[Yi-Ping],
Zhao, X.H.[Xiao-Hua],
Impact of connected vehicle environment on driving performance:
A case of an extra-long tunnel scenario,
IET-ITS(15), No. 3, 2021, pp. 423-431.
DOI Link
2106
BibRef
Xing, Y.[Yang],
Lv, C.[Chen],
Mo, X.Y.[Xiao-Yu],
Hu, Z.X.[Zhong-Xu],
Huang, C.[Chao],
Hang, P.[Peng],
Toward Safe and Smart Mobility: Energy-Aware Deep Learning for
Driving Behavior Analysis and Prediction of Connected Vehicles,
ITS(22), No. 7, July 2021, pp. 4267-4280.
IEEE DOI
2107
Energy consumption, Trajectory, Predictive models,
Connected vehicles, Acceleration, Deep learning, Energy management,
deep learning
BibRef
Nagahama, A.[Akihito],
Saito, T.[Takahiro],
Wada, T.[Takahiro],
Sonoda, K.[Kohei],
Autonomous Driving Learning Preference of Collision Avoidance
Maneuvers,
ITS(22), No. 9, September 2021, pp. 5624-5634.
IEEE DOI
2109
Trajectory, Mathematical model, Autonomous vehicles,
Collision avoidance, Roads, Torque, Autonomous driving, authority transfer
BibRef
Navarro, J.[Jordan],
Allali, S.[Sarah],
Cabrignac, N.[Nicolas],
Cegarra, J.[Julien],
Impact of Pilot's Expertise on Selection, Use, Trust, and Acceptance
of Automation,
HMS(51), No. 5, October 2021, pp. 432-441.
IEEE DOI
2109
Automation, Task analysis, Monitoring, Tools, Human factors, Batteries,
Analysis of variance, Aircraft piloting, automation, expertise,
workload
BibRef
Cura, A.[Aslihan],
Küçük, H.[Haluk],
Ergen, E.[Erdem],
Öksüzoglu, I.B.[Ismail Burak],
Driver Profiling Using Long Short Term Memory (LSTM) and
Convolutional Neural Network (CNN) Methods,
ITS(22), No. 10, October 2021, pp. 6572-6582.
IEEE DOI
2110
Vehicles, Acceleration, Engines, Neural networks, Fuels,
Road transportation, Cameras, Driver profiling, CNN, LSTM
BibRef
Lyu, N.C.[Neng-Chao],
Wang, Y.G.[Yu-Gang],
Wu, C.Z.[Chao-Zhong],
Wu, H.R.[Hao-Ran],
Wen, J.Q.[Jia-Qiang],
Exploring longitudinal driving behaviour on a freeway deceleration
lane using field operational test data,
IET-ITS(15), No. 11, 2021, pp. 1401-1413.
DOI Link
2110
BibRef
Ramah, S.E.[Salah-Eddine],
Bouhoute, A.[Afaf],
Boubouh, K.[Karim],
Berrada, I.[Ismail],
One Step Further Towards Real-Time Driving Maneuver Recognition Using
Phone Sensors,
ITS(22), No. 10, October 2021, pp. 6599-6611.
IEEE DOI
2110
Sensors, Vehicles, Smart phones, Gyroscopes, Accelerometers,
Automobiles, Acceleration, Maneuvers recognition, normalization
BibRef
Lin, C.T.[Chin-Teng],
Chuang, C.H.[Chun-Hsiang],
Hung, Y.C.[Yu-Chia],
Fang, C.N.[Chieh-Ning],
Wu, D.[Dongrui],
Wang, Y.K.[Yu-Kai],
A Driving Performance Forecasting System Based on Brain Dynamic State
Analysis Using 4-D Convolutional Neural Networks,
Cyber(51), No. 10, October 2021, pp. 4959-4967.
IEEE DOI
2110
Electroencephalography, Task analysis, Vehicles, Deep learning,
Fatigue, Monitoring, Feature extraction,
response time (RT)
BibRef
Wang, X.Y.[Xiao-Yuan],
Guo, Y.Q.[Yong-Qing],
Bai, C.L.[Cheng-Lin],
Yuan, Q.[Quan],
Liu, S.L.[Shan-Liang],
Han, J.Y.[Jun-Yan],
Driver's Intention Identification With the Involvement of Emotional
Factors in Two-Lane Roads,
ITS(22), No. 11, November 2021, pp. 6866-6874.
IEEE DOI
2112
Vehicles, Support vector machines, Hidden Markov models, Roads,
Safety, Visualization, Emotion recognition, Driver's emotion,
support vector machine
BibRef
Xu, D.H.[Dong-Hao],
Ding, Z.Z.[Zhe-Zhang],
He, X.[Xu],
Zhao, H.J.[Hui-Jing],
Moze, M.[Mathieu],
Aioun, F.[François],
Guillemard, F.[Franck],
Learning From Naturalistic Driving Data for Human-Like Autonomous
Highway Driving,
ITS(22), No. 12, December 2021, pp. 7341-7354.
IEEE DOI
2112
Trajectory, Planning, Cost function, Autonomous vehicles, Robots,
Automobiles, Autonomous vehicles, motion planning
BibRef
Ben-Younes, H.[Hédi],
Zablocki, É.[Éloi],
Pérez, P.[Patrick],
Cord, M.[Matthieu],
Driving behavior explanation with multi-level fusion,
PR(123), 2022, pp. 108421.
Elsevier DOI
2112
Explainable self-driving, Multi-level fusion,
Cause classification, Natural language explanations, HDD, BDD-X
BibRef
Li, Y.[Yi],
Yu, B.[Bo],
Chen, Y.[Yuren],
Hu, Z.H.[Zhi-Hua],
A new theory of driver vision pressure energy field and its
application in driver behaviour decision-making model,
IET-ITS(16), No. 1, 2022, pp. 1-12.
DOI Link
2112
BibRef
Lu, M.Q.[Ming-Qi],
Hu, Y.C.[Yao-Cong],
Lu, X.B.[Xiao-Bo],
Pose-guided model for driving behavior recognition using keypoint
action learning,
SP:IC(100), 2022, pp. 116513.
Elsevier DOI
2112
Driving behavior, Pose estimation, Keypoint action, Keypoint Gating module
BibRef
Seet, M.[Manuel],
Harvy, J.[Jonathan],
Bose, R.[Rohit],
Dragomir, A.[Andrei],
Bezerianos, A.[Anastasios],
Thakor, N.[Nitish],
Differential Impact of Autonomous Vehicle Malfunctions on Human Trust,
ITS(23), No. 1, January 2022, pp. 548-557.
IEEE DOI
2201
Junctions, Automation, Electroencephalography, Autonomous vehicles,
Monitoring, Brain modeling, Human factors,
electroencephalography
BibRef
Straub, T.[Tobias],
Frey, M.[Michael],
Gauterin, F.[Frank],
Learning From the Fleet:
Map Attributes for Energetic Representation of Driving Profiles,
ITS(23), No. 1, January 2022, pp. 471-482.
IEEE DOI
2201
Computational modeling, Resistance, Predictive models, Wheels,
Mechanical power transmission, Roads, Vehicle dynamics,
feature extraction
BibRef
Chen, H.Y.W.[Huei-Yen Winnie],
Donmez, B.[Birsen],
A Naturalistic Driving Study of Feedback Timing and Financial
Incentives in Promoting Speed Limit Compliance,
HMS(52), No. 1, February 2022, pp. 64-73.
IEEE DOI
2201
Real-time systems, Vehicles, Urban areas, Data collection,
Visualization, Statistics, Sociology, Driver safety, speeding
BibRef
Deng, Z.J.[Ze-Jian],
Chu, D.F.[Duan-Feng],
Wu, C.Z.[Chao-Zhong],
Liu, S.D.[Shi-Dong],
Sun, C.[Chen],
Liu, T.[Teng],
Cao, D.[Dongpu],
A Probabilistic Model for Driving-Style-Recognition-Enabled Driver
Steering Behaviors,
SMCS(52), No. 3, March 2022, pp. 1838-1851.
IEEE DOI
2202
Vehicles, Acceleration, Stochastic processes,
Support vector machines, Predictive models, Probabilistic logic,
stochastic programming
BibRef
Rasch, A.[Alexander],
Dozza, M.[Marco],
Modeling Drivers' Strategy When Overtaking Cyclists in the Presence
of Oncoming Traffic,
ITS(23), No. 3, March 2022, pp. 2180-2189.
IEEE DOI
2203
Vehicles, Acceleration, Safety, Predictive models, Roads, Accidents,
Brakes, Advanced driver assistance systems (ADAS), overtaking
BibRef
Chandra, R.[Rohan],
Bera, A.[Aniket],
Manocha, D.[Dinesh],
Using Graph-Theoretic Machine Learning to Predict Human Driver
Behavior,
ITS(23), No. 3, March 2022, pp. 2572-2585.
IEEE DOI
2203
Vehicles, Navigation, Trajectory, Machine learning, Planning,
Autonomous vehicles, Task analysis, Advanced driver assistance,
network theory
BibRef
Yang, L.[Liu],
Li, M.[Maoying],
Shen, C.Y.[Chen-Yang],
Hu, Q.H.[Qing-Hua],
Wen, J.[Jia],
Xu, S.[Shujie],
Discriminative Transfer Learning for Driving Pattern Recognition in
Unlabeled Scenes,
Cyber(52), No. 3, March 2022, pp. 1429-1442.
IEEE DOI
2203
Pattern recognition, Automotive engineering, Cybernetics,
Learning systems, Data structures, Companies,
transfer learning
BibRef
Imberg, H.[Henrik],
Lisovskaja, V.[Vera],
Selpi,
Nerman, O.[Olle],
Optimization of Two-Phase Sampling Designs With Application to
Naturalistic Driving Studies,
ITS(23), No. 4, April 2022, pp. 3575-3588.
IEEE DOI
2204
Databases, Automobiles, Safety, Vehicles, Brakes, Annotations,
Maximum likelihood estimation, Case-control studies,
unequal probability sampling
BibRef
Zhang, H.[Hua],
Zhang, Z.Y.[Zhi-Yuan],
Liang, J.[Jun],
Dynamic driving intention recognition of vehicles with different
driving styles of surrounding vehicles,
IET-ITS(16), No. 5, , pp. 571-585.
DOI Link
2204
BibRef
Wang, Z.[Zheng],
Zheng, R.[Rencheng],
Nacpil, E.J.C.[Edric John Cruz],
Nakano, K.[Kimihiko],
Modeling and analysis of driver behaviour under shared control
through weighted visual and haptic guidance,
IET-ITS(16), No. 5, 2022, pp. 648-660.
DOI Link
2204
BibRef
Liu, C.[Chao],
Wang, Z.[Zheng],
Nacpil, E.J.C.[Edric John Cruz],
Hou, W.B.[Wen-Bin],
Zheng, R.C.[Ren-Cheng],
Analysis of visual risk perception model for braking control
behaviour of human drivers: A literature review,
IET-ITS(16), No. 6, 2022, pp. 711-724.
DOI Link
2205
BibRef
Ayoub, J.[Jackie],
Avetisyan, L.[Lilit],
Makki, M.[Mustapha],
Zhou, F.[Feng],
An Investigation of Drivers' Dynamic Situational Trust in
Conditionally Automated Driving,
HMS(52), No. 3, June 2022, pp. 501-511.
IEEE DOI
2205
Videos, Particle measurements, Atmospheric measurements,
System performance, Vehicle dynamics, Vehicles, Uncertainty,
undertrust
BibRef
Xie, S.S.[Shan-Shan],
Chen, S.T.[Shi-Tao],
Zheng, J.Y.[Jing-Yue],
Tomizuka, M.[Masayoshi],
Zheng, N.N.[Nan-Ning],
Wang, J.Q.[Jian-Qiang],
From Human Driving to Automated Driving:
What Do We Know About Drivers?,
ITS(23), No. 7, July 2022, pp. 6189-6205.
IEEE DOI
2207
Cognition, Vehicles, Task analysis, Computer architecture,
Biological neural networks, Automation, Adaptation models,
human factors
BibRef
Tian, Y.T.[Yan-Tao],
Cao, X.H.[Xuan-Hao],
Huang, K.[Kai],
Fei, C.[Cong],
Zheng, Z.[Zhu],
Ji, X.[Xuewu],
Learning to Drive Like Human Beings: A Method Based on Deep
Reinforcement Learning,
ITS(23), No. 7, July 2022, pp. 6357-6367.
IEEE DOI
2207
Autonomous vehicles, Reinforcement learning, Task analysis, Wheels,
Roads, Mathematical model, Markov processes, Autonomous driving,
reinforcement learning
BibRef
Azadani, M.N.[Mozhgan Nasr],
Boukerche, A.[Azzedine],
Driving Behavior Analysis Guidelines for Intelligent Transportation
Systems,
ITS(23), No. 7, July 2022, pp. 6027-6045.
IEEE DOI
2207
Vehicles, Sensors, Smart phones, Global Positioning System,
Sensor fusion, Laser radar, Automobiles, Driving style,
survey
BibRef
Zhang, Z.Y.[Zi-Yu],
Wang, C.Y.[Chun-Yan],
Zhao, W.Z.[Wan-Zhong],
Xu, C.[Can],
Chen, G.P.[Guo-Ping],
Driving Authority Allocation Strategy Based on Driving Authority
Real-Time Allocation Domain,
ITS(23), No. 7, July 2022, pp. 8528-8543.
IEEE DOI
2207
Resource management, Vehicles, Real-time systems, Switches, Muscles,
Vehicle dynamics, Man-machine systems, dynamic optimization
BibRef
Li, L.[Li],
Zhao, C.[Can],
Wang, X.[Xiao],
Li, Z.H.[Zhi-Heng],
Chen, L.[Long],
Lv, Y.S.[Yi-Sheng],
Zheng, N.N.[Nan-Ning],
Wang, F.Y.[Fei-Yue],
Three Principles to Determine the Right-of-Way for AVs: Safe
Interaction With Humans,
ITS(23), No. 7, July 2022, pp. 7759-7774.
IEEE DOI
2207
Vehicles, Safety, Accidents, Sensors, Prototypes, Autonomous vehicles,
Automation, Autonomous vehicles, driving safety, social interaction
BibRef
Domeyer, J.E.[Joshua E.],
Lee, J.D.[John D.],
Toyoda, H.[Heishiro],
Mehler, B.[Bruce],
Reimer, B.[Bryan],
Driver-Pedestrian Perceptual Models Demonstrate Coupling:
Implications for Vehicle Automation,
HMS(52), No. 4, August 2022, pp. 557-566.
IEEE DOI
2208
Vehicles, Roads, Automation, Legged locomotion, Visualization, Safety,
Predictive models, Dynamic modeling, joint activity, perception,
vehicle automation
BibRef
He, B.T.[Bao-Tian],
Li, Y.B.[Yi-Bing],
Multi-future Transformer: Learning diverse interaction modes for
behaviour prediction in autonomous driving,
IET-ITS(16), No. 9, 2022, pp. 1249-1267.
DOI Link
2208
BibRef
Palaniappan, R.[Ramaswamy],
Mouli, S.[Surej],
Bowman, H.[Howard],
McLoughlin, I.[Ian],
Investigating the Cognitive Response of Brake Lights in Initiating
Braking Action Using EEG,
ITS(23), No. 8, August 2022, pp. 13878-13883.
IEEE DOI
2208
Brakes, Electroencephalography, Vehicles, Hardware, Brain modeling,
Light emitting diodes, Shape, Brake light reaction time,
road safety
BibRef
Huang, Z.Y.[Zhi-Yu],
Wu, J.D.[Jing-Da],
Lv, C.[Chen],
Driving Behavior Modeling Using Naturalistic Human Driving Data With
Inverse Reinforcement Learning,
ITS(23), No. 8, August 2022, pp. 10239-10251.
IEEE DOI
2208
Trajectory, Vehicles, Entropy, Decision making, Predictive models,
Hidden Markov models, Task analysis, Driving behavior modeling,
interaction awareness
BibRef
Huang, Z.Y.[Zhi-Yu],
Liu, H.C.[Hao-Chen],
Wu, J.D.[Jing-Da],
Lv, C.[Chen],
Conditional Predictive Behavior Planning With Inverse Reinforcement
Learning for Human-Like Autonomous Driving,
ITS(24), No. 7, July 2023, pp. 7244-7258.
IEEE DOI
2307
Behavioral sciences, Trajectory, Predictive models, Planning,
Proposals, Autonomous vehicles, Reinforcement learning,
inverse reinforcement learning
BibRef
Zhang, H.L.[Hai-Lun],
Fu, R.[Rui],
An Ensemble Learning-Online Semi-Supervised Approach for Vehicle
Behavior Recognition,
ITS(23), No. 8, August 2022, pp. 10610-10626.
IEEE DOI
2208
Hidden Markov models, Vehicles, Turning, Vehicle dynamics, Sensors,
Deep learning, Data models, Advanced driver assistance system,
vehicle behavior recognition
BibRef
Yang, S.[Sen],
Wang, W.[Wenshuo],
Xi, J.Q.[Jun-Qiang],
Leveraging Human Driving Preferences to Predict Vehicle Speed,
ITS(23), No. 8, August 2022, pp. 11137-11147.
IEEE DOI
2208
Vehicles, Hidden Markov models, Roads, Prediction algorithms,
Vehicle dynamics, Trajectory, Random variables,
driving preferences
BibRef
Fu, Y.C.[Yu-Chuan],
Li, C.L.[Chang-Le],
Yu, F.R.[F. Richard],
Luan, T.H.[Tom H.],
Zhang, Y.[Yao],
Hybrid Autonomous Driving Guidance Strategy Combining Deep
Reinforcement Learning and Expert System,
ITS(23), No. 8, August 2022, pp. 11273-11286.
IEEE DOI
2208
Autonomous vehicles, Decision making, Blockchains, Planning,
Adaptation models, Roads, Expert systems, Autonomous driving,
vehicular blockchain
BibRef
Fu, Y.C.[Yu-Chuan],
Li, C.L.[Chang-Le],
Yu, F.R.[F. Richard],
Luan, T.H.[Tom H.],
Zhang, Y.[Yao],
A Selective Federated Reinforcement Learning Strategy for Autonomous
Driving,
ITS(24), No. 2, February 2023, pp. 1655-1668.
IEEE DOI
2302
Autonomous vehicles, Adaptation models, Reinforcement learning,
Computational modeling, Data models, Training, Task analysis,
knowledge aggregation
BibRef
Tao, J.[Jili],
Zhang, R.[Ridong],
Intelligent Feature Selection Using GA and Neural Network
Optimization for Real-Time Driving Pattern Recognition,
ITS(23), No. 8, August 2022, pp. 12665-12674.
IEEE DOI
2208
Feature extraction, Neural networks,
Genetic algorithms, Support vector machines, Optimization,
neural network classifier
BibRef
Bhattacharyya, R.[Raunak],
Jung, S.[Soyeon],
Kruse, L.A.[Liam A.],
Senanayake, R.[Ransalu],
Kochenderfer, M.J.[Mykel J.],
A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling
Through Particle Filtering,
ITS(23), No. 8, August 2022, pp. 13055-13068.
IEEE DOI
2208
Vehicles, Data models, Merging, Mathematical models,
Autonomous vehicles, Trajectory, Task analysis,
particle filters
BibRef
Abdelrahman, A.E.[Abdalla Ebrahim],
Hassanein, H.S.[Hossam S.],
Abu-Ali, N.[Najah],
Robust Data-Driven Framework for Driver Behavior Profiling Using
Supervised Machine Learning,
ITS(23), No. 4, April 2022, pp. 3336-3350.
IEEE DOI
2204
BibRef
Abdelrahman, A.E.[Abdalla Ebrahim],
Hassanein, H.S.[Hossam S.],
Abu-Ali, N.[Najah],
A Robust Environment-Aware Driver Profiling Framework Using Ensemble
Supervised Learning,
ITS(23), No. 9, September 2022, pp. 14456-14469.
IEEE DOI
2209
Vehicles, Smart phones, Predictive models,
Machine learning algorithms, Insurance, Accidents, Sensors,
vehicle-to-cloud (V2C) applications.
Predictive models, Hidden Markov models, Roads,
Data models, Context modeling, Driver profiling, telematics
BibRef
Zhu, Z.[Zeyu],
Zhao, H.J.[Hui-Jing],
A Survey of Deep RL and IL for Autonomous Driving Policy Learning,
ITS(23), No. 9, September 2022, pp. 14043-14065.
IEEE DOI
2209
Task analysis, Reinforcement learning, Planning, Taxonomy, Safety,
Autonomous vehicles, Uncertainty, Deep reinforcement learning,
autonomous driving policy
BibRef
Zekany, S.A.[Stephen A.],
Larsen, T.F.[Thomas F.],
Dreslinski, R.G.[Ronald G.],
Wenisch, T.F.[Thomas F.],
Finding and Indexing Vehicle Maneuvers From Dashboard Camera Video,
ITS(23), No. 9, September 2022, pp. 16098-16109.
IEEE DOI
2209
Cameras, Deep learning, Trajectory, Global Positioning System,
Vehicle dynamics, Visual odometry, Roads, Intelligent systems,
software algorithms
BibRef
Lu, C.[Chao],
Lv, C.[Chen],
Gong, J.W.[Jian-Wei],
Wang, W.[Wenshuo],
Cao, D.[Dongpu],
Wang, F.Y.[Fei-Yue],
Instance-Level Knowledge Transfer for Data-Driven Driver Model
Adaptation With Homogeneous Domains,
ITS(23), No. 10, October 2022, pp. 17015-17026.
IEEE DOI
2210
Vehicles, Adaptation models, Data models, Hidden Markov models,
Knowledge transfer, Transfer learning, Training, Driver behaviour,
importance weight
BibRef
Azadani, M.N.[Mozhgan Nasr],
Boukerche, A.[Azzedine],
Siamese Temporal Convolutional Networks for Driver Identification
Using Driver Steering Behavior Analysis,
ITS(23), No. 10, October 2022, pp. 18076-18087.
IEEE DOI
2210
Vehicles, Wheels, Task analysis, Feature extraction, Brakes,
Automobiles, Deep learning, Driver verification,
driving behavior
BibRef
Li, X.[Xueyun],
Wang, Y.P.[Yi-Ping],
Su, C.[Chuqi],
Gong, X.[Xinle],
Huang, J.[Jin],
Yang, D.[Dengke],
Adaptive Authority Allocation Approach for Shared Steering Control
System,
ITS(23), No. 10, October 2022, pp. 19428-19439.
IEEE DOI
2210
Vehicles, Resource management, Adaptation models, Vehicle dynamics,
Control systems, Reliability, Man-machine systems, driver behavior
BibRef
Szoke, L.[Laszlo],
Aradi, S.[Szilárd],
Bécsi, T.[Tamás],
Gáspár, P.[Péter],
Skills to Drive: Successor Features for Autonomous Highway Pilot,
ITS(23), No. 10, October 2022, pp. 18707-18718.
IEEE DOI
2210
Task analysis, Road transportation, Reinforcement learning,
Autonomous vehicles, Training, Technological innovation, successor features
BibRef
Li, Q.[Qiang],
Liu, C.S.[Chun-Sheng],
Chang, F.[Faliang],
Li, S.[Shuang],
Liu, H.[Hui],
Liu, Z.[Zehao],
Adaptive Short-Temporal Induced Aware Fusion Network for Predicting
Attention Regions Like a Driver,
ITS(23), No. 10, October 2022, pp. 18695-18706.
IEEE DOI
2210
Feature extraction, Visualization, Vehicles, Task analysis,
Estimation, Solid modeling, Semantics, Driver attention prediction,
motion feature extraction
BibRef
Wang, J.H.[Jun-Hua],
Xu, W.X.[Wen-Xiang],
Fu, T.[Ting],
Jiang, R.[Rui],
Recognition of Trip-Based Aggressive Driving: A System Integrated
With Gaussian Mixture Model Structured of Factor-Analysis, and
Hierarchical Clustering,
ITS(23), No. 11, November 2022, pp. 20442-20451.
IEEE DOI
2212
Behavioral sciences, Vehicles, Data models, Clustering algorithms,
Computational modeling, Machine learning algorithms,
Gaussian mixture model
BibRef
Seo, H.W.[Hyun-Woo],
Shin, J.[Jongkyung],
Kim, K.H.[Ki-Hun],
Lim, C.[Chiehyeon],
Bae, J.[Jungcheol],
Driving Risk Assessment Using Non-Negative Matrix Factorization With
Driving Behavior Records,
ITS(23), No. 11, November 2022, pp. 20398-20412.
IEEE DOI
2212
Accidents, Risk management, Behavioral sciences, Vehicles,
Transportation, Computer crashes, Injuries,
non-negative matrix factorization
BibRef
Toghi, B.[Behrad],
Valiente, R.[Rodolfo],
Sadigh, D.[Dorsa],
Pedarsani, R.[Ramtin],
Fallah, Y.P.[Yaser P.],
Social Coordination and Altruism in Autonomous Driving,
ITS(23), No. 12, December 2022, pp. 24791-24804.
IEEE DOI
2212
Behavioral sciences, Vehicles, Safety, Games, Training, Navigation,
Decision making, Cooperative driving, social navigation,
multi-agent reinforcement learning
BibRef
Eraqi, H.M.[Hesham M.],
Moustafa, M.N.[Mohamed N.],
Honer, J.[Jens],
Dynamic Conditional Imitation Learning for Autonomous Driving,
ITS(23), No. 12, December 2022, pp. 22988-23001.
IEEE DOI
2212
Roads, Laser radar, Autonomous vehicles, Cameras, Planning, Navigation,
Meteorology, Autonomous driving, occupancy grid mapping,
road blockages avoidance
BibRef
Roitberg, A.[Alina],
Peng, K.Y.[Kun-Yu],
Schneider, D.[David],
Yang, K.L.[Kai-Lun],
Koulakis, M.[Marios],
Martinez, M.[Manuel],
Stiefelhagen, R.[Rainer],
Is My Driver Observation Model Overconfident? Input-Guided
Calibration Networks for Reliable and Interpretable Confidence Estimates,
ITS(23), No. 12, December 2022, pp. 25271-25286.
IEEE DOI
2212
Vehicles, Activity recognition, Uncertainty, Reliability,
Neural networks, Predictive models, Calibration, uncertainty in deep learning
BibRef
Wang, R.[Rui],
Xu, J.F.[Jin-Feng],
Liu, J.[Jia],
Wu, D.[Di],
Hao, Y.X.[Yi-Xue],
Li, X.Z.[Xian-Zhi],
Chen, M.[Min],
TIF: Trajectory and Information Flow Coupling Mechanism for Behavior
Analysis in Autonomous Driving,
ITS(23), No. 12, December 2022, pp. 25216-25225.
IEEE DOI
2212
Behavioral sciences, Sensors, Fabrics, Couplings, Trajectory,
Intelligent sensors, Motion segmentation, Behavior analysis, motion pattern
BibRef
Wu, C.[Chuna],
Cao, J.[Jing],
Du, Y.C.[Yu-Chuan],
Impacts of advanced driver assistance systems on commercial truck
driver behaviour performance using naturalistic data,
IET-ITS(17), No. 1, 2023, pp. 119-128.
DOI Link
2301
BibRef
Li, D.D.[Dan-Dan],
Wang, Y.[Yibo],
Xu, W.[Wei],
A Deep Multichannel Network Model for Driving Behavior Risk
Classification,
ITS(24), No. 1, January 2023, pp. 1204-1219.
IEEE DOI
2301
Behavioral sciences, Vehicles, Feature extraction, Accidents,
Hidden Markov models, Deep learning, Data models, Driving risk,
driving behavior
BibRef
Yang, D.[Di],
Ozbay, K.[Kaan],
Gao, J.Q.[Jing-Qin],
Zuo, F.[Fan],
A Functional Approach for Analyzing Time-Dependent Driver Response
Behavior to Real-World Connected Vehicle Warnings,
ITS(24), No. 3, March 2023, pp. 3438-3447.
IEEE DOI
2303
Vehicles, Behavioral sciences, Safety, Accidents, Urban areas,
Data models, Connected vehicles, Connected vehicles, functional data analysis
BibRef
Liu, Y.Q.[Ya-Qi],
Wang, X.Y.[Xiao-Yuan],
The Analysis of Driver's Behavioral Tendency Under Different
Emotional States Based on a Bayesian Network,
AffCom(14), No. 1, January 2023, pp. 165-177.
IEEE DOI
2303
Vehicles, Physiology, Bayes methods, Videos, Roads, Task analysis,
Safety, Vehicle operation, affective behavior,
Bayesian network
BibRef
Bhattacharyya, R.[Raunak],
Wulfe, B.[Blake],
Phillips, D.J.[Derek J.],
Kuefler, A.[Alex],
Morton, J.[Jeremy],
Senanayake, R.[Ransalu],
Kochenderfer, M.J.[Mykel J.],
Modeling Human Driving Behavior Through Generative Adversarial
Imitation Learning,
ITS(24), No. 3, March 2023, pp. 2874-2887.
IEEE DOI
2303
Behavioral sciences, Cost function, Trajectory, Costs, Vehicles,
Mathematical models, Computational modeling, Autonomous driving,
reinforcement learning
BibRef
Hu, H.Y.[Hong-Yu],
Liu, J.R.[Jia-Rui],
Chen, G.Y.[Guo-Ying],
Zhao, Y.T.[Yu-Ting],
Gao, Z.H.[Zhen-Hai],
Zheng, R.C.[Ren-Cheng],
Driver Identification Using Deep Generative Model With Limited Data,
ITS(24), No. 5, May 2023, pp. 5159-5171.
IEEE DOI
2305
Vehicles, Data models, Feature extraction,
Convolutional neural networks, Computational modeling, deep learning
BibRef
Hu, H.Y.[Hong-Yu],
Liu, J.R.[Jia-Rui],
Chen, G.Y.[Guo-Ying],
Zhao, Y.T.[Yu-Ting],
Men, Y.Z.[Yu-Zhuo],
Wang, P.[Pin],
Driver identification through vehicular CAN bus data:
An ensemble deep learning approach,
IET-ITS(17), No. 5, 2023, pp. 867-877.
DOI Link
2305
BibRef
Jeon, H.[Hyeongseok],
Kim, S.[Sanmin],
Lee, K.[Kibeom],
Kang, D.[Daejun],
Choi, J.[Junwon],
Kum, D.[Dongsuk],
Are Reactions to Ego Vehicles Predictable Without Data?: A
Semi-Supervised Approach,
ITS(24), No. 6, June 2023, pp. 6477-6490.
IEEE DOI
2306
Trajectory, Predictive models, Task analysis,
Unsupervised learning, History, Data models, Training,
data deficit problem
BibRef
Srinivasan, A.R.[Aravinda Ramakrishnan],
Lin, Y.S.[Yi-Shin],
Antonello, M.[Morris],
Knittel, A.[Anthony],
Hasan, M.[Mohamed],
Hawasly, M.[Majd],
Redford, J.[John],
Ramamoorthy, S.[Subramanian],
Leonetti, M.[Matteo],
Billington, J.[Jac],
Romano, R.[Richard],
Markkula, G.[Gustav],
Beyond RMSE: Do Machine-Learned Models of Road User Interaction
Produce Human-Like Behavior?,
ITS(24), No. 7, July 2023, pp. 7166-7177.
IEEE DOI
2307
Behavioral sciences, Predictive models, Trajectory, Measurement,
Roads, Analytical models, Merging, Machine-learned models, highway driving
BibRef
Na, X.X.[Xiao-Xiang],
Cole, D.J.[David J.],
Experimental Evaluation of a Game-Theoretic Human Driver Steering
Control Model,
Cyber(53), No. 8, August 2023, pp. 4791-4804.
IEEE DOI
2307
Vehicles, Mathematical models, Games, Automation,
Robot sensing systems, Predictive models, Axles, game theory
BibRef
Yi, Y.T.[Yang-Tian],
Lu, C.[Chao],
Wang, B.Y.[Bo-Yang],
Cheng, L.[Long],
Li, Z.[Zirui],
Gong, J.W.[Jian-Wei],
Fusion of Gaze and Scene Information for Driving Behaviour
Recognition: A Graph-Neural-Network- Based Framework,
ITS(24), No. 8, August 2023, pp. 8109-8120.
IEEE DOI
2308
Vehicles, Feature extraction, Cameras, Optical flow,
Graph neural networks, Trajectory, Reliability, Driving behaviours,
data fusion
BibRef
Guo, C.H.[Cheng-Hao],
Liu, H.Z.[Hai-Zhuang],
Chen, J.S.[Jian-Sheng],
Ma, H.M.[Hui-Min],
Temporal Information Fusion Network for Driving Behavior Prediction,
ITS(24), No. 9, September 2023, pp. 9415-9424.
IEEE DOI
2310
BibRef
Feng, Y.C.[Yu-Chao],
Hua, W.[Wei],
Sun, Y.X.[Yu-Xiang],
NLE-DM: Natural-Language Explanations for Decision Making of
Autonomous Driving Based on Semantic Scene Understanding,
ITS(24), No. 9, September 2023, pp. 9780-9791.
IEEE DOI
2310
BibRef
Hu, X.M.[Xue-Min],
Liu, Y.F.[Yan-Fang],
Tang, B.[Bo],
Yan, J.C.[Jun-Chi],
Chen, L.[Long],
Learning Dynamic Graph for Overtaking Strategy in Autonomous Driving,
ITS(24), No. 11, November 2023, pp. 11921-11933.
IEEE DOI
2311
BibRef
Jin, L.[Long],
Serialized Recommendation Prediction for Steering Point Behavior of
Intelligent Transportation Vehicles Based on Deep Learning,
ITS(24), No. 11, November 2023, pp. 13350-13358.
IEEE DOI
2311
BibRef
Yavas, M.U.[Muharrem Ugur],
Kumbasar, T.[Tufan],
Ure, N.K.[Nazim Kemal],
A Real-World Reinforcement Learning Framework for Safe and Human-Like
Tactical Decision-Making,
ITS(24), No. 11, November 2023, pp. 11773-11784.
IEEE DOI
2311
BibRef
Wen, X.[Xiao],
Jian, S.[Sisi],
He, D.[Dengbo],
Modeling the Effects of Autonomous Vehicles on Human Driver
Car-Following Behaviors Using Inverse Reinforcement Learning,
ITS(24), No. 12, December 2023, pp. 13903-13915.
IEEE DOI
2312
BibRef
Chen, X.B.[Xiao-Bo],
Gao, Y.X.[Yu-Xiang],
Yu, H.[Haoze],
Wang, H.[Hai],
Cai, Y.F.[Ying-Feng],
Driving Style Feature Extraction and Recognition Based on
Hyperdimensional Computing and Semi-Supervised Twin Projection Vector
Machine,
ITS(24), No. 12, December 2023, pp. 13976-13988.
IEEE DOI
2312
BibRef
Yuan, Y.X.[Yu-Xia],
Wang, X.W.[Xin-Wei],
Calvert, S.[Simeon],
Happee, R.[Riender],
Wang, M.[Meng],
A risk-based driver behaviour model,
IET-ITS(18), No. 1, 2024, pp. 88-100.
DOI Link
2401
driver behaviour model, human factors, path planning,
risk perception, vehicle dynamics and control
BibRef
Zhu, Y.F.[Yuan-Fang],
Jiang, M.[Meilan],
Yamamoto, T.[Toshiyuki],
Ding, N.[Naikan],
Shinkai, H.[Hiroko],
Aoki, H.[Hirofumi],
Shimazaki, K.[Kan],
A Framework for Combining Lateral and Longitudinal Acceleration to
Assess Driving Styles Using Unsupervised Approach,
ITS(25), No. 1, January 2024, pp. 638-656.
IEEE DOI
2402
Vehicles, Behavioral sciences, Machine learning algorithms, Fuels,
Intelligent transportation systems, Accidents, Road safety,
unsupervised approach
BibRef
Wang, H.J.[Huan-Jie],
Liu, H.B.[Hai-Bin],
Wang, W.S.[Wen-Shuo],
Sun, L.J.[Li-Jun],
On Trustworthy Decision-Making Process of Human Drivers From the View
of Perceptual Uncertainty Reduction,
ITS(25), No. 2, February 2024, pp. 1625-1636.
IEEE DOI
2402
Uncertainty, Vehicles, Predictive models, Data models,
Decision making, Merging, Task analysis, interaction
BibRef
Jokhio, S.[Sarang],
Olleja, P.[Pierluigi],
Bärgman, J.[Jonas],
Yan, F.[Fei],
Baumann, M.[Martin],
Exploring turn signal usage patterns in lane changes: A Bayesian
hierarchical modelling analysis of realistic driving data,
IET-ITS(18), No. 2, 2024, pp. 393-408.
DOI Link
2402
Turn signal usage, Lane changing, Realistic driving data,
Autonomous vehicles, Bayesian hierarchical modelling
BibRef
Shi, S.S.[Shao-Shuai],
Jiang, L.[Li],
Dai, D.X.[Deng-Xin],
Schiele, B.[Bernt],
MTR++: Multi-Agent Motion Prediction With Symmetric Scene Modeling
and Guided Intention Querying,
PAMI(46), No. 5, May 2024, pp. 3955-3971.
IEEE DOI
2404
Trajectory, Transformers, Behavioral sciences, Encoding,
Task analysis, Context modeling, Predictive models, autonomous driving
BibRef
Li, Z.N.[Zhao-Ning],
Jiang, Q.[Qiaoli],
Wu, Z.M.[Zheng-Ming],
Liu, A.[Anqi],
Wu, H.Y.[Hai-Yan],
Huang, M.[Miner],
Huang, K.[Kai],
Ku, Y.X.[Yi-Xuan],
Towards Human-Compatible Autonomous Car: A Study of Non-Verbal Turing
Test in Automated Driving With Affective Transition Modelling,
AffCom(15), No. 2, April 2024, pp. 478-492.
IEEE DOI
2406
Vehicles, Artificial intelligence, Automobiles, Psychology,
Computational modeling, Roads, Signal detection,
signal detection theory (SDT)
BibRef
Chen, C.[Chen],
Lan, Z.Q.[Zhi-Qian],
Zhan, G.J.[Guo-Jian],
Lyu, Y.[Yao],
Nie, B.B.[Bing-Bing],
Li, S.E.[Shengbo Eben],
Quantifying the Individual Differences of Drivers' Risk Perception
via Potential Damage Risk Model,
ITS(25), No. 7, July 2024, pp. 8093-8104.
IEEE DOI
2407
Vehicles, Autonomous vehicles, Safety, Mathematical models, Data models,
Roads, Attenuation, Automated vehicle, driving safety,
individual characteristic
BibRef
Zhang, W.Q.[Wen-Qi],
Qin, Y.J.[Yan-Jun],
Tao, X.M.[Xiao-Ming],
MMPHGCN: A Hypergraph Convolutional Network for Detection of Driver
Intention on Multimodal Physiological Signals,
SPLetters(31), 2024, pp. 1730-1734.
IEEE DOI
2407
Physiology, Electroencephalography, Feature extraction,
Convolution, Brain modeling, Electromyography, Training,
hypergraph convolution
BibRef
Li, Z.[Zirui],
Gong, J.W.[Jian-Wei],
Zhang, Z.[Zheyu],
Lu, C.[Chao],
Knoop, V.L.[Victor L.],
Wang, M.[Meng],
Interactive Behavior Modeling for Vulnerable Road Users With
Risk-Taking Styles in Urban Scenarios: A Heterogeneous Graph Learning
Approach,
ITS(25), No. 8, August 2024, pp. 8538-8555.
IEEE DOI
2408
Predictive models, Trajectory, Long short term memory, Roads,
Planning, Safety, Pedestrians, Vulnerable road users,
trajectory prediction
BibRef
Liu, Y.H.[Ya-Hui],
Li, J.Y.[Jing-Yuan],
Sun, Y.[Yingbo],
Ji, X.[Xuewu],
Lv, C.[Chen],
Personalized Trajectory-based Risk Prediction on Curved Roads with
Consideration of Driver Turning Behavior and Workload,
HMS(54), No. 4, August 2024, pp. 406-415.
IEEE DOI
2408
Vehicles, Turning, Trajectory, Roads, Indexes, Vehicle dynamics,
Calibration, Driver workload, driving behavior, human factors
BibRef
Queiroz, R.[Rodrigo],
Sharma, D.[Divit],
Caldas, R.[Ricardo],
Czarnecki, K.[Krzysztof],
García, S.[Sergio],
Berger, T.[Thorsten],
Pelliccione, P.[Patrizio],
A Driver-Vehicle Model for ADS Scenario-Based Testing,
ITS(25), No. 8, August 2024, pp. 8641-8654.
IEEE DOI
2408
Testing, Roads, Trajectory, Vehicles, Vehicle dynamics, Scalability, DSL,
Intelligent vehicles, autonomous vehicles, autonomous driving,
road traffic
BibRef
Zhang, C.Y.[Cheng-Yuan],
Sun, L.J.[Li-Jun],
Bayesian Calibration of the Intelligent Driver Model,
ITS(25), No. 8, August 2024, pp. 9308-9320.
IEEE DOI
2408
Calibration, Bayes methods, Behavioral sciences, Data models,
Maximum likelihood estimation, Vehicles, Uncertainty,
Gaussian processes
BibRef
Zhao, X.C.[Xiao-Cong],
Sun, J.[Jian],
Wang, M.[Meng],
Measuring Sociality in Driving Interaction,
ITS(25), No. 8, August 2024, pp. 9224-9237.
IEEE DOI
2408
Vehicles, Roads, Automobiles, Navigation, Games, Analytical models,
Task analysis, Driving interaction, social preference,
unprotected left turn
BibRef
Fang, J.W.[Jian-Wu],
Wang, F.[Fan],
Xue, J.R.[Jian-Ru],
Chua, T.S.[Tat-Seng],
Behavioral Intention Prediction in Driving Scenes: A Survey,
ITS(25), No. 8, August 2024, pp. 8334-8355.
IEEE DOI
2408
Roads, Surveys, Pedestrians, Trajectory, Task analysis, Reviews,
Pipelines, Behavioral intention prediction, challenges,
benchmarks
BibRef
Lian, Y.F.[Yu-Feng],
Sun, Z.B.[Zhong-Bo],
Liu, S.S.[Shuai-Shi],
Nie, Z.G.[Zhi-Gen],
A Full Transfer Learning LSTM-Based Fractional Order Optimization
Method of GM(r,2) for Inferring Driving Intention,
ITS(25), No. 9, September 2024, pp. 10741-10753.
IEEE DOI
2409
Hidden Markov models, Long short term memory, Safety, Data models,
Transfer learning, Optimization methods, Vehicle dynamics,
LSTM network
BibRef
Tang, C.[Chen],
Srishankar, N.[Nishan],
Martin, S.[Sujitha],
Tomizuka, M.[Masayoshi],
Grounded Relational Inference:
Domain Knowledge Driven Explainable Autonomous Driving,
ITS(25), No. 9, September 2024, pp. 10617-10635.
IEEE DOI
2409
Atmospheric modeling, Semantics, Autonomous vehicles, Trajectory,
Graph neural networks, Decoding, Aerospace electronics,
driving behavior modeling
BibRef
Zhao, C.Y.[Chen-Yang],
Chu, D.[Duanfeng],
Deng, Z.[Zejian],
Lu, L.P.[Li-Ping],
Human-Like Decision Making for Autonomous Driving With Social Skills,
ITS(25), No. 9, September 2024, pp. 12269-12284.
IEEE DOI
2409
Autonomous vehicles, Vehicles, Games, Behavioral sciences,
Uncertainty, Trajectory, Game theory, Autonomous driving,
stackelberg game theory
BibRef
Zhang, C.P.[Chao-Peng],
Wang, W.S.[Wen-Shuo],
Chen, Z.K.[Zhao-Kun],
Zhang, J.[Jian],
Sun, L.J.[Li-Jun],
Xi, J.Q.[Jun-Qiang],
Shareable Driving Style Learning and Analysis With a Hierarchical
Latent Model,
ITS(25), No. 9, September 2024, pp. 11471-11484.
IEEE DOI
2409
Vehicles, Behavioral sciences, Analytical models, Semantics,
Data models, Random variables, Probabilistic logic, Driving style,
hierarchical latent model
BibRef
Xu, H.[HuiZhi],
Xing, Z.[ZhaoHao],
Ge, Y.[YongShuai],
Hao, D.[DongSheng],
Chang, M.[MengYing],
Driver behaviour recognition based on recursive all-pair field
transform time series model,
IET-ITS(18), No. 9, 2024, pp. 1559-1573.
DOI Link
2409
computer vision, convolutional neural nets,
intelligent transportation systems, management and control, traffic modelling
BibRef
Gao, H.[Hang],
Hu, M.T.[Meng-Ting],
Liu, Y.[Yi],
Learning Driver-Irrelevant Features for Generalizable Driver Behavior
Recognition,
ITS(25), No. 10, October 2024, pp. 14115-14127.
IEEE DOI Code:
WWW Link.
2410
Training, Vehicles, Feature extraction, Data augmentation,
Data models, Testing, Public security, Generalization, co-training
BibRef
Zhang, C.[Chenkai],
Deguchi, D.[Daisuke],
Chen, J.[Jialei],
Murase, H.[Hiroshi],
Toward Explainable End-to-End Driving Models via Simplified
Objectification Constraints,
ITS(25), No. 10, October 2024, pp. 14521-14534.
IEEE DOI
2410
Predictive models, Object detection, Task analysis, Prototypes,
Proposals, Feature extraction, Computational modeling,
convolutional neural networks
BibRef
Cheng, K.[Kun],
Sun, D.[Dongye],
Jian, J.[Junhang],
Qin, D.T.[Da-Tong],
Chen, C.[Chong],
Liao, G.L.[Guang-Liang],
Kan, Y.Z.[Ying-Zhe],
Lv, C.[Chang],
Deep Learning Approach for Driver Speed Intention Recognition Based
on Naturalistic Driving Data,
ITS(25), No. 10, October 2024, pp. 14546-14559.
IEEE DOI
2410
Vehicles, Hidden Markov models, Roads, Gears, Data collection, Brakes,
Feature extraction, Speed intention, driving environments, deep learning
BibRef
Guo, X.F.[Xiao-Feng],
Zhu, Q.[Qing],
Wang, Y.[Yaonan],
Mo, Y.[Yang],
MG-GCT: A Motion-Guided Graph Convolutional Transformer for Traffic
Gesture Recognition,
ITS(25), No. 10, October 2024, pp. 14031-14039.
IEEE DOI
2410
Streams, Transformers, Gesture recognition,
Convolutional neural networks, Convolution,
temporal-transformer encoder
BibRef
Wu, J.D.[Jing-Da],
Yang, H.H.[Hao-Han],
Yang, L.[Lie],
Huang, Y.[Yi],
He, X.K.[Xiang-Kun],
Lv, C.[Chen],
Human-Guided Deep Reinforcement Learning for Optimal Decision Making
of Autonomous Vehicles,
SMCS(54), No. 11, November 2024, pp. 6595-6609.
IEEE DOI Code:
WWW Link.
2410
Decision making, Training, Behavioral sciences, Autonomous vehicles,
Task analysis, Safety, Navigation, human guidance
BibRef
You, J.W.[Jun-Wei],
Chen, Y.[Ying],
Jiang, Z.Y.[Zhuo-Yu],
Liu, Z.C.[Zhang-Chi],
Huang, Z.[Zilin],
Ding, Y.F.[Yi-Feng],
Ran, B.[Bin],
Exploring Driving Behavior for Autonomous Vehicles Based on Gramian
Angular Field Vision Transformer,
ITS(25), No. 11, November 2024, pp. 17493-17504.
IEEE DOI
2411
Vehicles, Data models, Trajectory, Time series analysis,
Autonomous vehicles, Transformers, Accidents.
BibRef
Zhu, J.[Jieyu],
Lv, C.[Chen],
Ma, Y.L.[Yan-Li],
Yang, H.H.[Hao-Han],
Zhang, Y.P.[Ya-Ping],
Quantitative Estimation of Driver Cognitive Workload: A Dual-Stage
Learning Approach,
ITS(25), No. 12, December 2024, pp. 20227-20239.
IEEE DOI
2412
Vehicles, Physiology, Measurement, Heart rate, Estimation,
Contrastive learning, Hidden Markov models,
supervised contrastive learning
BibRef
Maktoubian, J.[Jamal],
Tran, S.N.[Son N.],
Shillabeer, A.[Anna],
Amin, M.B.[Muhammad Bilal],
Sambrooks, L.[Lawrence],
Khoshkangini, R.[Reza],
Empirical Evaluation of Machine Learning Models for Fuel Consumption,
Driver Identification, and Behavior Prediction,
ITS(25), No. 12, December 2024, pp. 19156-19175.
IEEE DOI
2412
Vehicles, Predictive models, Biological system modeling,
Data models, Analytical models, Computational modeling, typesetting
BibRef
Liu, Y.[Yiru],
Zhao, X.C.[Xiao-Cong],
Tian, Y.[Ye],
Sun, J.[Jian],
Sociality Probe: Game-Theoretic Inverse Reinforcement Learning for
Modeling and Quantifying Social Patterns in Driving Interaction,
ITS(25), No. 12, December 2024, pp. 20841-20853.
IEEE DOI
2412
Vehicles, Behavioral sciences, Analytical models,
Autonomous vehicles, Trajectory, Roads, Game theory, reward function
BibRef
Hayashi, H.[Hiroaki],
Oka, N.[Naoki],
Sugano, S.[Shigeki],
Kamezaki, M.[Mitsuhiro],
TUN-DAS: Time-Series Analysis and Unsupervised Learning Based Driving
Behavior Assessment System,
ITS(26), No. 1, January 2025, pp. 604-619.
IEEE DOI
2501
Vehicles, Accidents, Unsupervised learning, Time series analysis,
Monitoring, Vehicle dynamics, Feature extraction, Ethics, Protocols,
driver monitoring system
BibRef
Qi, H.S.[Hong-Sheng],
Microscopic Modeling of Abnormal Driving Behavior: A Two-Dimensional
Stochastic Formulation with Customizable Safety Levels,
ITS(26), No. 1, January 2025, pp. 1163-1176.
IEEE DOI
2501
Microscopy, Analytical models, Noise, Adaptation models, Force,
Vehicles, Vehicle dynamics, Safety, Chaos, Autonomous vehicles,
two-dimensional
BibRef
Chen, Y.[Yutao],
Zhang, H.L.[Hong-Liang],
Chen, H.[Haocong],
Huang, J.[Jie],
Wang, B.[Bin],
Xiong, Z.X.[Zi-Xiang],
Wang, Y.[Yuyi],
Yuan, X.[Xiwen],
Dynamic Control Authority Allocation in Indirect Shared Control for
Steering Assistance,
ITS(26), No. 3, March 2025, pp. 3458-3470.
IEEE DOI
2503
Resource management, Safety, Dynamic scheduling, Vehicle dynamics, Vehicles,
Process control, Wheels, Standards, Real-time systems, intelligent vehicle
BibRef
Adhikari, B.[Bikram],
Duric, Z.[Zoran],
Wijesekera, D.[Duminda],
Yu, B.[Bo],
Repeated Route Naturalistic Driver Behavior Analysis Using Motion and
Gaze Measurements,
ITS(26), No. 3, March 2025, pp. 3273-3283.
IEEE DOI
2503
Vehicles, Cameras, Driver behavior, Sensors, Decoding, Accuracy, Roads,
Data mining, Calibration, Kinematics,
inertial measurement unit (IMU)
BibRef
Shi, R.[Rui],
Li, T.X.[Tian-Xing],
Yamaguchi, Y.S.[Yasu-Shi],
Zhang, L.G.[Li-Guo],
Exploring Decision Shifts in Autonomous Driving With
Attribution-Guided Visualization,
ITS(26), No. 3, March 2025, pp. 4165-4177.
IEEE DOI
2503
Visualization, Autonomous vehicles, Decision making, Optimization,
Computational modeling, Generators, Data visualization,
generative adversarial networks
BibRef
Peng, Z.H.[Zheng-Hao],
Luo, W.J.[Wen-Jie],
Lu, Y.[Yiren],
Shen, T.Y.[Tian-Yi],
Gulino, C.[Cole],
Seff, A.[Ari],
Fu, J.[Justin],
Improving Agent Behaviors with Rl Fine-tuning for Autonomous Driving,
ECCV24(XXV: 165-181).
Springer DOI
2412
BibRef
Wang, X.F.[Xiao-Feng],
Zhu, Z.[Zheng],
Huang, G.[Guan],
Chen, X.Z.[Xin-Ze],
Zhu, J.[Jiagang],
Lu, J.W.[Ji-Wen],
Drivedreamer: Towards Real-world-drive World Models for Autonomous
Driving,
ECCV24(XLVIII: 55-72).
Springer DOI
2412
BibRef
Wasi, A.[Abdul],
Gangisetty, S.[Shankar],
Rai, S.N.[Shyam Nandan],
Jawahar, C.V.,
Early Anticipation of Driving Maneuvers,
ECCV24(LXX: 152-169).
Springer DOI
2412
BibRef
Zhou, Y.S.[Yun-Song],
Huang, L.Y.[Lin-Yan],
Bu, Q.W.[Qing-Wen],
Zeng, J.[Jia],
Li, T.Y.[Tian-Yu],
Qiu, H.[Hang],
Zhu, H.Z.[Hong-Zi],
Guo, M.[Minyi],
Qiao, Y.[Yu],
Li, H.Y.[Hong-Yang],
Embodied Understanding of Driving Scenarios,
ECCV24(LXII: 129-148).
Springer DOI
2412
BibRef
Chitta, K.[Kashyap],
Dauner, D.[Daniel],
Geiger, A.[Andreas],
SLEDGE: Synthesizing Driving Environments with Generative Models and
Rule-based Traffic,
ECCV24(IV: 57-74).
Springer DOI
2412
BibRef
Wang, R.[Ruoyu],
Cai, C.[Chen],
Wang, W.Q.[Wen-Qian],
Gao, J.J.[Jian-Jun],
Lin, D.[Dan],
Liu, W.[Wenyang],
Yap, K.H.[Kim-Hui],
CM2-Net: Continual Cross-Modal Mapping Network for Driver Action
Recognition,
ICIP24(2236-2242)
IEEE DOI
2411
Continuing education, Image recognition, Feature extraction, Boosting,
Safety, Automobiles, Vehicles, Driver action recognition, continual learning
BibRef
Nguyen, H.H.[Huy-Hung],
Tran, C.D.[Chi Dai],
Pham, L.H.[Long Hoang],
Tran, D.N.N.[Duong Nguyen-Ngoc],
Tran, T.H.P.[Tai Huu-Phuong],
Vu, D.K.[Duong Khac],
Ho, Q.P.N.[Quoc Pham-Nam],
Huynh, N.D.M.[Ngoc Doan-Minh],
Jeon, H.M.[Hyung-Min],
Jeon, H.J.[Hyung-Joon],
Jeon, J.W.[Jae Wook],
Multi-View Spatial-Temporal Learning for Understanding Unusual
Behaviors in Untrimmed Naturalistic Driving Videos,
AICity24(7144-7152)
IEEE DOI Code:
WWW Link.
2410
Location awareness, Source coding, Urban areas, X3D,
Prediction algorithms, Classification algorithms,
action recognition
BibRef
Yang, Y.[Yi],
Zhang, Q.W.[Qing-Wen],
Li, C.[Ci],
Marta, D.S.[Daniel Simões],
Batool, N.[Nazre],
Folkesson, J.[John],
Human-Centric Autonomous Systems With LLMs for User Command Reasoning,
LLVMCrive24(988-994)
IEEE DOI
2404
Codes, Natural languages, Fasteners, Cognition, Reliability
BibRef
Fu, D.C.[Dao-Cheng],
Li, X.[Xin],
Wen, L.C.[Li-Cheng],
Dou, M.[Min],
Cai, P.L.[Pin-Long],
Shi, B.[Botian],
Qiao, Y.[Yu],
Drive Like a Human: Rethinking Autonomous Driving with Large Language
Models,
LLVMCrive24(910-919)
IEEE DOI Code:
WWW Link.
2404
Industries, Buildings, MIMICs, Drives, Cognition, Autonomous vehicles
BibRef
Wang, J.B.[Jing-Bo],
Yuan, Y.[Ye],
Luo, Z.Y.[Zheng-Yi],
Xie, K.[Kevin],
Lin, D.[Dahua],
Iqbal, U.[Umar],
Fidler, S.[Sanja],
Khamis, S.[Sameh],
Learning Human Dynamics in Autonomous Driving Scenarios,
ICCV23(20739-20749)
IEEE DOI
2401
BibRef
Jaeger, B.[Bernhard],
Chitta, K.[Kashyap],
Geiger, A.[Andreas],
Hidden Biases of End-to-End Driving Models,
ICCV23(8206-8215)
IEEE DOI
2401
BibRef
Teixeira e Silva, D.[Diana],
Cruz, R.P.M.[Ricardo P. M.],
Condition Invariance for Autonomous Driving by Adversarial Learning,
CIARP23(I:552-563).
Springer DOI
2312
BibRef
Zhang, T.T.[Tian-Tian],
Wang, Q.[Qingtian],
Dong, X.D.[Xiao-Dong],
Yu, W.Q.[Wen-Qing],
Sun, H.[Hao],
Zhou, X.Y.[Xu-Yang],
Zhen, A.[Aigong],
Cui, S.[Shun],
Wu, D.[Dong],
He, Z.J.[Zhong-Jiang],
Augmented Self-Mask Attention Transformer for Naturalistic Driving
Action Recognition,
AICity24(7108-7114)
IEEE DOI Code:
WWW Link.
2410
Computational modeling, Urban areas, Computer architecture,
Feature extraction, Transformers, Action Recognition,
Self-supervised Learning
BibRef
Dong, X.D.[Xiao-Dong],
Zhao, R.J.[Rui-Jie],
Sun, H.[Hao],
Wu, D.[Dong],
Wang, J.[Jin],
Zhou, X.Y.[Xu-Yang],
Liu, J.[Jiang],
Cui, S.[Shun],
He, Z.J.[Zhong-Jiang],
Multi-Attention Transformer for Naturalistic Driving Action
Recognition,
AICity23(5435-5441)
IEEE DOI
2309
BibRef
Li, R.[Rongchang],
Wu, C.[Cong],
Li, L.[Linze],
Shen, Z.[Zhongwei],
Xu, T.Y.[Tian-Yang],
Wu, X.J.[Xiao-Jun],
Li, X.[Xi],
Lu, J.W.[Ji-Wen],
Kittler, J.V.[Josef V.],
Action Probability Calibration for Efficient Naturalistic Driving
Action Localization,
AICity23(5270-5277)
IEEE DOI
2309
BibRef
Aboah, A.[Armstrong],
Bagci, U.[Ulas],
Mussah, A.R.[Abdul Rashid],
Owor, N.J.[Neema Jakisa],
Adu-Gyamfi, Y.[Yaw],
DeepSegmenter: Temporal Action Localization for Detecting Anomalies
in Untrimmed Naturalistic Driving Videos,
AICity23(5359-5365)
IEEE DOI
2309
BibRef
Zhou, W.[Wei],
Qian, Y.L.[Yin-Long],
Jie, Z.Q.[Ze-Qun],
Ma, L.[Lin],
Multi View Action Recognition for Distracted Driver Behavior
Localization,
AICity23(5375-5380)
IEEE DOI
2309
BibRef
Chen, C.[Chen],
Da, S.[Si],
Research on Ship's Officer Behavior Identification Based on Mask
R-CNN,
ICRVC22(56-61)
IEEE DOI
2301
Bridges, Navigation, Neural networks, Robot sensing systems,
Skeleton, Real-time systems, Behavioral sciences, computer vision,
K Nearest Neighbors (KNN)
BibRef
Khan, S.S.[Shehroz S.],
Shen, Z.T.[Zi-Ting],
Sun, H.Y.[Hao-Ying],
Patel, A.[Ax],
Abedi, A.[Ali],
Supervised Contrastive Learning for Detecting Anomalous Driving
Behaviours from Multimodal Videos,
CRV22(16-23)
IEEE DOI
2301
Training, Visualization, Task analysis, Anomaly detection,
Video recording, Tuning, Standards, driving behaviours,
video anomaly detection
BibRef
Han, Y.[Yuci],
Yilmaz, A.[Alper],
Learning to Drive Using Sparse Imitation Reinforcement Learning,
ICPR22(3736-3742)
IEEE DOI
2212
Training, Process control, Reinforcement learning,
Hybrid power systems, Complexity theory, Behavioral sciences
BibRef
Zhang, Q.H.[Qi-Hang],
Peng, Z.H.[Zheng-Hao],
Zhou, B.[Bolei],
Learning to Drive by Watching YouTube Videos: Action-Conditioned
Contrastive Policy Pretraining,
ECCV22(XXVI:111-128).
Springer DOI
2211
BibRef
Chen, D.[Dian],
Krähenbühl, P.[Philipp],
Learning from All Vehicles,
CVPR22(17201-17210)
IEEE DOI
2210
Driving policy from many vehicles.
Training, Navigation, Computational modeling, Cognition,
Pattern recognition, Task analysis, Navigation and autonomous driving
BibRef
Zhao, H.Y.[Hang-Yue],
Xiao, Y.C.[Yu-Chao],
Zhao, Y.Y.[Yan-Yun],
PAND: Precise Action Recognition on Naturalistic Driving,
AICity22(3290-3298)
IEEE DOI
2210
Location awareness, Urban areas, Pipelines, Object detection,
Data models
BibRef
Zhang, J.Y.[Jimu-Yang],
Zhu, R.Z.[Rui-Zhao],
Ohn-Bar, E.[Eshed],
SelfD: Self-Learning Large-Scale Driving Policies From the Web,
CVPR22(17295-17305)
IEEE DOI
2210
Training, Navigation, Annotations, Data collection, Cameras,
Navigation and autonomous driving, Robot vision,
Self- semi- meta- Vision applications and systems
BibRef
Ding, G.C.[Guan-Chen],
Han, W.W.[Wen-Wei],
Wang, C.L.[Cheng-Long],
Cui, M.P.[Ming-Peng],
Zhou, L.[Lin],
Pan, D.[Dianbo],
Wang, J.Y.[Jia-Yi],
Zhang, J.X.[Jun-Xi],
Chen, Z.Z.[Zhen-Zhong],
A Coarse-to-Fine Boundary Localization method for Naturalistic
Driving Action Recognition,
AICity22(3233-3240)
IEEE DOI
2210
Location awareness, Urban areas, Estimation, Detectors,
Cameras, Behavioral sciences
BibRef
Nguyen, C.[Chuong],
Nguyen, N.[Ngoc],
Huynh, S.[Su],
Nguyen, V.[Vinh],
Nguyen, S.[Son],
Learning Generalized Feature for Temporal Action Detection:
Application for Natural Driving Action Recognition Challenge,
AICity22(3248-3255)
IEEE DOI
2210
Training, Urban areas, Supervised learning, Detectors,
Feature extraction
BibRef
Alkanat, T.[Tunc],
Akdag, E.[Erkut],
Bondarev, E.[Egor],
de With, P.H.N.[Peter H.N.],
Density-Guided Label Smoothing for Temporal Localization of Driving
Actions,
AICity22(3173-3181)
IEEE DOI
2210
Location awareness, Performance evaluation, Smoothing methods,
Urban areas, Cameras, Robustness, Probability distribution
BibRef
Le, H.D.[Huy Duong],
Vu, M.Q.[Minh Quan],
Tran, M.T.[Manh Tung],
Phuc, N.V.[Nguyen Van],
Triplet Temporal-based Video Recognition with Multiview for Temporal
Action Localization,
AICity23(5428-5434)
IEEE DOI
2309
BibRef
Tran, M.T.[Manh Tung],
Vu, M.Q.[Minh Quan],
Hoang, N.D.[Ngoc Duong],
Bui, K.H.N.[Khac-Hoai Nam],
An Effective Temporal Localization Method with Multi-View 3D Action
Recognition for Untrimmed Naturalistic Driving Videos,
AICity22(3167-3172)
IEEE DOI
2210
Location awareness, Training, Solid modeling, Correlation,
Urban areas, X3D
BibRef
Bharadhwaj, M.[Manoj],
Ramadurai, G.[Gitakrishnan],
Ravindran, B.[Balaraman],
Detecting Vehicles on the Edge: Knowledge Distillation to Improve
Performance in Heterogeneous Road Traffic,
AICity22(3191-3197)
IEEE DOI
2210
Performance evaluation, Image edge detection,
Computational modeling, Soft sensors, Roads, Detectors, Real-time systems
BibRef
Greco, A.[Antonio],
Rundo, L.[Leonardo],
Saggese, A.[Alessia],
Vento, M.[Mario],
Vicinanza, A.[Antonio],
Imitation Learning for Autonomous Vehicle Driving:
How Does the Representation Matter?,
CIAP22(I:15-26).
Springer DOI
2205
BibRef
Gawronski, P.[Peter],
Burschka, D.[Darius],
Visual Prediction of Driver Behavior in Shared Road Areas,
ICPR21(9614-9621)
IEEE DOI
2105
Visualization, Roads, Collaboration, Cameras, Time measurement,
Trajectory, Topology
BibRef
Wang, S.[Shun],
Zhou, F.[Fang],
Chen, S.L.[Song-Lu],
Yang, C.[Chun],
Recurrent Graph Convolutional Network for Skeleton-based Abnormal
Driving Behavior Recognition,
DLPR20(551-565).
Springer DOI
2103
BibRef
Ameksa, M.,
Mousannif, H.,
Moatassime, H.A.[H. Al],
Elassad, Z.E.A.[Z. Elamrani Abou],
Toward Flexible Data Collection of Driving Behaviour,
SmartCityApp20(33-43).
DOI Link
2012
BibRef
Xia, Y.,
Kim, J.,
Canny, J.,
Zipser, K.,
Canas-Bajo, T.,
Whitney, D.,
Periphery-Fovea Multi-Resolution Driving Model Guided by Human
Attention,
WACV20(1756-1764)
IEEE DOI
2006
Visualization, Vehicles, Predictive models, Videos,
Feature extraction, Computational modeling, Cameras
BibRef
Li, M.G.,
Jiang, B.,
Che, Z.,
Shi, X.,
Liu, M.,
Meng, Y.,
Ye, J.,
Liu, Y.,
DBUS: Human Driving Behavior Understanding System,
ADW19(2436-2444)
IEEE DOI
2004
automobiles, behavioural sciences computing,
driver information systems, image segmentation, neural nets, Sensor Fusion
BibRef
Codevilla, F.,
Santana, E.,
Lopez, A.,
Gaidon, A.,
Exploring the Limitations of Behavior Cloning for Autonomous Driving,
ICCV19(9328-9337)
IEEE DOI
2004
Dataset, Driver Behavior.
WWW Link. behavioural sciences computing,
learning (artificial intelligence), neural nets, Vehicle dynamics
BibRef
Martin, M.,
Roitberg, A.,
Haurilet, M.,
Horne, M.,
Reiß, S.,
Voit, M.,
Stiefelhagen, R.,
Drive Act: A Multi-Modal Dataset for Fine-Grained Driver Behavior
Recognition in Autonomous Vehicles,
ICCV19(2801-2810)
IEEE DOI
2004
behavioural sciences computing, feature extraction,
image motion analysis, learning (artificial intelligence),
Skeleton
BibRef
Hong, J.[Joey],
Sapp, B.[Benjamin],
Philbin, J.[James],
Rules of the Road: Predicting Driving Behavior With a Convolutional
Model of Semantic Interactions,
CVPR19(8446-8454).
IEEE DOI
2002
BibRef
Koetsier, C.,
Busch, S.,
Sester, M.,
Trajectory Extraction for Analysis of Unsafe Driving Behaviour,
C3MGBD19(1573-1578).
DOI Link
1912
BibRef
Jegham, I.[Imen],
Ben Khalifa, A.[Anouar],
Alouani, I.[Ihsen],
Mahjoub, M.A.[Mohamed Ali],
MDAD: A Multimodal and Multiview in-Vehicle Driver Action Dataset,
CAIP19(I:518-529).
Springer DOI
1909
BibRef
Kaushik, M.[Meha],
Krishna, K.M.[K. Madhava],
Learning Driving Behaviors for Automated Cars in Unstructured
Environments,
AutoNUE18(V:583-599).
Springer DOI
1905
BibRef
Maqueda, A.I.[Ana I.],
Loquercio, A.[Antonio],
Gallego, G.[Guillermo],
García, N.[Narciso],
Scaramuzza, D.[Davide],
Event-Based Vision Meets Deep Learning on Steering Prediction for
Self-Driving Cars,
CVPR18(5419-5427)
IEEE DOI
1812
Cameras, Gray-scale, Task analysis, Neural networks,
Feature extraction, Histograms
BibRef
Brahma, P.P.,
Othon, A.,
Subset Replay Based Continual Learning for Scalable Improvement of
Autonomous Systems,
AutoDrive18(1179-11798)
IEEE DOI
1812
Training, Task analysis, Machine learning, Computational modeling,
Training data, Neural networks, Feature extraction
BibRef
Bera, A.,
Randhavane, T.,
Wang, A.,
Manocha, D.,
Kubin, E.,
Gray, K.,
Classifying Group Emotions for Socially-Aware Autonomous Vehicle
Navigation,
AutoDrive18(1152-11528)
IEEE DOI
1812
Navigation, Trajectory, Psychology, Computational modeling,
Autonomous robots, Heuristic algorithms
BibRef
Cheung, E.,
Bera, A.,
Manocha, D.,
Efficient and Safe Vehicle Navigation Based on Driver Behavior
Classification,
AutoDrive18(1137-11377)
IEEE DOI
1812
Trajectory, Navigation, Measurement, Autonomous vehicles,
Feature extraction, Automobiles
BibRef
He, S.,
Kangin, D.,
Mi, Y.,
Pugeault, N.,
Aggregated Sparse Attention for Steering Angle Prediction,
ICPR18(2398-2403)
IEEE DOI
1812
aggregates (materials), automobiles, driver information systems,
steering systems, vehicle dynamics, aggregated sparse attention,
Training
BibRef
Chen, Y.,
Wang, J.,
Li, J.,
Lu, C.,
Luo, Z.,
Xue, H.,
Wang, C.,
LiDAR-Video Driving Dataset: Learning Driving Policies Effectively,
CVPR18(5870-5878)
IEEE DOI
1812
Benchmark testing, Sensors, Roads,
Laser radar, Autonomous vehicles
BibRef
Ramanishka, V.,
Chen, Y.,
Misu, T.,
Saenko, K.,
Toward Driving Scene Understanding:
A Dataset for Learning Driver Behavior and Causal Reasoning,
CVPR18(7699-7707)
IEEE DOI
1812
Vehicles, Task analysis, Global Positioning System, Cognition,
Sensors, Cameras, Road transportation
BibRef
Morita, S.[Satoru],
Visualizing Viewpoint Movement on Driving by Space Information
Rendering,
ISVC18(205-214).
Springer DOI
1811
BibRef
Hecker, S.[Simon],
Dai, D.X.[Deng-Xin],
Van Gool, L.J.[Luc J.],
End-to-End Learning of Driving Models with Surround-View Cameras and
Route Planners,
ECCV18(VII: 449-468).
Springer DOI
1810
BibRef
Xu, J.,
Chen, Y.A.,
Guo, K.,
Wang, J.,
Menchinelli, F.,
Jiang, C.,
Zhang, C.,
Shao, L.,
What has been missed for real life driving? an inspirational thinking
from human innate biases,
AVSS17(1-6)
IEEE DOI
1806
cognition, cognitive systems, driver information systems, ear, eye,
learning (artificial intelligence), acoustician researchers,
vision researchers
BibRef
Budvytis, I.,
Sauer, P.,
Roddick, T.,
Breen, K.,
Cipolla, R.,
Large Scale Labelled Video Data Augmentation for Semantic
Segmentation in Driving Scenarios,
CVRoads17(230-237)
IEEE DOI
1802
Autonomous vehicles, Image segmentation, Labeling,
Machine learning, Semantics, Training, Uncertainty
BibRef
Bautista, M.A.[Miguel A.],
Fuchs, P.[Patrick],
Ommer, B.[Björn],
Learning Where to Drive by Watching Others,
GCPR17(29-40).
Springer DOI
1711
BibRef
Xu, H.,
Gao, Y.,
Yu, F.,
Darrell, T.J.,
End-to-End Learning of Driving Models from Large-Scale Video Datasets,
CVPR17(3530-3538)
IEEE DOI
1711
Computer architecture, Data models, Motion segmentation,
Predictive models, Training, Visualization
BibRef
Minhas, S.[Saad],
Hernández-Sabaté, A.[Aura],
Ehsan, S.[Shoaib],
Díaz-Chito, K.[Katerine],
Leonardis, A.[Ales],
López, A.M.[Antonio M.],
McDonald-Maier, K.D.[Klaus D.],
LEE: A Photorealistic Virtual Environment for Assessing Driver-Vehicle
Interactions in Self-driving Mode,
VARVAI16(III: 894-900).
Springer DOI
1611
BibRef
Chang, S.P.[Shao-Pin],
Chien, J.T.[Jui-Ting],
Wang, F.E.[Fu-En],
Yang, S.D.[Shang-Da],
Chen, H.T.[Hwann-Tzong],
Sun, M.[Min],
Extracting Driving Behavior:
Global Metric Localization from Dashcam Videos in the Wild,
CVRoads16(I: 136-148).
Springer DOI
1611
BibRef
Bradler, H.,
Wiegand, B.A.,
Mester, R.,
The Statistics of Driving Sequences --
And What We Can Learn from Them,
CVRoads15(106-114)
IEEE DOI
1602
Adaptive optics
BibRef
Wakita, T.[Toshihiro],
Ozawa, K.[Koji],
Miyajima, C.[Chiyomi],
Takeda, K.[Kazuya],
Parametric Versus Non-parametric Models of Driving Behavior Signals for
Driver Identification,
AVBPA05(739).
Springer DOI
0509
BibRef
Chapter on Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following continues in
Driving Simulator, Simulated driving .