Aldebaran Robotics,
2005.
WWW Link.
Vendor, Robots. The Nao robot.
A robot that sees, speaks, reacts to touch and surfs the web
as one story on it says.
Evolution Robotics,
2001.
Visual controlled robotics.
WWW Link.
Vendor, Autonomous Navigation. visual Pattern Recognition package,
visual Simultaneous Localization and Mapping package.
Cloud Cap Technology,
1999.
WWW Link.
Vendor, Autonomous Aircraft. A Goodrich Company.
Seegrid Corporation,
2010.
WWW Link.
Vendor, Mobile Robots. Vision for mapping and control. Sells vision-guided robots for
moving materials.
Flightmare,
2020.
WWW Link.
Code, Drone Control. Photorealistic, customizable, and easy to use simulator for quadrotors!
It is compatible with ROS, Gazebo, OpenAI Gym, and even Oculus #VR headsets.
Also a set of reinforcement learning baselines for benchmarking.
DDD17: End-To-End DAVIS Driving Dataset,
2017
WWW Link.
Dataset, Road Scenes. Over 12 h of a 346x260 pixel DAVIS sensor recording highway and city
driving in daytime, evening, night, dry and wet weather.
Waymo Open Dataset,
2020
WWW Link.
Dataset, Road Scenes. high-resolution sensor data collected by autonomous vehicles operated
by the Waymo Driver in a wide variety of situations.
Cameron, S.[Stephen],
Probert, P.[Penelope], (Eds.)
Advanced Guided Vehicles:
Aspects of the Oxford AGV Project,
World ScientificOctober 1994.
ISBN: 978-981-02-1393-0.
HTML Version. Reports on the Oxford University projects.
BibRef
9410
UZH FPV Drone Racing Dataset 2.0,
2024
WWW Link.
Dataset, Visual Odometry.
Dataset, SLAM. The dataset comprises dozens of real-world sequences where a quadrotor
controlled in first-person view (FPV) by a professional pilot has been
flown both indoors and outdoors. Each sequence contains images, IMU,
and events (from an event-based camera) recorded on-board, as well as
ground truth from a robotic total station or motion capture system.
UZH-FPV Drone Racing Dataset,
2019.
HTML Version. Earlier version.
28 real-world sequences where a quadrotor controlled in first-person view.
1906
See also Are We Ready for Autonomous Drone Racing? The UZH-FPV Drone Racing Dataset.
The ROad event Awareness Dataset for Autonomous Driving (ROAD),
2021
WWW Link.
Dataset, Autonomous Driving. It contains 22 long-duration videos (ca 8 minutes each), ideal for
continual learning research, annotated in terms of road
events, defined as triplets E = (Agent, Action, Location) and
represented as tubes, i.e., a series of frame-wise bounding
box detections.
ROAD is a large, high-quality multi-label benchmark, with 122K
labelled video frames comprising 560K detection bounding boxes
associated with 1.7M unique individual labels (560K agent labels, 640K
action labels and 499K location labels).
DSEC: A Stereo Event Camera Dataset for Driving Scenarios,
2021.
HTML Version. CVPR 2021 competition dataset.
Dataset, Stereo.
Dataset, Driving.
2104
Stereo Event Camera large-scale dataset for challenging driving
scenarios! DSEC features over 400GB of data including stereo VGA
Prophesee event cameras, stereo RGB cameras, Velodyne lidar, and
RTK-GPS, recorded in challenging high-dynamic-range, day and night,
sunrise and sunset, urban and Swiss-mountain driving scenarios.
Iyengar, S.S., and
Kashyap, R.L., Guest editors for
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Berlin:
Springer-Verlag1992, 356 pp.
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BookReferenced as
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VVGCollection of articles on the IVHS (intelligent vehicle and
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Weiss, L.E.[Lee E.],
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Review of vision systems for robotics.
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Intelligent Robots: Control and Cooperation,
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Masaki, I.,
Vision-Based Mobile Robots on Highways,
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ErlbaumHillsdale, NJ, 1996.
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Broggi, A.,
Preface to the Special Section on Machine Vision for
Intelligent Vehicles and Autonomous Robots,
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9807
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Vision-Based Vehicle Guidance,
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9708
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Machine-Vision Systems for Intelligent Transportation Systems,
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9812
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World Scientific1999, ISBN 981-02-3720-0.
Survey, Vehicle Guidance. Surveys work in guidance up through the ARGO project.
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Broggi, A.[Alberto],
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0202
Mobile Robots.
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0407
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0612
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Matthies, L.H.[Larry H.],
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Johnson, A.[Andrew],
Cheng, Y.[Yang],
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Goldberg, S.[Steve],
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Bechar, A.,
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Evaluation of different levels of interaction.
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real-time switching. Adapt to changes in environment.
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Diosi, A.,
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PROUD: Public Road Urban Driverless-Car Test,
ITS(16), No. 6, December 2015, pp. 3508-3519.
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1512
Autonomous automobiles
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Kirkpatrick, K.[Keith],
The Moral Challenges of Driverless Cars,
CACM(58), No. 8, August 2015, pp 19-20.
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1507
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Greenblatt, N.A.,
Self-driving cars and the law,
Spectrum(53), No. 2, February 2016, pp. 46-51.
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1603
Accidents; Autonomous automobiles; Legal aspects
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News
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Li, L.,
Hu, D.,
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China,
ITS(17), No. 7, July 2016, pp. 2020-2021.
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China;Special issues and sections;Unmanned aerial vehicles
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Survey, Autonomous Vehicles. Automobiles, Autonomous automobiles, Legged locomotion, Roads, Urban areas
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Signal Processing Improves Autonomous Vehicle Navigation Accuracy:
Guidance Innovations Promise Safer and More Reliable Autonomous
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SPMag(36), No. 2, March 2019, pp. 15-18.
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1903
[Special Reports]
mobile robots, navigation, remotely operated vehicles,
signal processing, telecommunication network reliability,
Autonomous vehicles
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Autonomous trucks need people,
Spectrum(56), No. 3, March 2019, pp. 21-21.
IEEE DOI
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[Opinion]
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Zhou, Z.Q.[Zhi Quan],
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Metamorphic Testing of Driverless Cars,
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DOI Link
1906
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Authors, N.[No],
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Emerging Applications and Technologies,
IET-ITS(13), No. 6, June 2019, pp. 925-926.
DOI Link
1906
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Malone, K.M.[Kerry M.],
Soekroella, A.M.G.[Aroen M.G.],
Estimating benefits of C-ITS deployment, when legacy roadside systems
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IET-ITS(13), No. 5, May 2019, pp. 915-924.
DOI Link
1906
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Curry, E.,
Sheth, A.,
Next-Generation Smart Environments:
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IEEE_Int_Sys(33), No. 3, May-June 2018, pp. 69-76.
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1908
Digital environments supporting smart cars, etc.
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Laplante, P.[Phil],
My Mother the Car
(or Why It's a Bad Idea to Give Your Car a Personality),
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1908
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AI Engineers: The autonomous-vehicle industry wants you: Cruise's AI
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Spectrum(56), No. 09, September 2019, pp. 4-4.
IEEE DOI
1909
News item, Spectral Lines.
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Regazzoni, C.,
Pitas, I.,
Perspectives in Autonomous Systems Research,
SPMag(36), No. 5, September 2019, pp. 148-147.
IEEE DOI
1909
[In the Spotlight Section]
Autonomous systems, Sensors, Task analysis, Actuators, Statistical analysis
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SecurityPrivacy(16), No. 5, 2018.
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1912
Discussion of legal needs.
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Xu, S.B.[Shao-Bing],
Peng, H.[Huei],
Design, Analysis, and Experiments of Preview Path Tracking Control
for Autonomous Vehicles,
ITS(21), No. 1, January 2020, pp. 48-58.
IEEE DOI
2001
Vehicle dynamics, Roads, Trajectory, Optimization,
Heuristic algorithms, Frequency-domain analysis,
vehicle dynamics control
BibRef
Xu, S.B.[Shao-Bing],
Peng, H.[Huei],
Tang, Y.F.[Yi-Fan],
Preview Path Tracking Control With Delay Compensation for Autonomous
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ITS(22), No. 5, May 2021, pp. 2979-2989.
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Delays, Stability analysis, Tracking, Vehicle dynamics,
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preview control
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Perry, T.S.,
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2003
Spectral Lines. News item.
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Liu, S.,
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Autonomous vehicles lite self-driving technologies should start
small, go slow,
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IEEE DOI
2003
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Edwards, J.,
Robotics Rolls Into High Gear With Signal Processing: A robotics
revolution promises to transform global industries and services, and
signal processing is at the forefront,
SPMag(37), No. 2, March 2020, pp. 10-13.
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2003
[Special Reports]
Drones, Robots, Robot sensing systems, Signal processing.
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Zhou, W.,
Berrio, J.S.,
Worrall, S.,
Nebot, E.,
Automated Evaluation of Semantic Segmentation Robustness for
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ITS(21), No. 5, May 2020, pp. 1951-1963.
IEEE DOI
2005
System validation, semantic segmentation, autonomous driving
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Claussmann, L.,
Revilloud, M.,
Gruyer, D.,
Glaser, S.,
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ITS(21), No. 5, May 2020, pp. 1826-1848.
IEEE DOI
2005
Planning, Autonomous vehicles, Roads, Automotive engineering,
Automobiles, Advanced driver assistance systems,
path planning
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Skrickij, V.[Viktor],
abanovic, E.[Eldar],
uraulis, V.[Vidas],
Autonomous road vehicles: recent issues and expectations,
IET-ITS(14), No. 6, June 2020, pp. 471-479.
DOI Link
2005
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Yasuda, Y.D.V.[Yuri D. V.],
Martins, L.E.G.[Luiz Eduardo G.],
Cappabianco, F.A.M.[Fabio A. M.],
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2006
Survey, Autonomous Navigation. Mobile robots, visual navigation, systematic literature review,
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Karam, L.J.,
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Milanes, V.,
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Ye, J.,
Autonomous Driving: Part 1-Sensing and Perception,
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2007
[From the Guest Editors]
Special issue and sections, Autonomous vehicles, Laser radar,
Ultrasonic imaging, Safety
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Heath, R.W.,
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[From the Editor]
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Janai, J.[Joel],
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Survey, Autonomous Vehicles.
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Thomas, E.[Elena],
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Cooperative systems, Automation, Haptic interfaces,
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Bar, A.,
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Image segmentation, Perturbation methods, Semantics, Cameras,
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Deter, D.,
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Measurement, Heuristic algorithms, Signal processing algorithms,
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Transportation, Companies, Autonomous automobiles, Automobiles,
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Wang, X.,
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Visual Human-Computer Interactions for Intelligent Vehicles and
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Intelligent vehicles, Vehicles, Vehicle dynamics, Automation, Safety,
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2102
Autonomous vehicles, Deep learning, Task analysis, Training,
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Sensor fusion, Wheels, Planning, Laser radar, Radar measurements,
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Rosenbaum, L.,
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Timm, F.,
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Autonomous vehicles, Object detection, Cameras, Sensors, Laser radar,
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Elsevier DOI
2108
Cognitive vision, Deep semantics,
Declarative spatial reasoning, Spatial cognition and AI
BibRef
Chen, R.[Rui],
Arief, M.[Mansur],
Zhang, W.Y.[Wei-Yang],
Zhao, D.[Ding],
How to Evaluate Proving Grounds for Self-Driving? A Quantitative
Approach,
ITS(22), No. 9, September 2021, pp. 5737-5748.
IEEE DOI
2109
Testing, Roads, Trajectory, Data mining, Measurement, Vehicle dynamics,
Self-driving, testing, proving ground, design, unsupervised learning
BibRef
Uskova, O.[Olga],
On Russian Farms, the Robotic Revolution Has Begun: Hundreds of
Aftermarket AIs are Harvesting Grain,
Spectrum(58), No. 9, September 2021, pp. 40-45.
IEEE DOI
2109
Satellites, Receivers, Agricultural machinery, Rocks,
Robot sensing systems, Reliability, Sun
BibRef
Wu, Z.Y.[Zhong-Yi],
Zhou, H.M.[Hong-Mei],
Xi, H.J.[Hai-Jiao],
Wu, N.[Nan],
Analysing public acceptance of autonomous buses based on an extended
TAM model,
IET-ITS(15), No. 10, 2021, pp. 1318-1330.
DOI Link
2109
BibRef
Jiang, K.[Kun],
Yang, D.[Diange],
Wijaya, B.[Benny],
Zhang, B.[Bowei],
Yang, M.M.[Meng-Meng],
Zhang, K.[Kai],
Tang, X.W.[Xue-Wei],
Adding ears to intelligent connected vehicles by combining microphone
arrays and high definition map,
IET-ITS(15), No. 10, 2021, pp. 1228-1240.
DOI Link
2109
BibRef
Li, L.[Li],
Zheng, N.N.[Nan-Ning],
Wang, F.Y.[Fei-Yue],
A Theoretical Foundation of Intelligence Testing and Its Application
for Intelligent Vehicles,
ITS(22), No. 10, October 2021, pp. 6297-6306.
IEEE DOI
2110
Testing, Intelligent vehicles, Picture archiving and communication systems,
probably approximately correct (PAC) learning
BibRef
Chattopadhyay, A.[Anupam],
Lam, K.Y.[Kwok-Yan],
Tavva, Y.[Yaswanth],
Autonomous Vehicle: Security by Design,
ITS(22), No. 11, November 2021, pp. 7015-7029.
IEEE DOI
2112
Security, Automobiles, Sensor systems, Actuators, Standards,
Autonomous vehicles, Autonomous vehicles (AV), security,
sociotechnical systems
BibRef
Yu, T.F.[Teng-Fei],
Huang, H.[He],
Jiang, N.[Nana],
Acharya, T.D.[Tri Dev],
Study on Relative Accuracy and Verification Method of High-Definition
Maps for Autonomous Driving,
IJGI(10), No. 11, 2021, pp. xx-yy.
DOI Link
2112
BibRef
Thombre, S.[Sarang],
Zhao, Z.[Zheng],
Ramm-Schmidt, H.[Henrik],
García, J.M.V.[José M. Vallet],
Malkamäki, T.[Tuomo],
Nikolskiy, S.[Sergey],
Hammarberg, T.[Toni],
Nuortie, H.[Hiski],
Bhuiyan, M.Z.H.[M. Zahidul H.],
Särkkä, S.[Simo],
Lehtola, V.V.[Ville V.],
Sensors and AI Techniques for Situational Awareness in Autonomous
Ships: A Review,
ITS(23), No. 1, January 2022, pp. 64-83.
IEEE DOI
2201
Marine vehicles, Sensor systems, Artificial intelligence,
Intelligent sensors, Global navigation satellite system,
maritime
BibRef
Mozaffari, S.[Sajjad],
Al-Jarrah, O.Y.[Omar Y.],
Dianati, M.[Mehrdad],
Jennings, P.[Paul],
Mouzakitis, A.[Alexandros],
Deep Learning-Based Vehicle Behavior Prediction for Autonomous
Driving Applications: A Review,
ITS(23), No. 1, January 2022, pp. 33-47.
IEEE DOI
2201
TV, Autonomous vehicles, Sensors, Roads, History, Machine learning,
Trajectory, Vehicle behaviour prediction, trajectory prediction,
deep learning
BibRef
Liu, S.S.[Shao-Shan],
Gaudiot, J.L.[Jean-Luc],
Rise of the Autonomous Machines,
Computer(55), No. 1, January 2022, pp. 64-73.
IEEE DOI
2201
Planning, Cameras, Location awareness,
Semantics, Synchronization, Software
BibRef
Ross, P.E.[Philip E.],
Flying Pallets Without Pilots: A drone startup will test a radical
new vision of long-range cargo transport in Europe,
Spectrum(59), No. 1, January 2022, pp. 30-31.
IEEE DOI
2201
Airplanes, Atmospheric modeling, Pallets, Drones
BibRef
Tu, Y.J.T.[Yu-Ju Tony],
Shang, S.S.[Shari S.],
Wu, J.[Junyi],
Is Your Autonomous Vehicle as Smart as You Expected?,
CACM(65), No. 2, February 2022, pp. 31-34.
DOI Link
2202
BibRef
Bonnefon, J.F.,
Shariff, A.,
Rahwan, I.,
The social dilemma of autonomous vehicles,
Science(352), No. 6293, 2016, pp. 1573-1576.
DOI Link
BibRef
1600
Aradi, S.[Szilárd],
Survey of Deep Reinforcement Learning for Motion Planning of
Autonomous Vehicles,
ITS(23), No. 2, February 2022, pp. 740-759.
IEEE DOI
2202
Planning, Autonomous vehicles,
Learning (artificial intelligence), Machine learning, Trajectory,
reinforcement learning
BibRef
Minhas, S.[Saad],
Hernández-Sabaté, A.[Aura],
Ehsan, S.[Shoaib],
McDonald-Maier, K.D.[Klaus D.],
Effects of Non-Driving Related Tasks During Self-Driving Mode,
ITS(23), No. 2, February 2022, pp. 1391-1399.
IEEE DOI
2202
Automobiles, Roads, Task analysis, Manuals, Autonomous vehicles,
Computer crashes, Mental work capacity, virtual environment
BibRef
Machida, T.[Takashi],
Shitaoka, K.[Kazuya],
Test Coverage Index for ADAS/ADS Assessment Based on Various
Real-World Information Points,
ITS(23), No. 2, February 2022, pp. 1443-1455.
IEEE DOI
2202
How to assess
Advanced Driving Assistance System/Autonomous Driving System.
Indexes, Roads, Search problems, Accidents, Sensors, Cameras,
ADAS/ADS assessment, test coverage index, real world modeling, FOT
BibRef
Gao, H.B.[Hong-Bo],
Zhu, J.[Juping],
Zhang, T.[Tong],
Xie, G.[Guotao],
Kan, Z.[Zhen],
Hao, Z.Y.[Zheng-Yuan],
Liu, K.[Kang],
Situational Assessment for Intelligent Vehicles Based on Stochastic
Model and Gaussian Distributions in Typical Traffic Scenarios,
SMCS(52), No. 3, March 2022, pp. 1426-1436.
IEEE DOI
2202
Uncertainty, Stochastic processes, Risk management, Trajectory,
Vehicle dynamics, Intelligent vehicles, Predictive models,
uncertainty risk awareness
BibRef
Kirkpatrick, K.[Keith],
Still Waiting for Self-Driving Cars,
CACM(65), No. 4, April 2022, pp. 12-14.
DOI Link
2204
News analysis of why.
BibRef
Kuhn, C.B.[Christopher B.],
Hofbauer, M.[Markus],
Petrovic, G.[Goran],
Steinbach, E.[Eckehard],
Introspective Failure Prediction for Autonomous Driving Using Late
Fusion of State and Camera Information,
ITS(23), No. 5, May 2022, pp. 4445-4459.
IEEE DOI
2205
Uncertainty, Autonomous vehicles, Automobiles, Predictive models,
Accidents, Data models, Estimation, Failure prediction,
introspection
BibRef
Kiran, B.R.[B. Ravi],
Sobh, I.[Ibrahim],
Talpaert, V.[Victor],
Mannion, P.[Patrick],
Al Sallab, A.A.[Ahmad A.],
Yogamani, S.[Senthil],
Pérez, P.[Patrick],
Deep Reinforcement Learning for Autonomous Driving: A Survey,
ITS(23), No. 6, June 2022, pp. 4909-4926.
IEEE DOI
2206
Reinforcement learning, Autonomous vehicles, Task analysis,
Planning, Robot sensing systems, Pipelines, Decision making,
safe reinforcement learning
BibRef
Qian, R.[Rui],
Lai, X.[Xin],
Li, X.R.[Xi-Rong],
3D Object Detection for Autonomous Driving: A Survey,
PR(130), 2022, pp. 108796.
Elsevier DOI
2206
Survey, Object Detection. 3D object detection, Autonomous driving, Point clouds
BibRef
Cao, Z.[Zhong],
Xu, S.B.[Shao-Bing],
Peng, H.[Huei],
Yang, D.[Diange],
Zidek, R.[Robert],
Confidence-Aware Reinforcement Learning for Self-Driving Cars,
ITS(23), No. 7, July 2022, pp. 7419-7430.
IEEE DOI
2207
Training, Reinforcement learning, Autonomous vehicles,
Training data, Markov processes, Trajectory, Planning, motion planning
BibRef
Zhu, B.[Bing],
Zhang, P.X.[Pei-Xing],
Zhao, J.[Jian],
Deng, W.W.[Wei-Wen],
Hazardous Scenario Enhanced Generation for Automated Vehicle Testing
Based on Optimization Searching Method,
ITS(23), No. 7, July 2022, pp. 7321-7331.
IEEE DOI
2207
Testing, Space exploration, Optimization, Security, Probability,
Monte Carlo methods, Life estimation, Automated vehicles,
Optimization Searching
BibRef
Xu, S.[Sihan],
Wang, Z.Y.[Zhi-Yu],
Fan, L.L.[Ling-Ling],
Cai, X.[Xiangrui],
Ji, H.[Hua],
Khoo, S.C.[Siau-Cheng],
Gupta, B.B.[Brij Bhooshan],
DeepSuite: A Test Suite Optimizer for Autonomous Vehicles,
ITS(23), No. 7, July 2022, pp. 9506-9517.
IEEE DOI
2207
Testing, Autonomous vehicles, Neurons, Deep learning, Labeling,
Fuzzing, Statistics, Autonomous vehicles, data collection,
test suite optimization
BibRef
Derakhshan, S.[Shadi],
Nezami, F.N.[Farbod Nosrat],
Wächter, M.A.[Maximilian Alexander],
Czeszumski, A.[Artur],
Keshava, A.[Ashima],
Lukanov, H.[Hristofor],
de Palol, M.V.[Marc Vidal],
Pipa, G.[Gordon],
König, P.[Peter],
Talking Cars, Doubtful Users: A Population Study in Virtual Reality,
HMS(52), No. 4, August 2022, pp. 602-612.
IEEE DOI
2208
Automobiles, Magnetic heads, Angular velocity,
Analysis of variance, Resists, Correlation, Visualization,
virtual reality (VR)
BibRef
Su, H.T.[Hao-Tian],
Jia, Y.[Yunyi],
Study of Human Comfort in Autonomous Vehicles Using Wearable Sensors,
ITS(23), No. 8, August 2022, pp. 11490-11504.
IEEE DOI
2208
Physiology, Roads, Autonomous vehicles, Wearable sensors, Software,
Psychology, Brain modeling, Autonomous vehicles,
virtual environment
BibRef
Omeiza, D.[Daniel],
Webb, H.[Helena],
Jirotka, M.[Marina],
Kunze, L.[Lars],
Explanations in Autonomous Driving: A Survey,
ITS(23), No. 8, August 2022, pp. 10142-10162.
IEEE DOI
2208
Autonomous vehicles, Stakeholders, Automation, Regulation,
Artificial intelligence, Accidents, Standards, Explanations,
standards
BibRef
Yu, B.[Bo],
Chen, C.[Chongyu],
Tang, J.[Jie],
Liu, S.S.[Shao-Shan],
Gaudiot, J.L.[Jean-Luc],
Autonomous Vehicles Digital Twin: A Practical Paradigm for Autonomous
Driving System Development,
Computer(55), No. 9, September 2022, pp. 26-34.
IEEE DOI
2209
Digital twins, Reliability, Autonomous vehicles,
Performance evaluation, Testing
BibRef
Li, A.[Ao],
Chen, S.T.[Shi-Tao],
Sun, L.[Liting],
Zheng, N.N.[Nan-Ning],
Tomizuka, M.[Masayoshi],
Zhan, W.[Wei],
SceGene: Bio-Inspired Traffic Scenario Generation for Autonomous
Driving Testing,
ITS(23), No. 9, September 2022, pp. 14859-14874.
IEEE DOI
2209
Mathematical models, Biological information theory, Testing,
Microscopy, Evolution (biology), Biological system modeling,
scenario generation
BibRef
Zablocki, É.[Éloi],
Ben-Younes, H.[Hédi],
Pérez, P.[Patrick],
Cord, M.[Matthieu],
Explainability of Deep Vision-Based Autonomous Driving Systems:
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IJCV(130), No. 10, October 2022, pp. 2425-2452.
Springer DOI
2209
BibRef
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Yi, D.[Dewei],
Dianati, M.[Mehrdad],
Mouzakitis, A.[Alexandros],
A Survey on Imitation Learning Techniques for End-to-End Autonomous
Vehicles,
ITS(23), No. 9, September 2022, pp. 14128-14147.
IEEE DOI
2209
Autonomous vehicles, Task analysis, Cloning, Training, Deep learning,
Cameras, Uncertainty, Intelligent vehicles, autonomous vehicles,
neural networks
BibRef
Caillot, A.[Antoine],
Ouerghi, S.[Safa],
Vasseur, P.[Pascal],
Boutteau, R.[Rémi],
Dupuis, Y.[Yohan],
Survey on Cooperative Perception in an Automotive Context,
ITS(23), No. 9, September 2022, pp. 14204-14223.
IEEE DOI
2209
Sensors, Global Positioning System, Cameras, Task analysis,
Satellites, Location awareness, tracking
BibRef
Jiao, X.Y.[Xin-Yu],
Cao, Z.[Zhong],
Chen, J.J.[Jun-Jie],
Jiang, K.[Kun],
Yang, D.[Diange],
A General Autonomous Driving Planner Adaptive to Scenario
Characteristics,
ITS(23), No. 11, November 2022, pp. 21228-21240.
IEEE DOI
2212
Autonomous vehicles, Planning, Complexity theory,
Adaptation models, Semantics, Roads, multi-scenario driving decision
BibRef
Hacohen, S.[Shlomi],
Medina, O.[Oded],
Shoval, S.[Shraga],
Autonomous Driving: A Survey of Technological Gaps Using Google
Scholar and Web of Science Trend Analysis,
ITS(23), No. 11, November 2022, pp. 21241-21258.
IEEE DOI
2212
Location awareness, Roads, Sensors, Autonomous vehicles,
Internet, Laser radar, Autonomous vehicle, Google trends
BibRef
Zhou, M.T.[Ming-Ting],
Sui, H.G.[Hai-Gang],
Chen, S.X.[Shan-Xiong],
Chen, X.[Xu],
Wang, W.Q.[Wen-Qing],
Wang, J.X.[Jian-Xun],
Liu, J.[Junyi],
UGRoadUpd: An Unchanged-Guided Historical Road Database Updating
Framework Based on Bi-Temporal Remote Sensing Images,
ITS(23), No. 11, November 2022, pp. 21465-21477.
IEEE DOI
2212
Roads, Databases, Feature extraction, Image segmentation,
Remote sensing, Deep learning, Benchmark testing,
road change detection datasets
BibRef
Khan, M.A.[Manzoor Ahmed],
Sayed, H.E.[Hesham El],
Malik, S.[Sumbal],
Zia, T.[Talha],
Khan, J.[Jalal],
Alkaabi, N.[Najla],
Ignatious, H.[Henry],
Level-5 Autonomous Driving: Are We There Yet? A Review of Research
Literature,
Surveys(55), No. 2, February 2023, pp. xx-yy.
DOI Link
2212
Autonomous driving, mobile networks, sensor fusion, platooning, 5G
BibRef
Singh, G.[Gurkirt],
Akrigg, S.[Stephen],
di Maio, M.[Manuele],
Fontana, V.[Valentina],
Alitappeh, R.J.[Reza Javanmard],
Khan, S.[Salman],
Saha, S.[Suman],
Jeddisaravi, K.[Kossar],
Yousefi, F.[Farzad],
Culley, J.[Jacob],
Nicholson, T.[Tom],
Omokeowa, J.[Jordan],
Grazioso, S.[Stanislao],
Bradley, A.[Andrew],
di Gironimo, G.[Giuseppe],
Cuzzolin, F.[Fabio],
ROAD: The Road Event Awareness Dataset for Autonomous Driving,
PAMI(45), No. 1, January 2023, pp. 1036-1054.
IEEE DOI
2212
Dataset, Autonomous Driving. Roads, Autonomous vehicles, Task analysis, Videos, Benchmark testing,
Decision making, Vehicle dynamics, Autonomous driving,
decision making
BibRef
Gu, N.[Nan],
Wang, D.[Dan],
Peng, Z.H.[Zhou-Hua],
Wang, J.[Jun],
Han, Q.L.[Qing-Long],
Advances in Line-of-Sight Guidance for Path Following of Autonomous
Marine Vehicles: An Overview,
SMCS(53), No. 1, January 2023, pp. 12-28.
IEEE DOI
2301
Sea measurements, Motion control, Kinematics, Sea surface,
Current measurement, Attitude control, Acoustic measurements,
line-of-sight (LOS) guidance
BibRef
Li, Q.Y.[Quan-Yi],
Peng, Z.H.[Zheng-Hao],
Feng, L.[Lan],
Zhang, Q.H.[Qi-Hang],
Xue, Z.H.[Zheng-Hai],
Zhou, B.[Bolei],
MetaDrive: Composing Diverse Driving Scenarios for Generalizable
Reinforcement Learning,
PAMI(45), No. 3, March 2023, pp. 3461-3475.
IEEE DOI
2302
Task analysis, Roads, Reinforcement learning, Benchmark testing,
Training, Safety, Autonomous vehicles, Reinforcement learning, simulation
BibRef
Khosravian, A.[Amir],
Amirkhani, A.[Abdollah],
Masih-Tehrani, M.[Masoud],
Yazdanijoo, A.[Alireza],
Multi-domain autonomous driving dataset: Towards enhancing the
generalization of the convolutional neural networks in new
environments,
IET-IPR(17), No. 4, 2023, pp. 1253-1266.
DOI Link
2303
convolutional neural nets, database indexing, image annotation, vehicles
BibRef
Qiao, Y.Y.[Yuan-Yuan],
Yin, J.X.[Jia-Xin],
Wang, W.[Wei],
Duarte, F.[Fábio],
Yang, J.[Jie],
Ratti, C.[Carlo],
Survey of Deep Learning for Autonomous Surface Vehicles in Marine
Environments,
ITS(24), No. 4, April 2023, pp. 3678-3701.
IEEE DOI
2304
Sensors, Sea surface, Sensor systems, Marine vehicles,
Control systems, Deep learning, Task analysis,
neural network
BibRef
Farah, H.[Haneen],
Olstam, J.[Johan],
Zheng, Z.[Zuduo],
Guest Editorial Introduction to the Special Issue on Deployment of
Connected and Automated Vehicles in Mixed Traffic Environment and the
Implications on Traffic Safety and Efficiency,
ITS(24), No. 6, June 2023, pp. 6432-6435.
IEEE DOI
2306
Special issues and sections, Autonomous vehicles,
Connected vehicles, Traffic control, Vehicle safety
BibRef
Testolina, P.[Paolo],
Barbato, F.[Francesco],
Michieli, U.[Umberto],
Giordani, M.[Marco],
Zanuttigh, P.[Pietro],
Zorzi, M.[Michele],
SELMA: SEmantic Large-Scale Multimodal Acquisitions in Variable
Weather, Daytime and Viewpoints,
ITS(24), No. 7, July 2023, pp. 7012-7024.
IEEE DOI
2307
Cameras, Sensors, Semantics, Meteorology, Autonomous vehicles,
Task analysis, Synthetic data, Synthetic dataset, CARLA, sensor fusion
BibRef
Wang, Y.J.[Ying-Jie],
Mao, Q.Y.[Qiu-Yu],
Zhu, H.Q.[Han-Qi],
Deng, J.J.[Jia-Jun],
Zhang, Y.[Yu],
Ji, J.M.[Jian-Min],
Li, H.Q.[Hou-Qiang],
Zhang, Y.Y.[Yan-Yong],
Multi-Modal 3D Object Detection in Autonomous Driving: A Survey,
IJCV(131), No. 8, August 2023, pp. 2122-2152.
Springer DOI
2307
BibRef
Mao, R.Q.[Rui-Qing],
Guo, J.Y.[Jing-Yu],
Jia, Y.[Yukuan],
Sun, Y.X.[Yu-Xuan],
Zhou, S.[Sheng],
Niu, Z.S.[Zhi-Sheng],
Dolphins: Dataset for Collaborative Perception Enabled Harmonious and
Interconnected Self-driving,
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Springer DOI
2307
BibRef
Mao, J.G.[Jia-Geng],
Shi, S.S.[Shao-Shuai],
Wang, X.G.[Xiao-Gang],
Li, H.S.[Hong-Sheng],
3D Object Detection for Autonomous Driving: A Comprehensive Survey,
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Springer DOI
2307
Survey, Object Detection.
BibRef
Rahmani, S.[Saeed],
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Bouguila, N.[Nizar],
Patterson, Z.[Zachary],
Graph Neural Networks for Intelligent Transportation Systems:
A Survey,
ITS(24), No. 8, August 2023, pp. 8846-8885.
IEEE DOI
2308
Transportation, Forecasting, Graph neural networks, Deep learning,
Search problems, Laplace equations, Safety, Graph neural networks,
ITS
BibRef
Chen, L.[Long],
Li, Y.C.[Yu-Chen],
Huang, C.[Chao],
Xing, Y.[Yang],
Tian, D.X.[Da-Xin],
Li, L.[Li],
Hu, Z.X.[Zhong-Xu],
Teng, S.[Siyu],
Lv, C.[Chen],
Wang, J.J.[Jin-Jun],
Cao, D.[Dongpu],
Zheng, N.N.[Nan-Ning],
Wang, F.Y.[Fei-Yue],
Milestones in Autonomous Driving and Intelligent Vehicles: Part I:
Control, Computing System Design, Communication, HD Map, Testing, and
Human Behaviors,
SMCS(53), No. 9, September 2023, pp. 5831-5847.
IEEE DOI
2309
BibRef
Chen, L.[Long],
Teng, S.[Siyu],
Li, B.[Bai],
Na, X.X.[Xiao-Xiang],
Li, Y.C.[Yu-Chen],
Li, Z.X.[Zi-Xuan],
Wang, J.J.[Jin-Jun],
Cao, D.[Dongpu],
Zheng, N.N.[Nan-Ning],
Wang, F.Y.[Fei-Yue],
Milestones in Autonomous Driving and Intelligent Vehicles: Part II:
Perception and Planning,
SMCS(53), No. 10, October 2023, pp. 6401-6415.
IEEE DOI
2310
BibRef
Wang, Y.N.[Yu-Ning],
Jiang, J.[Junkai],
Li, S.[Shangyi],
Li, R.[Ruochen],
Xu, S.B.[Shao-Bing],
Wang, J.Q.[Jian-Qiang],
Li, K.Q.[Ke-Qiang],
Decision-Making Driven by Driver Intelligence and Environment
Reasoning for High-Level Autonomous Vehicles: A Survey,
ITS(24), No. 10, October 2023, pp. 10362-10381.
IEEE DOI
2310
BibRef
Yang, K.[Kai],
Tang, X.L.[Xiao-Lin],
Li, J.[Jun],
Wang, H.[Hong],
Zhong, G.[Guichuan],
Chen, J.X.[Jia-Xin],
Cao, D.[Dongpu],
Uncertainties in Onboard Algorithms for Autonomous Vehicles:
Challenges, Mitigation, and Perspectives,
ITS(24), No. 9, September 2023, pp. 8963-8987.
IEEE DOI
2310
BibRef
Huang, J.C.[Jian-Chang],
Song, G.H.[Guo-Hua],
He, F.[Feng],
Tan, Z.[Zhe],
Energetic Impacts of Autonomous Vehicles in Real-World Traffic
Conditions From Nine Open-Source Datasets,
ITS(24), No. 9, September 2023, pp. 9901-9914.
IEEE DOI
2310
BibRef
Zhu, Y.[Yu],
Wang, J.[Jian],
Guo, X.Y.[Xin-Yu],
Meng, F.[Fan=Qiang],
Liu, T.T.[Tong-Tao],
Functional Testing Scenario Library Generation Framework for
Connected and Automated Vehicles,
ITS(24), No. 9, September 2023, pp. 9712-9724.
IEEE DOI
2310
BibRef
Manikandan, N.S.,
Ganesan, K.,
Energy-aware automatic video annotation tool for autonomous vehicle,
IJCVR(13), No. 5, 2023, pp. 510-532.
DOI Link
2310
BibRef
Rabiee, S.[Sadegh],
Biswas, J.[Joydeep],
Introspective perception for mobile robots,
AI(324), 2023, pp. 103999.
Elsevier DOI
2312
Competence-aware perception, Introspection, Mobile robots
BibRef
Zhang, C.[Ce],
Eskandarian, A.[Azim],
A Quality Index Metric and Method for Online Self-Assessment of
Autonomous Vehicles Sensory Perception,
ITS(24), No. 12, December 2023, pp. 13801-13812.
IEEE DOI
2312
BibRef
Tengilimoglu, O.[Oguz],
Carsten, O.[Oliver],
Wadud, Z.[Zia],
Infrastructure-related challenges in implementing connected and
automated vehicles on urban roads:
Insights from experts and stakeholders,
IET-ITS(17), No. 12, 2023, pp. 2352-2368.
DOI Link
2312
automated driving, challenges,
connected and automated vehicles, financial requirements, urban network
BibRef
Xie, Y.Z.[Yi-Zhou],
Zhang, Y.[Yong],
Dai, K.[Kunpeng],
Yin, C.L.[Cheng-Liang],
A real-time critical-scenario-generation framework for defect
detection of autonomous driving system,
IET-ITS(18), No. 1, 2024, pp. 114-128.
DOI Link
2401
accident prevention, adaptive control,
automated driving and intelligent vehicles, safety, SOTIF
BibRef
Chen, Y.L.[Yi-Lun],
Shiwakoti, N.[Nirajan],
Stasinopoulos, P.[Peter],
Khan, S.K.[Shah Khalid],
Aghabayk, K.[Kayvan],
Exploring the association between socio-demographic factors and
public acceptance towards fully automated vehicles: Insights from a
survey in Australia,
IET-ITS(18), No. 1, 2024, pp. 154-172.
DOI Link
2401
automated driving and intelligent vehicles,
intelligent transportation systems, socio-economic effects, user experience
BibRef
Yatbaz, H.Y.[Hakan Yekta],
Dianati, M.[Mehrdad],
Woodman, R.[Roger],
Introspection of DNN-Based Perception Functions in Automated Driving
Systems: State-of-the-Art and Open Research Challenges,
ITS(25), No. 2, February 2024, pp. 1112-1130.
IEEE DOI
2402
Safety, Sensors, Object detection, Monitoring,
Artificial neural networks, Semantics, Semantic segmentation, deep learning
BibRef
Khalid, A.[Adnan],
Mushtaq, Z.[Zohaib],
Arif, S.[Saad],
Zeb, K.[Kamran],
Khan, M.A.[Muhammad Attique],
Bakshi, S.[Sambit],
Control Schemes for Quadrotor UAV: Taxonomy and Survey,
Surveys(56), No. 5, November 2023, pp. xx-yy.
DOI Link
2402
Fuzzy Logic Control, Sliding Mode Control,
Linear Quadratic Regulator, PID, Quadrotor, Unmanned Aerial Vehicles
BibRef
Rassőlkin, A.[Anton],
Maksimkins, P.[Pavels],
Stupans, A.[Andrejs],
Rjabtikov, V.[Viktor],
enfelds, A.[Armands],
Kuts, V.[Vladimir],
The Spatial Representation of a Self-Driving Vehicle for the Virtual
Entity of a Digital Twin,
Computer(57), No. 5, May 2024, pp. 58-66.
IEEE DOI
2405
Propulsion, Digital twins, Standards, Electric vehicles, PD control,
Adaptive control, Error analysis, Spatial resolution, Autonomous automobiles
BibRef
Lu, M.[Meng],
Cooperative and Automated Road Transport - European Perspective,
ITS(25), No. 4, April 2024, pp. 332-365.
IEEE DOI
2405
Connected vehicles, Autonomous systems, Roads, Europe,
Cooperative systems, Safety, Information and communication technology
BibRef
Cronin, B.[Brian],
Federal Highway Administration (FHWA) Update,
ITS(25), No. 4, April 2024, pp. 54-70.
IEEE DOI
2405
Automation, Connected vehicles, Roads, Standards,
Intelligent transportation systems, System of systems
BibRef
Ma, Z.[Zeyu],
Zheng, Z.Q.[Zi-Qiang],
Wei, J.[Jiwei],
Yang, Y.[Yang],
Shen, H.T.[Heng Tao],
Instance-Dictionary Learning for Open-World Object Detection in
Autonomous Driving Scenarios,
CirSysVideo(34), No. 5, May 2024, pp. 3395-3408.
IEEE DOI
2405
Object detection, Feature extraction, Dictionaries, Training,
Visualization, Semantics, Autonomous vehicles, autonomous driving
BibRef
Nawaz, M.[Mehmood],
Tang, J.K.T.[Jeff Kai-Tai],
Bibi, K.[Khadija],
Xiao, S.L.[Shun-Li],
Ho, H.P.[Ho-Pui],
Yuan, W.[Wu],
Robust Cognitive Capability in Autonomous Driving Using Sensor Fusion
Techniques: A Survey,
ITS(25), No. 5, May 2024, pp. 3228-3243.
IEEE DOI
2405
Sensor fusion, Autonomous vehicles, Cameras, Laser radar, Vehicles, Radar,
Meteorology, Sensor fusion, autonomous vehicles, RGB cameras, object tracking
BibRef
Zhang, C.[Chi],
Ma, X.N.[Xiao-Ning],
Xu, L.H.[Li-Heng],
Lu, H.A.[Hao-Ang],
Wang, L.[Le],
Su, Y.Q.[Yuan-Qi],
Liu, Y.H.[Yue-Hu],
Li, L.[Li],
Worst Perception Scenario Search via Recurrent Neural Controller and
K-Reciprocal Re-Ranking,
ITS(25), No. 6, June 2024, pp. 5612-5626.
IEEE DOI
2406
Improve method, by studying where it does worst.
Testing, Visualization, Autonomous vehicles, Task analysis,
Feature extraction, Visual perception,
K-reciprocal re-ranking
BibRef
Novo, A.[Alvaro],
Lobon, F.[Francisco],
Garcia-de Marina, H.[Hector],
Romero, S.[Samuel],
Barranco, F.[Francisco],
Neuromorphic Perception and Navigation for Mobile Robots: A Review,
Surveys(56), No. 10, May 2024, pp. xx-yy.
DOI Link
2407
Navigation, hippocampus, neuromorphic sensors, brain inspired
BibRef
Zolfaghari, B.[Behrouz],
Abbasmollaei, M.[Mostafa],
Hajizadeh, F.[Fahimeh],
Yanai, N.[Naoto],
Bibak, K.[Khodakhast],
Secure UAV (Drone) and the Great Promise of AI,
Surveys(56), No. 11, July 2024, pp. xx-yy.
DOI Link
2408
Unmanned aerial vehicle, UAV, drone, artificial intelligence, AI,
security, quantum inspired AI, bio-inspired AI
BibRef
Sadid, H.[Hashmatullah],
Antoniou, C.[Constantinos],
A simulation-based impact assessment of autonomous vehicles in urban
networks,
IET-ITS(18), No. 9, 2024, pp. 1677-1696.
DOI Link
2409
autonomous driving, safety, transport modelling and microsimulation
BibRef
Chen, L.[Li],
Wu, P.H.[Peng-Hao],
Chitta, K.[Kashyap],
Jaeger, B.[Bernhard],
Geiger, A.[Andreas],
Li, H.Y.[Hong-Yang],
End-to-End Autonomous Driving: Challenges and Frontiers,
PAMI(46), No. 12, December 2024, pp. 10164-10183.
IEEE DOI
2411
Task analysis, Planning, Autonomous vehicles, Trajectory, Surveys,
Imitation learning, Benchmark testing, Autonomous driving,
simulation
BibRef
Lin, J.C.[Jia-Cheng],
Chen, J.J.[Jia-Jun],
Peng, K.Y.[Kun-Yu],
He, X.[Xuan],
Li, Z.Y.[Zhi-Yong],
Stiefelhagen, R.[Rainer],
Yang, K.L.[Kai-Lun],
EchoTrack: Auditory Referring Multi-Object Tracking for Autonomous
Driving,
ITS(25), No. 11, November 2024, pp. 18964-18977.
IEEE DOI Code:
WWW Link.
2411
Task analysis, Frequency-domain analysis, Visualization,
Autonomous vehicles, Benchmark testing, Transformers, Semantics,
autonomous driving
BibRef
Zhong, F.J.[Fu-Jin],
Wu, Y.[Yini],
Yu, H.[Hong],
Wang, G.Y.[Guo-Yin],
Lu, Z.T.[Zhan-Tao],
A benchmark dataset and semantics-guided detection network for
spatial-temporal human actions in urban driving scenes,
PR(158), 2025, pp. 111035.
Elsevier DOI Code:
WWW Link.
2411
Spatial-temporal action detection, Urban driving scenes,
Benchmark dataset, Semantic inference
BibRef
Rashed, H.[Hazem],
Mohamed, E.[Eslam],
Sistu, G.[Ganesh],
Kumar, V.R.[Varun Ravi],
Eising, C.[Ciarán],
El-Sallab, A.[Ahmad],
Yogamani, S.[Senthil],
Generalized Object Detection on Fisheye Cameras for Autonomous
Driving: Dataset, Representations and Baseline,
WACV21(2271-2279)
IEEE DOI
PDF File. Results:
WWW Link.
2106
Measurement, Adaptation models, Image segmentation,
Object detection, Cameras, Sampling methods
BibRef
Li, Z.Q.[Zhi-Qi],
Yu, Z.D.[Zhi-Ding],
Lan, S.Y.[Shi-Yi],
Li, J.H.[Jia-Han],
Kautz, J.[Jan],
Lu, T.[Tong],
Alvarez, J.M.[Jose M.],
Is Ego Status All You Need for Open-Loop End-to-End Autonomous
Driving?,
CVPR24(14864-14873)
IEEE DOI Code:
WWW Link.
2410
Measurement, Roads, Predictive models, Benchmark testing, Planning, Trajectory
BibRef
Tonderski, A.[Adam],
Lindström, C.[Carl],
Hess, G.[Georg],
Ljungbergh, W.[William],
Svensson, L.[Lennart],
Petersson, C.[Christoffer],
NeuRAD: Neural Rendering for Autonomous Driving,
CVPR24(14895-14904)
IEEE DOI
2410
Training, Laser radar, Source coding, Semantics, Training data,
Neural radiance field, Rendering (computer graphics), Autonomous Driving
BibRef
Sachdeva, E.[Enna],
Agarwal, N.[Nakul],
Chundi, S.[Suhas],
Roelofs, S.[Sean],
Li, J.C.[Jia-Chen],
Kochenderfer, M.[Mykel],
Choi, C.[Chiho],
Dariush, B.[Behzad],
Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking
and Reasoning,
WACV24(7498-7507)
IEEE DOI
2404
Visualization, Annotations, Semantics, Natural languages, Closed box,
Benchmark testing, Applications, Autonomous Driving, Algorithms,
Vision + language and/or other modalities
BibRef
Franchi, G.[Gianni],
Hariat, M.[Marwane],
Yu, X.L.[Xuan-Long],
Belkhir, N.[Nacim],
Manzanera, A.[Antoine],
Filliat, D.[David],
InfraParis: A multi-modal and multi-task autonomous driving dataset,
WACV24(2961-2971)
IEEE DOI Code:
WWW Link.
2404
Uncertainty, Computational modeling, Semantic segmentation,
Object detection, Multitasking, Data models, Algorithms,
Autonomous Driving
BibRef
Cui, C.[Can],
Ma, Y.S.[Yun-Sheng],
Cao, X.[Xu],
Ye, W.Q.[Wen-Qian],
Zhou, Y.[Yang],
Liang, K.[Kaizhao],
Chen, J.[Jintai],
Lu, J.[Juanwu],
Yang, Z.[Zichong],
Liao, K.D.[Kuei-Da],
Gao, T.[Tianren],
Li, E.[Erlong],
Tang, K.[Kun],
Cao, Z.P.[Zhi-Peng],
Zhou, T.[Tong],
Liu, A.[Ao],
Yan, X.R.[Xin-Rui],
Mei, S.Q.[Shu-Qi],
Cao, J.G.[Jian-Guo],
Wang, Z.[Ziran],
Zheng, C.[Chao],
A Survey on Multimodal Large Language Models for Autonomous Driving,
LLVMCrive24(958-979)
IEEE DOI
2404
Surveys, Industries, Systematics, Transportation, Benchmark testing
BibRef
Alibeigi, M.[Mina],
Ljungbergh, W.[William],
Tonderski, A.[Adam],
Hess, G.[Georg],
Lilja, A.[Adam],
Lindström, C.[Carl],
Motorniuk, D.[Daria],
Fu, J.S.[Jun-Sheng],
Widahl, J.[Jenny],
Petersson, C.[Christoffer],
Zenseact Open Dataset: A large-scale and diverse multimodal dataset
for autonomous driving,
ICCV23(20121-20131)
IEEE DOI
2401
BibRef
Yatbaz, H.Y.[Hakan Yekta],
Dianati, M.[Mehrdad],
Koufos, K.[Konstantinos],
Woodman, R.[Roger],
Introspection of 2D Object Detection using Processed Neural
Activation Patterns in Automated Driving Systems,
BRAVO23(4049-4056)
IEEE DOI
2401
BibRef
Singh, A.[Apoorv],
Transformer-Based Sensor Fusion for Autonomous Driving: A Survey,
VCL23(3304-3309)
IEEE DOI
2401
BibRef
Wang, X.F.[Xiao-Feng],
Zhu, Z.[Zheng],
Zhang, Y.P.[Yun-Peng],
Huang, G.[Guan],
Ye, Y.[Yun],
Xu, W.B.[Wen-Bo],
Chen, Z.W.[Zi-Wei],
Wang, X.G.[Xin-Gang],
Are We Ready for Vision-Centric Driving Streaming Perception? The
ASAP Benchmark,
CVPR23(9600-9610)
IEEE DOI
2309
BibRef
Marathe, A.[Aboli],
Ramanan, D.[Deva],
Walambe, R.[Rahee],
Kotecha, K.[Ketan],
WEDGE: A multi-weather autonomous driving dataset built from
generative vision-language models,
VDU23(3318-3327)
IEEE DOI
2309
BibRef
Dokania, S.[Shubham],
Hafez, A.H.A.[A. H. Abdul],
Subramanian, A.[Anbumani],
Chandraker, M.[Manmohan],
Jawahar, C.V.,
IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenes,
WACV23(4471-4480)
IEEE DOI
2302
Training, Adaptation models, Laser radar, Roads, Urban areas, Layout,
Applications: Robotics, 3D computer vision,
visual reasoning
BibRef
Li, Y.C.[Yu-Chen],
Li, Z.X.[Zi-Xuan],
Teng, S.Y.[Si-Yu],
Zhang, Y.[Yu],
Zhou, Y.H.[Yu-Hang],
Zhu, Y.C.[Yu-Chang],
Cao, D.[Dongpu],
Tian, B.[Bin],
Ai, Y.F.[Yun-Feng],
Zhe, X.Y.[Xuan-Yuan],
Chen, L.[Long],
AutoMine: An Unmanned Mine Dataset,
CVPR22(21276-21285)
IEEE DOI
2210
Location awareness, Snow, Roads, Sensors, Data mining, Task analysis,
Datasets and evaluation, 3D from multi-view and sensors,
Navigation and autonomous driving
BibRef
Klingner, M.[Marvin],
Müller, K.[Konstantin],
Mirzaie, M.[Mona],
Breitenstein, J.[Jasmin],
Termöhlen, J.A.[Jan-Aike],
Fingscheidt, T.[Tim],
On the Choice of Data for Efficient Training and Validation of
End-to-End Driving Models,
VDU22(4802-4811)
IEEE DOI
2210
Training, Machine learning algorithms, Correlation,
Training data, Machine learning, Data models
BibRef
Bogdoll, D.[Daniel],
Guneshka, S.[Stefani],
Zöllner, J.M.[J. Marius],
One Ontology to Rule Them All:
Corner Case Scenarios for Autonomous Driving,
SafeDrive22(409-425).
Springer DOI
2304
BibRef
Bogdoll, D.[Daniel],
Nitsche, M.[Maximilian],
Zöllner, J.M.[J. Marius],
Anomaly Detection in Autonomous Driving: A Survey,
WAD22(4487-4498)
IEEE DOI
2210
Laser radar, Roads, Radar detection, Benchmark testing, Cameras
BibRef
Cui, Y.M.[Yi-Ming],
Cao, Z.W.[Zhi-Wen],
Xie, Y.X.[Yi-Xin],
Jiang, X.Y.[Xing-Yu],
Tao, F.[Feng],
Chen, Y.J.V.[Ying-Jie Victor],
Li, L.[Lin],
Liu, D.F.[Dong-Fang],
DG-Labeler and DGL-MOTS Dataset:
Boost the Autonomous Driving Perception,
WACV22(3411-3420)
IEEE DOI
2202
Training, Annotations, Pipelines, Transportation,
Training data, Task analysis, Vision for Aerial/Drone/Underwater/Ground Vehicles
BibRef
Brunel, A.[Anthony],
Bourki, A.[Amine],
Strauss, O.[Olivier],
Demonceaux, C.[Cédric],
FLYBO: A Unified Benchmark Environment for Autonomous Flying Robots,
3DV21(1420-1431)
IEEE DOI
2201
Surface reconstruction, Codes, Benchmark testing, Inspection,
Sensors, Complexity theory, Robotic Vision, Datasets, 3D Reconstruction
BibRef
Li, L.[Li],
Ismail, K.N.[Khalid N.],
Shum, H.P.H.[Hubert P. H.],
Breckon, T.P.[Toby P.],
DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic
Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving
Applications,
3DV21(1227-1237)
IEEE DOI
2201
Dataset, Autonomous Driving. Reflectivity, Laser radar, Image resolution, Supervised learning,
Estimation, Benchmark testing, autonomous driving, dataset,
three dimensional
BibRef
Yuan, Y.,
Sester, M.,
Comap: a Synthetic Dataset for Collective Multi-agent Perception of
Autonomous Driving,
ISPRS21(B2-2021: 255-263).
DOI Link
2201
BibRef
Jin, J.C.[Jiong-Chao],
Fatemi, A.[Arezou],
Lira, W.M.P.[Wallace Michel Pinto],
Yu, F.G.[Feng-Gen],
Leng, B.[Biao],
Ma, R.[Rui],
Mahdavi-Amiri, A.[Ali],
Zhang, H.[Hao],
RaidaR: A Rich Annotated Image Dataset of Rainy Street Scenes,
AVVision21(2951-2961)
IEEE DOI
2112
Image segmentation, Rain, Annotations, Roads,
Semantics
BibRef
Xu, Q.[Qi],
Ma, Y.[Yinan],
Wu, J.[Jing],
Long, C.[Chengnian],
Huang, X.L.[Xiao-Lin],
CDAda: A Curriculum Domain Adaptation for Nighttime Semantic
Segmentation,
AVVision21(2962-2971)
IEEE DOI
2112
Training, Adaptation models, Image segmentation,
Computational modeling, Semantics, Training data, Entropy
BibRef
Siam, M.[Mennatullah],
Kendall, A.[Alex],
Jagersand, M.[Martin],
Video Class Agnostic Segmentation Benchmark for Autonomous Driving,
WAD21(2819-2828)
IEEE DOI
2109
Training, Tracking, Motion segmentation, Semantics,
Video sequences, Benchmark testing
BibRef
Swan, R.M.[R. Michael],
Atha, D.[Deegan],
Leopold, H.A.[Henry A.],
Gildner, M.[Matthew],
Oij, S.[Stephanie],
Chiu, C.[Cindy],
Ono, M.[Masahiro],
AI4MARS: A Dataset for Terrain-Aware Autonomous Driving on Mars,
AI4Space21(1982-1991)
IEEE DOI
2109
Training, Space vehicles, Deep learning, Productivity, Mars,
Image segmentation, Semantics
BibRef
Thoduka, S.[Santosh],
Hochgeschwender, N.[Nico],
Benchmarking Robots by Inducing Failures in Competition Scenarios,
DHM21(II:263-276).
Springer DOI
2108
BibRef
Papachristodoulou, A.[Andreas],
Kyrkou, C.[Christos],
Theocharides, T.[Theocharis],
DriveGuard: Robustification of Automated Driving Systems with Deep
Spatio-Temporal Convolutional Autoencoder,
WACVW21(107-116) Autonomous Vehicle Vision
IEEE DOI
2105
Image segmentation, Computational modeling,
Semantics, Computer architecture, Cameras
BibRef
Xu, W.[Weihuang],
Souly, N.[Nasim],
Brahma, P.P.[Pratik Prabhanjan],
Reliability of GAN Generated Data to Train and Validate Perception
Systems for Autonomous Vehicles,
WACVW21(171-180) Autonomous Vehicle Vision
IEEE DOI
2105
Training, Training data, Object detection, Tools,
Generative adversarial networks, Data models
BibRef
Rosano, M.[Marco],
Furnari, A.[Antonino],
Gulino, L.[Luigi],
Farinella, G.M.[Giovanni Maria],
On Embodied Visual Navigation in Real Environments Through Habitat,
ICPR21(9740-9747)
IEEE DOI
2105
Simulators to generat navagiation data.
Deep learning, Visualization, Adaptation models, Actuators,
Navigation, Virtual environments, Reinforcement learning
BibRef
Koilias, A.[Alexandros],
Mousas, C.[Christos],
Rekabdar, B.[Banafsheh],
Anagnostopoulos, C.N.[Christos-Nikolaos],
Passenger Anxiety About Virtual Driver Awareness During a Trip with a
Virtual Autonomous Vehicle,
ISVC20(I:654-665).
Springer DOI
2103
BibRef
Sun, B.,
Sha, H.,
Rafie, M.,
Yang, L.,
CDVA/VCM: Language for Intelligent and Autonomous Vehicles,
ICIP20(3104-3108)
IEEE DOI
2011
Navigation, Transform coding, Standards, Autonomous vehicles,
Natural languages, Feature extraction, Roads, CDVA, VCM, Language,
Autonomous Vehicles
BibRef
Zhang, S.,
Peng, H.,
Nageshrao, S.,
Tseng, H.E.,
Generating Socially Acceptable Perturbations for Efficient Evaluation
of Autonomous Vehicles,
SAIAD20(1341-1347)
IEEE DOI
2008
Perturbation methods, Learning (artificial intelligence),
Training, Machine learning, Games, Mathematical model, Autonomous vehicles
BibRef
Caesar, H.,
Bankiti, V.,
Lang, A.H.,
Vora, S.,
Liong, V.E.,
Xu, Q.,
Krishnan, A.,
Pan, Y.,
Baldan, G.,
Beijbom, O.,
nuScenes: A Multimodal Dataset for Autonomous Driving,
CVPR20(11618-11628)
IEEE DOI
2008
Sensors, Laser radar, Cameras,
Radar tracking, Autonomous vehicles
BibRef
Ettinger, S.[Scott],
Cheng, S.Y.[Shu-Yang],
Caine, B.[Benjamin],
Liu, C.X.[Chen-Xi],
Zhao, H.[Hang],
Pradhan, S.[Sabeek],
Chai, Y.N.[Yu-Ning],
Sapp, B.[Ben],
Qi, C.[Charles],
Zhou, Y.[Yin],
Yang, Z.[Zoey],
Chouard, A.[Aurélien],
Sun, P.[Pei],
Ngiam, J.[Jiquan],
Vasudevan, V.[Vijay],
McCauley, A.[Alexander],
Shlens, J.[Jonathon],
Anguelov, D.[Dragomir],
Large Scale Interactive Motion Forecasting for Autonomous Driving:
The Waymo Open Motion Dataset,
ICCV21(9690-9699)
IEEE DOI
2203
Measurement, Computational modeling, Roads, Urban areas,
Predictive models, Data models, Datasets and evaluation, Motion and tracking
BibRef
Sun, P.,
Kretzschmar, H.,
Dotiwalla, X.,
Chouard, A.,
Patnaik, V.,
Tsui, P.,
Guo, J.,
Zhou, Y.,
Chai, Y.,
Caine, B.,
Vasudevan, V.,
Han, W.,
Ngiam, J.,
Zhao, H.,
Timofeev, A.,
Ettinger, S.,
Krivokon, M.,
Gao, A.,
Joshi, A.,
Zhang, Y.,
Shlens, J.,
Chen, Z.,
Anguelov, D.[Dragomir],
Scalability in Perception for Autonomous Driving: Waymo Open Dataset,
CVPR20(2443-2451)
IEEE DOI
2008
Laser radar, Cameras,
Autonomous vehicles, Radar tracking, Semantics
BibRef
Lakshminarayana, N.,
Large Scale Multimodal Data Capture, Evaluation and Maintenance
Framework for Autonomous Driving Datasets,
AutoNUE19(4302-4309)
IEEE DOI
2004
learning (artificial intelligence), sensor fusion,
traffic engineering computing, open-source framework,
framework
BibRef
Yang, G.R.[Guo-Run],
Song, X.[Xiao],
Huang, C.Q.[Chao-Qin],
Deng, Z.D.[Zhi-Dong],
Shi, J.P.[Jian-Ping],
Zhou, B.[Bolei],
DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous
Driving Scenarios,
CVPR19(899-908).
IEEE DOI
2002
BibRef
Varma, G.,
Subramanian, A.,
Namboodiri, A.,
Chandraker, M.,
Jawahar, C.V.,
IDD: A Dataset for Exploring Problems of Autonomous Navigation in
Unconstrained Environments,
WACV19(1743-1751)
IEEE DOI
1904
image segmentation, learning (artificial intelligence),
mobile robots, path planning, road traffic, robot vision,
Motorcycles
BibRef
Loquercio, A.[Antonio],
Kaufmann, E.[Elia],
Ranflt, R.[Rene],
Mueller, M.[Matthias],
Koltun, V.[Vladlen],
Scaramuzza, D.[Davide],
Learning High-Speed Flight in the Wild,
Science Robotics2021.
WWW Link. Project page:
HTML Version. Code, dataset page:
WWW Link.
Code, Drone Control.
BibRef
2100
Codevilla, F.[Felipe],
López, A.M.[Antonio M.],
Koltun, V.[Vladlen],
Dosovitskiy, A.[Alexey],
On Offline Evaluation of Vision-Based Driving Models,
ECCV18(XV: 246-262).
Springer DOI
1810
BibRef
Geiger, A.[Andreas],
Lenz, P.[Philip],
Urtasun, R.[Raquel],
Are we ready for autonomous driving? The KITTI vision benchmark suite,
CVPR12(3354-3361).
IEEE DOI
1208
BibRef
Leonard, J.J.[John J.],
Challenges for Autonomous Mobile Robots,
IMVIP07(4-4).
IEEE DOI
0709
BibRef
Jolic, M.S.N.,
Modelling the Robotised Multiterminal Port System: RMT-PS,
IVS04(222-225).
IEEE DOI
0411
Analysis of the cargo handling system.
BibRef
Manduchi, R.,
Matthies, L.H.,
Pollara, F.,
From cross-country autonomous navigation to intelligent deep space
communications: visual sensor processing at JPL,
CIAP01(472-477).
IEEE DOI
0210
BibRef
Chapter on Active Vision, Camera Calibration, Mobile Robots, Navigation, Road Following continues in
Driver Assistance Systems and Techniques .