16.7.4.2.10 Pedestrian Safety Issues

Chapter Contents (Back)
Pedestrian Safety. Related to: See also Driver Assistance Systems and Techniques.

Jung, H.G., Kwak, B.M., Shim, J.S., Yoon, P J., Kim, J.,
Precrash Dipping Nose (PCDN) Needs Pedestrian Recognition,
ITS(9), No. 4, December 2008, pp. 678-687.
IEEE DOI 0812
BibRef

Sidla, O.[Oliver],
Improved pedestrian tracking for urban planning,
SPIE(Newsroom), December 17, 2009.
DOI Link 0912
Enhanced image-analysis methods enable new applications for public-transport scheduling, traffic control, and safety monitoring. BibRef

Treuillet, S.[Sylvie], Royer, E.[Eric],
Outdoor/indoor Vision-based Localization For Blind Pedestrian Navigation Assistance,
IJIG(10), No. 4, October 2010, pp. 481-496.
DOI Link 1101
BibRef

Keller, C.G., Dang, T., Fritz, H., Joos, A., Rabe, C., Gavrila, D.M.,
Active Pedestrian Safety by Automatic Braking and Evasive Steering,
ITS(12), No. 4, December 2011, pp. 1292-1304.
IEEE DOI 1112
BibRef

Pedestrians tracked for scheduling, traffic control, and safety,
VisSys(16), No. 2, February 2011.
HTML Version. News item refrencing SLR Engineering work. See also SLR Engineering. BibRef 1102

Zhang, Y., Yao, D., Qiu, T.Z., Peng, L., Zhang, Y.,
Pedestrian Safety Analysis in Mixed Traffic Conditions Using Video Data,
ITS(13), No. 4, December 2012, pp. 1832-1844.
IEEE DOI 1212
BibRef

Kataoka, H.[Hirokatsu], Tamura, K.[Kimimasa], Iwata, K.[Kenji], Satoh, Y.[Yutaka], Matsui, Y.[Yasuhiro], Aoki, Y.[Yoshimitsu],
Extended Feature Descriptor and Vehicle Motion Model with Tracking-by-Detection for Pedestrian Active Safety,
IEICE(E97-D), No. 2, February 2013, pp. 296-304.
WWW Link. 1402
BibRef

Kataoka, H.[Hirokatsu], Hashimoto, K.[Kiyoshi], Iwata, K.[Kenji], Satoh, Y.[Yutaka], Navab, N.[Nassir], Ilic, S.[Slobodan], Aoki, Y.[Yoshimitsu],
Extended Co-occurrence HOG with Dense Trajectories for Fine-Grained Activity Recognition,
ACCV14(V: 336-349).
Springer DOI 1504
BibRef

Borges, P.V.K., Zlot, R., Tews, A.,
Integrating Off-Board Cameras and Vehicle On-Board Localization for Pedestrian Safety,
ITS(14), No. 2, 2013, pp. 720-730.
IEEE DOI 1307
Navigation; Safety; Tracking; Autonomous vehicles; pedestrian detection BibRef

Prioletti, A., Mogelmose, A., Grisleri, P., Trivedi, M.M., Broggi, A., Moeslund, T.B.,
Part-Based Pedestrian Detection and Feature-Based Tracking for Driver Assistance: Real-Time, Robust Algorithms, and Evaluation,
ITS(14), No. 3, 2013, pp. 1346-1359.
IEEE DOI 1309
Advanced driver assistance system (ADAS) BibRef

Zhang, Y., Yao, D., Qiu, T.Z., Peng, L.,
Scene-based pedestrian safety performance model in mixed traffic situation,
IET-ITS(8), No. 3, May 2014, pp. 209-218.
DOI Link 1407
BibRef

Harris, M.,
A cheaper way for robocars to avoid pedestrians,
Spectrum(52), No. 7, July 2015, pp. 16-16.
IEEE DOI 1507
BibRef

Pham, T.Q., Nakagawa, C., Shintani, A., Ito, T.,
Evaluation of the Effects of a Personal Mobility Vehicle on Multiple Pedestrians Using Personal Space,
ITS(16), No. 4, August 2015, pp. 2028-2037.
IEEE DOI 1508
Indexes BibRef

Rosado, A.L.[A. Lˇpez], Chien, S., Li, L., Yi, Q., Chen, Y., Sherony, R.,
Certainty and Critical Speed for Decision Making in Tests of Pedestrian Automatic Emergency Braking Systems,
ITS(18), No. 6, June 2017, pp. 1358-1370.
IEEE DOI 1706
Analytical models, Automobiles, Computer crashes, Decision making, Safety, Vehicle crash testing, Pedestrian protection, active safety margin, critical speed for decision making, prediction, model BibRef

Doric, I., Reitberger, A., Wittmann, S., Harrison, R., Brandmeier, T.,
A Novel Approach for the Test of Active Pedestrian Safety Systems,
ITS(18), No. 5, May 2017, pp. 1299-1312.
IEEE DOI 1705
Accidents, Knee, Legged locomotion, Microscopy, Roads, Safety, Sensor systems, ADAS, pedestrian detection, BibRef

Bhat, A., Aoki, S., Rajkumar, R.,
Tools and Methodologies for Autonomous Driving Systems,
PIEEE(106), No. 9, September 2018, pp. 1700-1716.
IEEE DOI 1810
automobiles, embedded systems, mobile robots, pedestrians, road safety, safety-critical software, verification, software tools BibRef

Strawderman, L.J., Campbell, B.S., May, D.C., Bethel, C.L., Usher, J.M., Carruth, D.W.,
Understanding Human Response to the Presence and Actions of Unmanned Ground Vehicle Systems in Field Environment,
HMS(48), No. 4, August 2018, pp. 325-336.
IEEE DOI 1808
human-robot interaction, mobile robots, pedestrians, remotely operated vehicles, sport, video signal processing, remotely operated vehicles BibRef

Deb, S., Rahman, M.M., Strawderman, L.J., Garrison, T.M.,
Pedestrians' Receptivity Toward Fully Automated Vehicles: Research Review and Roadmap for Future Research,
HMS(48), No. 3, June 2018, pp. 279-290.
IEEE DOI 1805
More the reaction to no driver. Automation, Automobiles, Legged locomotion, Roads, Vehicle crash testing, Autonomous vehicles, virtual reality (VR) BibRef

Flores, C., Merdrignac, P., de Charette, R., Navas, F., MilanÚs, V., Nashashibi, F.,
A Cooperative Car-Following/Emergency Braking System With Prediction-Based Pedestrian Avoidance Capabilities,
ITS(20), No. 5, May 2019, pp. 1837-1846.
IEEE DOI 1905
Laser radar, Global Positioning System, Kalman filters, Image segmentation, Urban areas, Safety, Cooperative systems, collision avoidance system BibRef

Deb, S., Carruth, D.W., Hudson, C.R.,
How Communicating Features can Help Pedestrian Safety in the Presence of Self-Driving Vehicles: Virtual Reality Experiment,
HMS(50), No. 2, April 2020, pp. 176-186.
IEEE DOI 2004
Autonomous vehicles (AVs), communicating features, human-automation interaction, pedestrian safety, virtual reality (VR) BibRef

Goldhammer, M., K÷hler, S., Zernetsch, S., Doll, K., Sick, B., Dietmayer, K.,
Intentions of Vulnerable Road Users: Detection and Forecasting by Means of Machine Learning,
ITS(21), No. 7, July 2020, pp. 3035-3045.
IEEE DOI 2007
Trajectory, Predictive models, Hidden Markov models, Roads, Cameras, Machine learning, Time series analysis, Road safety, artificial neural networks BibRef

Yu, B., Zhu, K., Wu, K., Zhang, M.,
Improved OpenCL-Based Implementation of Social Field Pedestrian Model,
ITS(21), No. 7, July 2020, pp. 2828-2839.
IEEE DOI 2007
Computational modeling, Graphics processing units, Force, Numerical models, Legged locomotion, Computer architecture, pedestrian flow BibRef


Chaabane, M., Trabelsi, A., Blanchard, N., Beveridge, R.,
Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction,
WACV20(2286-2295)
IEEE DOI 2006
Predictive models, Hidden Markov models, Feature extraction, Autonomous vehicles, Decoding BibRef

Gao, D., Wu, Z., Zhang, W.,
Safe-Net: Solid and Abstract Feature Extraction Network for Pedestrian Attribute Recognition,
ICIP19(1655-1659)
IEEE DOI 1910
Pedestrian Attribute, GAN, Image Segmentation BibRef

Suzuki, T., Aoki, Y., Kataoka, H.,
Pedestrian near-miss analysis on vehicle-mounted driving recorders,
MVA17(416-419)
DOI Link 1708
Autonomous vehicles, Benchmark testing, Computer vision, Pattern recognition, Safety, Urban areas, Visualization BibRef

Ke, R., Lutin, J., Spears, J., Wang, Y.,
A Cost-Effective Framework for Automated Vehicle-Pedestrian Near-Miss Detection Through Onboard Monocular Vision,
Traffic17(898-905)
IEEE DOI 1709
Cameras, Feature extraction, Safety, Sensors, Surveillance, Tracking, Videos BibRef

Brazil, G., Yin, X., Liu, X.,
Illuminating Pedestrians via Simultaneous Detection and Segmentation,
ICCV17(4960-4969)
IEEE DOI 1802
computer vision, feature extraction, image segmentation, object detection, pedestrians, road safety, BibRef

Favoreel, W.[Wouter],
Pedestrian sensing for increased traffic safety and efficiency at signalized intersections,
AVSBS11(539-542).
IEEE DOI 1111
AVSS 2011 demo session. BibRef

Grubb, G., Zelinsky, A., Nilsson, L., Rilbe, M.,
3D vision sensing for improved pedestrian safety,
IVS04(19-24).
IEEE DOI 0411
BibRef

Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Human Detection, Tracking, Infrared, IR, Thermal Images .


Last update:Jul 10, 2020 at 16:03:35