17.1.3.2.14 Pedestrian Safety Issues, Pedestrian Behavior

Schlegel, C.[Christian], Illmann, J.[Jörg], Jaberg, H.[Heiko], Schuster, M.[Matthias], Wörz, R.[Robert],
Integrating Vision-Based Behaviors with an Autonomous Robot,
Videre(1), No. 4, Winter 2000, pp. xx-yy. 0005
BibRef
Earlier: CVS99(1 ff.).
Springer DOI 0209
BibRef
Earlier:
Vision Based Person Tracking with a Mobile Robot,
BMVC98(xx-yy). BibRef

Ling, H.[Huang], Wu, J.P.[Jian-Ping],
A study on cyclist behavior at signalized intersections,
ITS(5), No. 4, December 2004, pp. 293-299.
IEEE Abstract. 0501
BibRef

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

Greene, D., Liu, J., Reich, J., Hirokawa, Y., Shinagawa, A., Ito, H., Mikami, T.,
An Efficient Computational Architecture for a Collision Early-Warning System for Vehicles, Pedestrians, and Bicyclists,
ITS(12), No. 4, December 2011, pp. 942-953.
IEEE DOI 1112
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

Xu, Y., Xu, D., Lin, S., Han, T.X., Cao, X., Li, X.,
Detection of Sudden Pedestrian Crossings for Driving Assistance Systems,
SMC-B(42), No. 3, June 2012, pp. 729-739.
IEEE DOI 1202
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

Vazquez, D.[David], Lopez, A.M.[Antonio M.], Marin, J., Ponsa, D.[Daniel], Geronimo, D.,
Virtual and Real World Adaptationfor Pedestrian Detection,
PAMI(36), No. 4, April 2014, pp. 797-809.
IEEE DOI 1404
BibRef
Earlier: A1, A2, A4, Only:
Unsupervised domain adaptation of virtual and real worlds for pedestrian detection,
ICPR12(3492-3495).
WWW Link. 1302
Accuracy BibRef

Geronimo, D.[David], Lopez, A.M.[Antonio M.],
Vision-based Pedestrian Protection Systems for Intelligent Vehicles,

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

Said, Y., Atri, M.,
Efficient and high-performance pedestrian detector implementation for intelligent vehicles,
IET-ITS(10), No. 6, 2016, pp. 438-444.
DOI Link 1608
computer vision BibRef

Li, F.L.[Fu-Liang], Zhang, R.H.[Rong-Hui], You, F.[Feng],
Fast pedestrian detection and dynamic tracking for intelligent vehicles within V2V cooperative environment,
IET-IPR(11), No. 10, October 2017, pp. 833-840.
DOI Link 1710
BibRef

Jeong, M., Ko, B.C., Nam, J.Y.,
Early Detection of Sudden Pedestrian Crossing for Safe Driving During Summer Nights,
CirSysVideo(27), No. 6, June 2017, pp. 1368-1380.
IEEE DOI 1706
Cameras, Feature extraction, Finite impulse response filters, Image color analysis, Roads, Support vector machines, Vehicles, Cascade random forest (CaRF), Keimyung University (KMU) pedestrian data set, far-infrared (FIR) image, sudden pedestrian crossing (SPC), virtual reference line BibRef

Rosado, A.L.[A. López], 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

Tang, S.[Suhua], Obana, S.[Sadao],
Improving performance of pedestrian positioning by using vehicular communication signals,
IET-ITS(12), No. 5, June 2018, pp. 366-374.
DOI Link 1805
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., Milanés, 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

Coscia, P.[Pasquale], Castaldo, F.[Francesco], Palmieri, F.A.N.[Francesco A.N.], Alahi, A.[Alexandre], Savarese, S.[Silvio], Ballan, L.[Lamberto],
Long-term path prediction in urban scenarios using circular distributions,
IVC(69), 2018, pp. 81-91.
Elsevier DOI 1802
Predict near-future for pedestrians, etc. Long-term path prediction, Circular distribution, Human-scene interaction, Stochastic model 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., Köhler, 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, pedestrian flow BibRef

Liberto, C.[Carlo], Nigro, M.[Marialisa], Carrese, S.[Stefano], Mannini, L.[Livia], Valenti, G.[Gaetano], Zarelli, C.[Cristiano],
Simulation framework for pedestrian dynamics: modelling and calibration,
IET-ITS(14), No. 9, September 2020, pp. 1048-1057.
DOI Link 2008
BibRef

Zhou, Z.P.[Zhu-Ping], Peng, Y.L.[Yun-Long], Cai, Y.F.[Yi-Fei],
Vision-based approach for predicting the probability of vehicle-pedestrian collisions at intersections,
IET-ITS(14), No. 11, November 2020, pp. 1447-1455.
DOI Link 2010
BibRef

Elhenawy, M.[Mohammed], Ashqar, H.I.[Huthaifa I.], Masoud, M.[Mahmoud], Almannaa, M.H.[Mohammed H.], Rakotonirainy, A.[Andry], Rakha, H.A.[Hesham A.],
Deep Transfer Learning for Vulnerable Road Users Detection using Smartphone Sensors Data,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Hou, L., Xin, L., Li, S.E., Cheng, B., Wang, W.,
Interactive Trajectory Prediction of Surrounding Road Users for Autonomous Driving Using Structural-LSTM Network,
ITS(21), No. 11, November 2020, pp. 4615-4625.
IEEE DOI 2011
Trajectory, Roads, Predictive models, Prototypes, Computational modeling, Decoding, Autonomous vehicles, LSTM BibRef

Rasouli, A., Tsotsos, J.K.,
Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice,
ITS(21), No. 3, March 2020, pp. 900-918.
IEEE DOI 2003
Survey, Pedestrian Detection. Autonomous vehicles, Roads, Cameras, Automobiles, Observers, pedestrian behavior, traffic interaction, survey BibRef

Gilroy, S.[Shane], Jones, E.[Edward], Glavin, M.[Martin],
Overcoming Occlusion in the Automotive Environment: A Review,
ITS(22), No. 1, January 2021, pp. 23-35.
IEEE DOI 2012
Automotive engineering, Object recognition, Object detection, Roads, Cognition, Task analysis, Automation, Occlusion handling, autonomous vehicles BibRef

Pugh, N.[Nigel], Park, H.[Hyoshin], Derjany, P.[Pierrot], Liu, D.[Dahai], Namilae, S.[Sirish],
Deep adaptive learning for safe and efficient navigation of pedestrian dynamics,
IET-ITS(15), No. 4, 2021, pp. 538-548.
DOI Link 2106
BibRef

Yu, K.P.[Ke-Ping], Lin, L.[Long], Alazab, M.[Mamoun], Tan, L.[Liang], Gu, B.[Bo],
Deep Learning-Based Traffic Safety Solution for a Mixture of Autonomous and Manual Vehicles in a 5G-Enabled Intelligent Transportation System,
ITS(22), No. 7, July 2021, pp. 4337-4347.
IEEE DOI 2107
Vehicles, Hidden Markov models, Safety, Manuals, 5G mobile communication, Real-time systems, Autonomous vehicles, intention recognition BibRef

Camara, F.[Fanta], Bellotto, N.[Nicola], Cosar, S.[Serhan], Nathanael, D.[Dimitris], Althoff, M.[Matthias], Wu, J.Y.[Jing-Yuan], Ruenz, J.[Johannes], Dietrich, A.[André], Fox, C.W.[Charles W.],
Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking,
ITS(22), No. 10, October 2021, pp. 6131-6151.
IEEE DOI 2110
BibRef
And: Erratum: ITS(22), No. 11, November 2021, pp. 7317-7317.
IEEE DOI 2112
Sensors, Cameras, Psychology, Autonomous vehicles, Predictive models, Computational modeling, Review, survey, pedestrians, datasets BibRef

Camara, F.[Fanta], Bellotto, N.[Nicola], Cosar, S.[Serhan], Weber, F.[Florian], Nathanael, D.[Dimitris], Althoff, M.[Matthias], Wu, J.Y.[Jing-Yuan], Ruenz, J.[Johannes], Dietrich, A.[André], Markkula, G.[Gustav], Schieben, A.[Anna], Tango, F.[Fabio], Merat, N.[Natasha], Fox, C.[Charles],
Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior,
ITS(22), No. 9, September 2021, pp. 5453-5472.
IEEE DOI 2109
BibRef
And: Erratum: ITS(22), No. 11, November 2021, pp. 7317-7317.
IEEE DOI 2112
Predictive models, Trajectory, Hidden Markov models, Autonomous vehicles, Psychology, Legged locomotion, datasets BibRef

Ibrahim, M.R.[Mohamed R.], Haworth, J.[James], Christie, N.[Nicola], Cheng, T.[Tao],
CyclingNet: Detecting cycling near misses from video streams in complex urban scenes with deep learning,
IET-ITS(15), No. 10, 2021, pp. 1331-1344.
DOI Link 2109
action recognition, cycling near misses, deep learning, video streams BibRef

Tran, T.T.M.[Tram Thi Minh], Parker, C.[Callum], Tomitsch, M.[Martin],
A Review of Virtual Reality Studies on Autonomous Vehicle-Pedestrian Interaction,
HMS(51), No. 6, December 2021, pp. 641-652.
IEEE DOI 2112
Safety, Autonomous vehicles, Virtual reality, Autonomous vehicles, external human-machine interfaces, pedestrians, virtual reality BibRef

Galinskaite, L.[Lina], Ulevicius, A.[Alius], Valskys, V.[Vaidotas], Samas, A.[Arunas], Busher, P.E.[Peter E.], Ignatavicius, G.[Gytautas],
The Influence of Landscape Structure on Wildlife-Vehicle Collisions: Geostatistical Analysis on Hot Spot and Habitat Proximity Relations,
IJGI(11), No. 1, 2022, pp. xx-yy.
DOI Link 2201
Animals, not people. BibRef

Zhao, S.Z.[Sheng-Zhe], Li, H.P.[Hao-Peng], Ke, Q.H.[Qiu-Hong], Liu, L.C.[Liang-Chen], Zhang, R.[Rui],
Action-ViT: Pedestrian Intent Prediction in Traffic Scenes,
SPLetters(29), 2022, pp. 324-328.
IEEE DOI 2202
Transformers, Predictive models, Feature extraction, Roads, Task analysis, Trajectory, Legged locomotion, Intention prediction, temporal model BibRef

Zhang, S.L.[Shi-Le], Abdel-Aty, M.[Mohamed], Wu, Y.[Yina], Zheng, O.[Ou],
Pedestrian Crossing Intention Prediction at Red-Light Using Pose Estimation,
ITS(23), No. 3, March 2022, pp. 2331-2339.
IEEE DOI 2203
Pose estimation, Videos, Trajectory, Legged locomotion, Vehicles, Support vector machines, Safety, Pedestrian crossing intention, artificial intelligence (AI) BibRef

Iwata, T.[Tomoharu], Shimizu, H.[Hitoshi], Marumo, N.[Naoki],
Probabilistic Pedestrian Models for Estimating Unobserved Road Populations,
ITS(23), No. 4, April 2022, pp. 3037-3047.
IEEE DOI 2204
Roads, Sociology, Statistics, Probabilistic logic, Tomography, Gaussian distribution, Task analysis, Pedestrian modeling, traffic simulator BibRef

Feng, J.[Jian], Wang, C.Y.[Chun-Yan], Xu, C.[Can], Kuang, D.[Dengming], Zhao, W.[Wanzhong],
Active Collision Avoidance Strategy Considering Motion Uncertainty of the pedestrian,
ITS(23), No. 4, April 2022, pp. 3543-3555.
IEEE DOI 2204
Collision avoidance, Planning, Safety, Trajectory, Uncertainty, Acceleration, Injuries, Active collision avoidance, multi-objective evaluation BibRef

Domeyer, J.E.[Joshua E.], Lee, J.D.[John D.], Toyoda, H.[Heishiro], Mehler, B.[Bruce], Reimer, B.[Bryan],
Interdependence in Vehicle-Pedestrian Encounters and its Implications for Vehicle Automation,
ITS(23), No. 5, May 2022, pp. 4122-4134.
IEEE DOI 2205
Roads, Automation, Safety, Vehicles, Measurement, Accidents, Instruments, Human factors, automation, autonomous vehicles, pedestrian BibRef

Zhou, S.Y.[Si-Yuan], Sun, X.[Xu], Liu, B.J.[Bing-Jian], Burnett, G.[Gary],
Factors Affecting Pedestrians' Trust in Automated Vehicles: Literature Review and Theoretical Model,
HMS(52), No. 3, June 2022, pp. 490-500.
IEEE DOI 2205
Artificial intelligence, Roads, Automation, Uncertainty, Libraries, Environmental factors, Bibliographies, trust BibRef

Yang, B.[Biao], Zhan, W.Q.[Wei-Qin], Wang, P.[Pin], Chan, C.Y.[Ching-Yao], Cai, Y.F.[Ying-Feng], Wang, N.[Nan],
Crossing or Not? Context-Based Recognition of Pedestrian Crossing Intention in the Urban Environment,
ITS(23), No. 6, June 2022, pp. 5338-5349.
IEEE DOI 2206
Trajectory, Feature extraction, Skeleton, Autonomous automobiles, Automobiles, Spatiotemporal phenomena, Crossing intention, self-driving car BibRef

Held, P.[Patrick], Steinhauser, D.[Dagmar], Koch, A.[Andreas], Brandmeier, T.[Thomas], Schwarz, U.T.[Ulrich T.],
A Novel Approach for Model-Based Pedestrian Tracking Using Automotive Radar,
ITS(23), No. 7, July 2022, pp. 7082-7095.
IEEE DOI 2207
Legged locomotion, Radar, Radar tracking, Kinematics, Feature extraction, Radar cross-sections, Foot, data association BibRef

Yin, N.[Nan], Singh, A.K.[Amit Kumar], Lv, H.B.[Hai-Bin],
Personalized Situation Adaptive Human-Vehicles-Interaction (HVI) Prediction in COVID-19 Context,
ITS(23), No. 7, July 2022, pp. 9809-9818.
IEEE DOI 2207
Transportation, Context-aware services, Adaptation models, COVID-19, Real-time systems, Mathematical models, DC-LSTM BibRef

Papini, G.P.R.[Gastone Pietro Rosati], Plebe, A.[Alice], da Lio, M.[Mauro], Donà, R.[Riccardo],
A Reinforcement Learning Approach for Enacting Cautious Behaviours in Autonomous Driving System: Safe Speed Choice in the Interaction With Distracted Pedestrians,
ITS(23), No. 7, July 2022, pp. 8805-8822.
IEEE DOI 2207
Vehicles, Roads, Reinforcement learning, Trajectory, Neural networks, Autonomous vehicles, Training, Vulnerable road users, intelligent speed adaptation BibRef

Shen, X.[Xun], Raksincharoensak, P.[Pongsathorn],
Pedestrian-Aware Statistical Risk Assessment,
ITS(23), No. 7, July 2022, pp. 7910-7918.
IEEE DOI 2207
Modeling, Measurement, Predictive models, Risk management, Logistics, Analytical models, Earthquakes, Near-accident event, logistic regression BibRef

Li, Y.H.[You-Huizi], Yin, Y.[Yuyu], Chen, X.[Xu], Wan, J.[Jian], Jia, G.Y.[Gang-Yong], Sha, K.W.[Ke-Wei],
A Secure Dynamic Mix Zone Pseudonym Changing Scheme Based on Traffic Context Prediction,
ITS(23), No. 7, July 2022, pp. 9492-9505.
IEEE DOI 2207
Safety, Trajectory, Roads, Vehicle dynamics, Real-time systems, Accidents, Urban areas, Trajectory privacy, security, traffic prediction BibRef

Liu, Y.[Yishu], Zhang, Q.[Qi], Lv, Z.H.[Zhi-Han],
Real-Time Intelligent Automatic Transportation Safety Based on Big Data Management,
ITS(23), No. 7, July 2022, pp. 9702-9711.
IEEE DOI 2207
Transportation, Big Data, Safety, Real-time systems, Prediction algorithms, Sparks, Roads, Big data analytics, DBN BibRef

Manikandan, N.S., Kaliyaperumal, G.[Ganesan],
Collision avoidance approaches for autonomous mobile robots to tackle the problem of pedestrians roaming on campus road,
PRL(160), 2022, pp. 112-121.
Elsevier DOI 2208
BibRef

Malik, F.A.[Faheem Ahmed], Dala, L.[Laurent], Busawon, K.[Krishna],
Intelligent Nanoscopic Cyclist Crash Modelling for Variable Environmental Conditions,
ITS(23), No. 8, August 2022, pp. 11178-11189.
IEEE DOI 2208
Accidents, Safety, Computer crashes, Road safety, Predictive models, Lighting, Mathematical model, Intelligent transportation system, environmental conditions BibRef

Hara, K.[Kensho], Kataoka, H.[Hirokatsu], Inaba, M.[Masaki], Narioka, K.[Kenichi], Hotta, R.[Ryusuke], Satoh, Y.[Yutaka],
Predicting Appearance of Vehicles From Blind Spots Based on Pedestrian Behaviors at Crossroads,
ITS(23), No. 8, August 2022, pp. 11917-11929.
IEEE DOI 2208
Videos, Spatiotemporal phenomena, Semantics, Cameras, Accidents, Vehicles, Deep learning, future prediction, action recognition, spatiotemporal 3D convolution BibRef

Yang, B.[Bo], Ning, J.[Jieqing], Kaizuka, T.[Tsutomu], Nishihira, M.[Munetaka], Nakano, K.[Kimihiko],
Effects of Exterior Lighting System of Parked Vehicles on the Behaviors of Cyclists,
ITS(23), No. 8, August 2022, pp. 12451-12463.
IEEE DOI 2208
Roads, Lighting, Accidents, Animation, Vehicles, Licenses, Safety, Cyclist, exterior lighting system, parked vehicle BibRef

Herman, M.[Michael], Wagner, J.[Jörg], Prabhakaran, V.[Vishnu], Möser, N.[Nicolas], Ziesche, H.[Hanna], Ahmed, W.[Waleed], Bürkle, L.[Lutz], Kloppenburg, E.[Ernst], Gläser, C.[Claudius],
Pedestrian Behavior Prediction for Automated Driving: Requirements, Metrics, and Relevant Features,
ITS(23), No. 9, September 2022, pp. 14922-14937.
IEEE DOI 2209
Measurement, Trajectory, Predictive models, Task analysis, Probabilistic logic, Vehicles, Mathematical models, machine learning BibRef

Liu, Y.C.[Yen-Chen], Jafari, A.[Alireza], Shim, J.K.[Jae Kun], Paley, D.A.[Derek A.],
Dynamic Modeling and Simulation of Electric Scooter Interactions With a Pedestrian Crowd Using a Social Force Model,
ITS(23), No. 9, September 2022, pp. 16448-16461.
IEEE DOI 2209
Force, Motorcycles, Numerical models, Vehicle dynamics, Dynamics, Automobiles, Mathematical models, Electric scooter, Monte Carlo simulations BibRef

Zang, G.Q.[Guo-Qin], Azouigui, S.[Shéhérazade], Saudrais, S.[Sébastien], Hébert, M.[Mathieu], Gonçalves, W.[Whilk],
Evaluating the Understandability of Light Patterns and Pictograms for Autonomous Vehicle-to-Pedestrian Communication Functions,
ITS(23), No. 10, October 2022, pp. 18668-18680.
IEEE DOI 2210
Roads, Color, Symbols, Autonomous vehicles, Monitoring, Automation, Autonomous vehicle, signal design BibRef

Zhang, X.C.[Xing-Chen], Angeloudis, P.[Panagiotis], Demiris, Y.F.[Yi-Fannis],
ST CrossingPose: A Spatial-Temporal Graph Convolutional Network for Skeleton-Based Pedestrian Crossing Intention Prediction,
ITS(23), No. 11, November 2022, pp. 20773-20782.
IEEE DOI 2212
Skeleton, Feature extraction, Safety, Convolution, Performance evaluation, Data mining, Benchmark testing, intelligent vehicle BibRef

Zhou, W.X.[Wei-Xuan], Wang, X.S.[Xue-Song],
Calibrating and Comparing Autonomous Braking Systems in Motorized-to-Non-Motorized-Vehicle Conflict Scenarios,
ITS(23), No. 11, November 2022, pp. 20636-20651.
IEEE DOI 2212
Accidents, Safety, Brakes, Vehicles, Bicycles, Automobiles, Data mining, Automatic preventive braking, autonomous emergency braking, safety-critical event BibRef

Wu, W.S.[Wan-Shu], Guo, J.H.[Jin-Han], Ma, Z.Y.[Zi-Ying], Zhao, K.[Kai],
Data-Driven Approach to Assess Street Safety: Large-Scale Analysis of the Microscopic Design,
IJGI(11), No. 11, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Wang, Y.K.[Yuan-Kai], Qiu, W.S.[Wai-Shan], Jiang, Q.R.[Qing-Rui], Li, W.J.[Wen-Jing], Ji, T.[Tong], Dong, L.[Lin],
Drivers or Pedestrians, Whose Dynamic Perceptions Are More Effective to Explain Street Vitality? A Case Study in Guangzhou,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
BibRef

Ye, Q.M.[Qi-Ming], Feng, Y.X.[Yu-Xiang], Macias, J.J.E.[Jose Javier Escribano], Stettler, M.[Marc], Angeloudis, P.[Panagiotis],
Adaptive Road Configurations for Improved Autonomous Vehicle-Pedestrian Interactions Using Reinforcement Learning,
ITS(24), No. 2, February 2023, pp. 2024-2034.
IEEE DOI 2302
Roads, Aerospace electronics, Optimization, Costs, Space exploration, Microscopy, Heuristic algorithms, Autonomous vehicles, pedestrians, infrastructure management BibRef

Chen, K.[Kai], Zhu, H.H.[Hai-Hua], Tang, D.[Dunbing], Zheng, K.[Kun],
Future pedestrian location prediction in first-person videos for autonomous vehicles and social robots,
IVC(134), 2023, pp. 104671.
Elsevier DOI 2305
Social intention, Human-vehicle interactions, First-person videos, Image depth, Social spatial dependencies, Transformer BibRef

Liu, W.[Wen], Shao, Y.X.[Yi-Xiao], Zhai, S.H.[Shi-Hong], Yang, Z.[Zhao], Chen, P.S.[Pei-Shuai],
Computer Vision-Based Tracking of Workers in Construction Sites Based on MDNet,
IEICE(E106-D), No. 5, May 2023, pp. 653-661.
WWW Link. 2305
BibRef

Huang, R.[Rong], Zhao, X.[Xuan], Yuan, Y.F.[Yu-Fei], Yu, Q.[Qiang], Liu, C.Q.[Cheng-Qing], Daamen, W.[Winnie],
Modeling Pedestrian Tactical and Operational Decisions Under Risk and Uncertainty: A Two-Layer Model Framework,
ITS(24), No. 5, May 2023, pp. 5259-5281.
IEEE DOI 2305
Decision making, Uncertainty, Computational modeling, Mathematical models, Numerical models, Sensitivity analysis, cellular automaton BibRef

Zhang, X.C.[Xing-Chen], Angeloudis, P.[Panagiotis], Demiris, Y.F.[Yi-Fannis],
Dual-branch spatio-temporal graph neural networks for pedestrian trajectory prediction,
PR(142), 2023, pp. 109633.
Elsevier DOI 2307
Pedestrian trajectory prediction, Social interactions, Graph convolutional networks, Graph attention networks, Spatio-temporal graph BibRef

Zhai, X.L.[Xiao-Lin], Hu, Z.X.[Zheng-Xi], Yang, D.Y.[Ding-Ye], Zhou, L.[Lei], Liu, J.[Jingtai],
Social Aware Multi-modal Pedestrian Crossing Behavior Prediction,
ACCV22(IV:275-290).
Springer DOI 2307
BibRef

Wang, Y.N.[Yu-Ning], Huang, H.[Heye], Zhang, B.[Bo], Wang, J.Q.[Jian-Qiang],
A differentiated decision-making algorithm for automated vehicles based on pedestrian feature estimation,
IET-ITS(17), No. 7, 2023, pp. 1454-1466.
DOI Link 2307
automated vehicles, feature estimation, decision-making, pedestrian, interaction BibRef

Lin, M.C.[Ming-Chih], Lin, Y.C.[Yu-Chen], Hung, M.K.[Ming-Ku],
Pedestrian potentially dangerous behaviour prediction based on attention-long-short-term memory with egocentric vision,
IET-ITS(17), No. 7, 2023, pp. 1331-1343.
DOI Link 2307
advanced driver assistance systems, artificial intelligence, image recognition, perception BibRef

Zhang, C.[Chi], Berger, C.[Christian],
Pedestrian Behavior Prediction Using Deep Learning Methods for Urban Scenarios: A Review,
ITS(24), No. 10, October 2023, pp. 10279-10301.
IEEE DOI 2310
BibRef

Melotti, G.[Gledson], Lu, W.H.[Wei-Hao], Conde, P.[Pedro], Zhao, D.[Dezong], Asvadi, A.[Alireza], Gonçalves, N.[Nuno], Premebida, C.[Cristiano],
Probabilistic Approach for Road-Users Detection,
ITS(24), No. 9, September 2023, pp. 9253-9267.
IEEE DOI 2310
BibRef

Manikandan, N.S., Ganesan, K.,
Energy-aware vehicle/pedestrian detection and close movement alert at nighttime in dense slow traffic on Indian urban roads using a depth camera,
IJCVR(13), No. 6, 2023, pp. 658-676.
DOI Link 2310
BibRef

Li, P.[Pei], Guo, H.[Huizhong], Bao, S.[Shan], Kusari, A.[Arpan],
A Probabilistic Framework for Estimating the Risk of Pedestrian-Vehicle Conflicts at Intersections,
ITS(24), No. 12, December 2023, pp. 14111-14120.
IEEE DOI 2312
BibRef

Veluchamy, S., Mahesh, K.M.[K. Michael], Muthukrishnan, R., Karthi, S.,
HY-LSTM: A new time series deep learning architecture for estimation of pedestrian time to cross in advanced driver assistance system,
JVCIR(97), 2023, pp. 103982.
Elsevier DOI 2312
Advanced driver assistance system, Pedestrian time, LSTM, OCNN, Deep joint segmentation BibRef

Zhou, W.[Wei], Liu, Y.Q.[Yu-Qing], Zhao, L.[Lei], Xu, S.[Sixuan], Wang, C.[Chen],
Pedestrian Crossing Intention Prediction From Surveillance Videos for Over-the-Horizon Safety Warning,
ITS(25), No. 2, February 2024, pp. 1394-1407.
IEEE DOI 2402
Pedestrians, Surveillance, Trajectory, Cameras, Feature extraction, Safety, Predictive models, Traffic safety, environment graph BibRef

Wang, M.X.[Ming-Xi], Li, L.L.[Lei-Lei], Liu, J.B.[Jing-Bin], Chen, R.Z.[Rui-Zhi],
Neural Network Aided Factor Graph Optimization for Collaborative Pedestrian Navigation,
ITS(25), No. 1, January 2024, pp. 303-314.
IEEE DOI 2402
Pedestrians, Navigation, Collaboration, Sensors, Global navigation satellite system, Artificial neural networks, factor graph optimization BibRef

Tian, K.[Kai], Markkula, G.[Gustav], Wei, C.[Chongfeng], Lee, Y.M.[Yee Mun], Madigan, R.[Ruth], Hirose, T.[Toshiya], Merat, N.[Natasha], Romano, R.[Richard],
Deconstructing Pedestrian Crossing Decisions in Interactions With Continuous Traffic: An Anthropomorphic Model,
ITS(25), No. 3, March 2024, pp. 2466-2478.
IEEE DOI 2405
Pedestrians, Roads, Computational modeling, Visualization, Decision making, Visual perception, Psychology, traffic flow BibRef

Zhang, Z.Y.[Zhen-Yuan], Lai, H.Z.[Hui-Zhen], Huang, D.[Darong], Fang, X.[Xin], Zhou, M.[Mu], Zhang, Y.[Ying],
RETA: 4D Radar-Based End-to-End Joint Tracking and Activity Estimation for Low-Observable Pedestrian Safety in Cluttered Traffic Scenarios,
ITS(25), No. 5, May 2024, pp. 4413-4426.
IEEE DOI 2405
Radar tracking, Pedestrians, Radar, Radar cross-sections, Feature extraction, Target tracking, Activity recognition, FMCW radar BibRef

Wu, J.Y.[Jing-Yuan], Ruenz, J.[Johannes], Berkemeyer, H.[Hendrik], Dixon, L.[Liza], Althoff, M.[Matthias],
Goal-Oriented Pedestrian Motion Prediction,
ITS(25), No. 6, June 2024, pp. 5282-5298.
IEEE DOI 2406
Pedestrians, Trajectory, Vehicle dynamics, Planning, Behavioral sciences, Markov processes, Mathematical models, pedestrian motion prediction BibRef

Li, S.W.[Shi-Wei], Li, Q.Q.[Qian-Qian], Xu, J.[Jiao], Zhang, Y.Z.[Yu-Zhao],
Simulation of cross-pedestrian flow in intersection based on direction fuzzy visual field,
IET-ITS(18), No. 6, 2024, pp. 1045-1067.
DOI Link 2406
cellular automata, fuzzy systems, pedestrians, simulation BibRef

Kitchat, K.[Kotcharat], Chiu, Y.L.[Yi-Lun], Lin, Y.C.[Yu-Chiu], Sun, M.T.[Min-Te], Wada, T.[Tomotaka], Sakai, K.[Kazuya], Ku, W.S.[Wei-Shinn], Wu, S.C.[Shiaw-Chian], Jeng, A.A.K.[Andy An-Kai], Liu, C.H.[Ching-Hao],
PedCross: Pedestrian Crossing Prediction for Auto-Driving Bus,
ITS(25), No. 8, August 2024, pp. 8730-8740.
IEEE DOI 2408
Pedestrians, Skeleton, Predictive models, Long short term memory, Hidden Markov models, YOLO, Trajectory, pose detection BibRef

Chen, X.B.[Xiao-Bo], Zhang, S.L.[Shi-Lin], Li, J.[Jun], Yang, J.[Jian],
Pedestrian Crossing Intention Prediction Based on Cross-Modal Transformer and Uncertainty-Aware Multi-Task Learning for Autonomous Driving,
ITS(25), No. 9, September 2024, pp. 12538-12549.
IEEE DOI Code:
WWW Link. 2409
Pedestrians, Predictive models, Transformers, Task analysis, Multitasking, Feature extraction, Data models, homoscedastic uncertainty BibRef

Luo, Y.[Yan], Zhao, M.[Muming], Sun, J.[Jun], Zhai, G.T.[Guang-Tao], Zhang, C.Y.[Chong-Yang],
Consistent GT-Proposal Assignment for Challenging Pedestrian Detection,
MultMed(26), 2024, pp. 9398-9409.
IEEE DOI 2410
Proposals, Pedestrians, Location awareness, Detectors, Training, Measurement, Pedestrian detection, label assignment, depth distribution BibRef

Papathanasopoulou, V.[Vasileia], Perakis, H.[Harris], Spyropoulou, I.[Ioanna], Gikas, V.[Vassilis],
Pedestrian Simulation Challenges: Modeling Techniques and Emerging Positioning Technologies for ITS Applications,
ITS(25), No. 10, October 2024, pp. 12876-12892.
IEEE DOI 2410
Pedestrians, Data models, Computational modeling, Predictive models, Force, Mathematical models, Pedestrian behavior, simulation performance BibRef

Park, S.[Sungjune], Kim, H.[Hyunjun], Ro, Y.M.[Yong Man],
Integrating Language-Derived Appearance Elements With Visual Cues in Pedestrian Detection,
CirSysVideo(34), No. 9, September 2024, pp. 7975-7985.
IEEE DOI 2410
Pedestrians, Visualization, Task analysis, Detectors, Surveillance, Safety, Image classification, Pedestrian detection, language-derived appearance element BibRef

Han, Y.J.[Young-Jun], Lee, H.[Hasik],
Impact of Smartphone Activity on Pedestrian Safety: A Case Study in Seoul,
ITS(25), No. 10, October 2024, pp. 13194-13203.
IEEE DOI 2410
Pedestrians, Accidents, Safety, Legged locomotion, Transportation, Surveys, Solid modeling, Pedestrian accident, smartphone data, multiple regression BibRef

Li, H.J.[Hao-Jie], Jin, Y.[Yuan], Ren, G.[Gang],
Interpretable Prediction of Pedestrian Crossing Intention: Fusion of Human Skeletal Information in Natural Driving Scenarios,
ITS(25), No. 11, November 2024, pp. 18153-18170.
IEEE DOI 2411
Pedestrians, Feature extraction, Accuracy, Predictive models, Skeleton, Vehicle dynamics, Prediction algorithms, machine learning BibRef

Dai, S.Y.[Si-Yang], Liu, J.[Jun], Cheung, N.M.[Ngai-Man],
Uncertainty-Aware Pedestrian Crossing Prediction via Reinforcement Learning,
CirSysVideo(34), No. 10, October 2024, pp. 9540-9549.
IEEE DOI 2411
Uncertainty, Pedestrians, Predictive models, Reinforcement learning, Labeling, Roads, Trajectory, uncertainty estimation BibRef

Perrusquía, A.[Adolfo], Wei, Z.[Zhuangkun], Guo, W.S.[Wei-Si],
Trajectory Intent Prediction of Autonomous Systems Using Dynamic Mode Decomposition,
SMCS(54), No. 12, December 2024, pp. 7897-7908.
IEEE DOI 2411
Trajectory, Autonomous systems, Predictive models, Prediction algorithms, Heuristic algorithms, Data models, trajectory intent prediction BibRef

Song, W.F.[Wen-Feng], Jin, X.[Xingliang], Ding, Y.[Yang], Gao, Y.[Yang], Hou, X.[Xia],
Dual Temporal Transformers for Fine-Grained Dangerous Action Recognition,
ICIP23(415-419)
IEEE DOI Code:
WWW Link. 2312
BibRef

Ham, J.S.[Je-Seok], Kim, D.H.[Dae Hoe], Jung, N.K.[Nam-Kyo], Moon, J.[Jinyoung],
CIPF: Crossing Intention Prediction Network based on Feature Fusion Modules for Improving Pedestrian Safety,
Precognition23(3666-3675)
IEEE DOI 2309
BibRef

Ham, J.S.[Je-Seok], Bae, K.[Kangmin], Moon, J.[Jinyoung],
MCIP: Multi-stream Network for Pedestrian Crossing Intention Prediction,
AVVision22(663-679).
Springer DOI 2304
BibRef

Gesnouin, J.[Joseph], Pechberti, S.[Steve], Stanciulcscu, B.[Bogdan], Moutarde, F.[Fabien],
TrouSPI-Net: Spatio-temporal attention on parallel atrous convolutions and U-GRUs for skeletal pedestrian crossing prediction,
FG21(01-07)
IEEE DOI 2303
Face recognition, Neural networks, Predictive models, Parallel processing, Feature extraction, Skeleton, Safety BibRef

Vozniak, I.[Igor], Müller, P.[Philipp], Hell, L.[Lorena], Lipp, N.[Nils], Abouelazm, A.[Ahmed], Müller, C.[Christian],
Context-empowered Visual Attention Prediction in Pedestrian Scenarios,
WACV23(950-960)
IEEE DOI 2302
Visualization, Uncertainty, Navigation, Training data, Predictive models, Safety, Behavioral sciences, Vision + language and/or other modalities BibRef

Zhou, C.[Chen], AlRegib, G.[Ghassan], Parchami, A.[Armin], Singh, K.[Kunjan],
Learning Trajectory-Conditioned Relations to Predict Pedestrian Crossing Behavior,
ICIP22(4088-4092)
IEEE DOI 2211
Feature extraction, Trajectory, Behavioral sciences, Smart transportation, Intelligent systems, Intent Prediction, Pedestrian Crossing BibRef

Osman, N.[Nada], Cancelli, E.[Enrico], Camporese, G.[Guglielmo], Coscia, P.[Pasquale], Ballan, L.[Lamberto],
Early Pedestrian Intent Prediction via Features Estimation,
ICIP22(3446-3450)
IEEE DOI 2211
Measurement, Visualization, Protocols, Estimation, Predictive models, Safety, Pedestrian Intent Prediction, Action Anticipation, LSTM BibRef

Mangalam, K.[Karttikeya], An, Y.[Yang], Girase, H.[Harshayu], Malik, J.[Jitendra],
From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting,
ICCV21(15213-15222)
IEEE DOI 2203
Heating systems, Uncertainty, Computational modeling, Benchmark testing, Trajectory, Forecasting, Vision applications and systems BibRef

Rasouli, A.[Amir], Rohani, M.[Mohsen], Luo, J.[Jun],
Bifold and Semantic Reasoning for Pedestrian Behavior Prediction,
ICCV21(15580-15590)
IEEE DOI 2203
Semantics, Gesture recognition, Predictive models, Benchmark testing, Cognition, Encoding, Scene analysis and understanding BibRef

Borgmann, B., Hebel, M., Arens, M., Stilla, U.,
Information Acquisition on Pedestrian Movements In Urban Traffic with A Mobile Multi-sensor System,
ISPRS21(B2-2021: 131-138).
DOI Link 2201
BibRef

Chen, T.[Tina], Tian, R.R.[Ren-Ran], Ding, Z.M.[Zheng-Ming],
Visual Reasoning using Graph Convolutional Networks for Predicting Pedestrian Crossing Intention,
AVVision21(3096-3102)
IEEE DOI 2112
Convolutional codes, Visualization, Roads, Pose estimation, Predictive models, Feature extraction, Cognition BibRef

Singh, A.[Ankur], Suddamalla, U.[Upendra],
Multi-Input Fusion for Practical Pedestrian Intention Prediction,
SoMoF21(2304-2311)
IEEE DOI 2112
Visualization, Navigation, Convolution, Roads, Video sequences BibRef

Bhattacharyya, A.[Apratim], Reino, D.O.[Daniel Olmeda], Fritz, M.[Mario], Schiele, B.[Bernt],
Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers,
CVPR21(6404-6413)
IEEE DOI 2111
Computational modeling, Urban areas, Predictive models, Turning, Trajectory BibRef

Feifel, P.[Patrick], Bonarens, F.[Frank], Köster, F.[Frank],
Reevaluating the Safety Impact of Inherent Interpretability on Deep Neural Networks for Pedestrian Detection,
SAIAD21(29-37)
IEEE DOI 2109
Deep learning, Measurement, Pipelines, Prototypes, Semisupervised learning, Cognition, Software BibRef

Lyssenko, M.[Maria], Gladisch, C.[Christoph], Heinzemann, C.[Christian], Woehrle, M.[Matthias], Triebel, R.[Rudolph],
From Evaluation to Verification: Towards Task-oriented Relevance Metrics for Pedestrian Detection in Safety-critical Domains,
SAIAD21(38-45)
IEEE DOI 2109
Measurement, Meters, System performance, Proposals, Motion measurement BibRef

Shimizu, T.[Takahiro], Koide, K.[Kenji], Oishi, S.J.[Shun-Ji], Yokozuka, M.[Masashi], Banno, A.[Atsuhiko], Shino, M.[Motoki],
Sensor-independent Pedestrian Detection for Personal Mobility Vehicles in Walking Space Using Dataset Generated by Simulation,
ICPR21(1788-1795)
IEEE DOI 2105
Legged locomotion, Space vehicles, Training, Solid modeling, Laser radar, Wheelchairs BibRef

John, V.[Vijay], Boyali, A.[Ali], Thompson, S.[Simon], Lakshmanan, A.[Annamalai], Mita, S.[Seiichi],
Visible and Thermal Camera-based Jaywalking Estimation Using a Hierarchical Deep Learning Framework,
MMHUA20(123-135).
Springer DOI 2103
BibRef

Ujjwal, U., Dziri, A., Leroy, B., Bremond, F.,
A One-and-Half Stage Pedestrian Detector,
WACV20(765-774)
IEEE DOI 2006
Detectors, Proposals, Feature extraction, Semantics, Training, Complexity theory, Autonomous vehicles BibRef

Kress, V.[Viktor], Zernetsch, S.[Stefan], Doll, K.[Konrad], Sick, B.[Bernhard],
Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks,
3DHU20(57-71).
Springer DOI 2103
BibRef

Yuan, J., Wu, X., Yuan, S.,
A Rapid Recognition Method for Pedestrian Abnormal Behavior,
CVIDL20(241-245)
IEEE DOI 2102
convolutional neural nets, feature extraction, image fusion, image recognition, image sequences, multi-scale information BibRef

Sanjeewani, P., Verma, B.,
An optimisation Technique for the Detection of Safety Attributes Using Roadside Video Data,
IVCNZ20(1-6)
IEEE DOI 2012
Industries, Evolutionary computation, Road safety, Safety, Classification algorithms, Convolutional neural networks, road safety BibRef

Fernando, T., Denman, S., Sridharan, S., Fookes, C.,
Neighbourhood Context Embeddings in Deep Inverse Reinforcement Learning for Predicting Pedestrian Motion Over Long Time Horizons,
HBU19(1179-1187)
IEEE DOI 2004
behavioural sciences computing, entropy, feature extraction, image motion analysis, pedestrians, LSTM 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

Dhaka, D., Ishii, M., Sato, A.,
Latent Linear Dynamics for Modeling Pedestrian Behaviors,
ICPR18(1592-1597)
IEEE DOI 1812
Trajectory, Data models, Clustering algorithms, Inference algorithms, Dynamics, Heuristic algorithms, Kalman filters BibRef

Yatim, H.S.M.[Halimatul Saadiah M.], Talib, A.Z.[Abdullah Zawawi], Haron, F.[Fazilah],
An Automated Image-Based Approach for Tracking Pedestrian Movements from Top-View Video,
IVIC17(279-289).
Springer DOI 1711
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, 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
feature extraction, image segmentation, object detection, pedestrians, road safety, BibRef

Cancela, B., Iglesias, A., Ortega, M., Penedo, M.G.,
Unsupervised Trajectory Modelling Using Temporal Information via Minimal Paths,
CVPR14(2553-2560)
IEEE DOI 1409
geodesic active contours; pedestrian behavior; trajectory analysis BibRef

Nakanishi, W., Fuse, T., Ishikawa, T.,
Adaptive Parameter Estimation of Person Recognition Model in a Stochastic Human Tracking Process,
Seamless15(49-53).
DOI Link 1508
BibRef

Nakanishi, W., Fuse, T.,
Sensitive Analysis of Observation Model for Human Tracking Using a Stochastic Process,
CloseRange14(445-450).
DOI Link 1411
BibRef
Earlier:
Multiple Human Tracking In Complex Situation By Data Assimilation With Pedestrian Behavior Model,
ISPRS12(XXXIX-B3:409-414).
DOI Link 1209
BibRef

Leal-Taixe, L.[Laura], Pons-Moll, G.[Gerard], Rosenhahn, B.[Bodo],
Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker,
MSVALC11(120-127).
IEEE DOI 1201
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

Boltes, M.[Maik], Zhang, J.[Jun], Seyfried, A.[Armin], Steffen, B.[Bernhard],
T-junction: Experiments, trajectory collection, and analysis,
MSVALC11(158-165).
IEEE DOI 1201
Pedestrian behavior at T-junctions. BibRef

Cho, H.G.[Hyung-Gi], Rybski, P.[Paul], Zhang, W.[Wende],
Vision-based Bicyclist Detection and Tracking for Intelligent Vehicles,
CMU-RI-TR-10-11, January, 2010.
WWW Link. 1102
From the vehicle. 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 -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Human Detection, Tracking, Infrared, IR, Thermal Images .