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Pedestrian Models for Autonomous Driving Part II: high level models of human behaviour [article]

Fanta Camara, Nicola Bellotto, Serhan Cosar, Florian Weber, Dimitris Nathanael, Matthias Althoff, Jingyuan Wu, Johannes Ruenz, André Dietrich, Gustav Markkula, Anna Schieben, Fabio Tango, Natasha Merat (+1 others)
2020 arXiv   pre-print
This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behaviour, from prediction of individual pedestrians' likely destinations and paths, to game theoretic  ...  This survey clearly shows that, although there are good models for optimal walking behaviour, high-level psychological and social modelling of pedestrian behaviour still remains an open research question  ...  Between the high level surveyed in this part II and the low levels of part I, researchers infer psychological information from perceptual information.  ... 
arXiv:2003.11959v1 fatcat:acjjwohahvdlxgy56j45fjtkdq

Pedestrian Models for Autonomous Driving Part I: low level models, from sensing to tracking [article]

Fanta Camara, Nicola Bellotto, Serhan Cosar, Dimitris Nathanael, Matthias Althoff, Jingyuan Wu, Johannes Ruenz, André Dietrich, Charles W. Fox
2020 arXiv   pre-print
Autonomous vehicles (AVs) must share space with human pedestrians, both in on-road cases such as cars at pedestrian crossings and off-road cases such as delivery vehicles navigating through crowds on high-streets  ...  image detection to high-level psychology models, from the perspective of an AV designer.  ...  crucial interest as it provides the mathematical bridge from low to high level pedestrian behavior models.  ... 
arXiv:2002.11669v1 fatcat:fgg5j5jdwrbujjgtj2uhgrx2am

Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking

Fanta Camara, Nicola Bellotto, Serhan Cosar, Dimitris Nathanael, Matthias Althoff, Jingyuan Wu, Johannes Ruenz, Andre Dietrich, Charles Fox
2020 IEEE transactions on intelligent transportation systems (Print)  
image detection to high-level psychology models, from the perspective of an AV designer.  ...  Technologies at these levels are found to be mature and available as foundations for use in high-level systems, such as behaviour modelling, prediction and interaction control.  ...  +Event/Activity Models +Effects of Class on Trajectory +Pedestrian Interaction Models +Game Theory and Signalling Models Part II Sec. II-D Part II Sec. II-E Part II Sec. III Part II Sec.  ... 
doi:10.1109/tits.2020.3006768 fatcat:awa5dgk4rbazteetyyqrndbgxq

Decision-Making Technology for Autonomous Vehicles Learning-Based Methods, Applications and Future Outlook [article]

Qi Liu, Xueyuan Li, Shihua Yuan, Zirui Li
2021 arXiv   pre-print
This article proposes a brief review on learning-based decision-making technology for autonomous vehicles since it is significant for safer and efficient performance of autonomous vehicles.  ...  Finally, promising research topics in the future study of decision-making technology for autonomous vehicles are prospected.  ...  Decision-making is expressed to generate human-level safe and reasonable driving behaviors considering surrounding environmental information, motion of other traffic participants and state estimation of  ... 
arXiv:2107.01110v1 fatcat:ohffatmrmfbzdlihgvywryzupa

Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving [article]

Junyao Guo, Unmesh Kurup, Mohak Shah
2018 arXiv   pre-print
However, for complex, cluttered and unseen environments with high uncertainty, autonomous driving systems still frequently demonstrate erroneous or unexpected behaviors, that could lead to catastrophic  ...  models.  ...  Maxim Likhachev from Carnegie Mellon University for his invaluable comments that improved the manuscript.  ... 
arXiv:1811.11277v1 fatcat:ztrxyydtuveijizfn6a2dmt5ui

Trusted Autonomous Vehicles: an Interactive Exhibit

Hugo L. S. Araujo, Nervo Xavier Verdezoto, Syed Wali, Carlos Diego N. Damasceno, Rayna Dimitrova, Genovefa Kefalidou, Mehdi Mehtarizadeh, Mohammad Reza Mousavi, Jemima Onime, Jan Oliver Ringert, Jose Miguel Rojas
2019 2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)  
for ensuring their quality, i.e., employing software testing and simulations.  ...  ., safety and reliability, of autonomous vehicles is paramount to improving public expectations, perception and acceptance.  ...  can allow for quantitatively measuring the safety of driving behaviors.  ... 
doi:10.1109/iucc/dsci/smartcns.2019.00091 fatcat:t4imu3xdkrhenhzzznq2p6a4am

Vehicle-pedestrian dynamic interaction through tractography of relative movements and articulated pedestrian pose estimation

Rifat Mueid, Lauren Christopher, Renran Tian
2016 2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)  
dynamics that are critical for safe autonomous driving and transportation safety innovations.  ...  Based on existing single-frame human pose estimation model, we have implemented Kalman filtering with other new techniques to make stable stickfigure videos of the pedestrian dynamic motion.  ...  ACKNOWLEDGMENT We would like to thank SAMSUNG Global Research Outreach (GRO) Program for supporting this project.  ... 
doi:10.1109/aipr.2016.8010592 dblp:conf/aipr/MueidCT16 fatcat:gfnf5mmttzgtbfrpz5caiusbqa

Overview of Tools Supporting Planning for Automated Driving [article]

Kailin Tong, Zlatan Ajanovic, Georg Stettinger
2020 arXiv   pre-print
Planning is an essential topic in the realm of automated driving.  ...  Besides planning algorithms that are widely covered in the literature, planning requires different software tools for its development, validation, and execution.  ...  Existing planning algorithms originate predominantly from the community of robotics: Their target is to convert high-level specifications of tasks from humans into low-level descriptions of how to move  ... 
arXiv:2003.04081v1 fatcat:xeufjrn64fca5dxo7ajkcyz4pi

A Hierarchical Pedestrian Behavior Model to Generate Realistic Human Behavior in Traffic Simulation [article]

Scott Larter, Rodrigo Queiroz, Sean Sedwards, Atrisha Sarkar, Krzysztof Czarnecki
2022 arXiv   pre-print
Modelling pedestrian behavior is crucial in the development and testing of autonomous vehicles.  ...  Our model is shown to replicate the real-world pedestrians' trajectories with a high degree of fidelity and a decision-making accuracy of 98% or better, given only high-level routing information for each  ...  In this work, we present a hierarchical pedestrian behavior model that incorporates behavior trees to handle high-level decision-making processes and an adapted Social Force Model to drive low-level motion  ... 
arXiv:2206.01601v1 fatcat:ykmthibdfnhwhpis4fdc4gxd6a

Predicting Pedestrian Crossing Intention with Feature Fusion and Spatio-Temporal Attention [article]

Dongfang Yang, Haolin Zhang, Ekim Yurtsever, Keith Redmill, Ümit Özgüner
2021 arXiv   pre-print
Pedestrian crossing intention should be recognized in real-time, especially for urban driving. Recent works have shown the potential of using vision-based deep neural network models for this task.  ...  Predicting vulnerable road user behavior is an essential prerequisite for deploying Automated Driving Systems (ADS) in the real-world.  ...  In level 4 autnomous driving, pedestrian crossing behavior is one of the most important behaviors that needs to be studied urgently.  ... 
arXiv:2104.05485v2 fatcat:o5efy5n2cbcdfi7xsbclabegpq

Liability for robots II: an economic analysis

Alice Guerra, Francesco Parisi, Daniel Pi
2021 Journal of Institutional Economics  
In turn, this rule will bring down the price of safer robots, driving unsafe technology out of the market.  ...  Thanks to the percolation effect of residual liability, operators will also be incentivized to adopt optimal activity levels in robots' usage.  ...  The substitution is not necessarily total: a robot may be partially autonomous, sharing decision-making duties with a human operator; or fully autonomous but the operator still decides its objectives and  ... 
doi:10.1017/s1744137421000837 fatcat:apjdqznubnaebnuljoxhyy2gdy

Autonomous Vehicle Evaluation: A Comprehensive Survey on Modeling and Simulation Approaches

Hesham Alghodhaifi, Sridhar Lakshmanan
2021 IEEE Access  
In recent years, autonomous vehicles (AVs), which observe the driving environment and lead a few or all of the driving duties, have garnered tremendous success.  ...  Unfortunately, this approach is time-consuming and costly because one needs to drive thousands of miles to experience a police-reported collision and nearly millions of miles for a fatal crash.  ...  Therefore, the car-following model is an essential part of modeling the behavior of human-driven vehicles (HVs), connected autonomous vehicles (CAVs), and AVs in microsimulation modeling [110, 313] .  ... 
doi:10.1109/access.2021.3125620 fatcat:z4uj4riyorhu7hliv4sqvnz6n4

Joint Attention in Driver-Pedestrian Interaction: from Theory to Practice [article]

Amir Rasouli, John K. Tsotsos
2018 arXiv   pre-print
Today, one of the major challenges that autonomous vehicles are facing is the ability to drive in urban environments.  ...  In this literature review we aim to address the interaction problem between pedestrians and drivers (or vehicles) from joint attention point of view.  ...  Part II Joint Attention, Interaction and Behavior Understanding 3 Joint Attention in Human Interaction The precursor to any form of social interaction between humans (or primates [81] , see Figure  ... 
arXiv:1802.02522v2 fatcat:nzeq5eleajcktjl32m2kyqu7rq

GAMMA: A General Agent Motion Model for Autonomous Driving [article]

Yuanfu Luo and Panpan Cai and Yiyuan Lee and David Hsu
2022 arXiv   pre-print
Further, GAMMA explicitly conditions the prediction on human behavioral states as parameters of the optimization model, in order to account for versatile human behaviors.  ...  Further, the computational efficiency and the flexibility of GAMMA enable (i) simulation of mixed urban traffic at many locations worldwide and (ii) planning for autonomous driving in dense traffic with  ...  We propose a General Agent Motion Model for Autonomous driving (GAMMA). GAMMA offers high prediction accuracy, computational efficiency, and simplicity of implementation.  ... 
arXiv:1906.01566v6 fatcat:qlob2tld7nbpjnbw6aptkgrmay

STAF: Spatio-Temporal Attention Framework for Understanding Road Agents Behaviors

R. Trabelsi, R. Khemmar, B. Decoux, J.-Y. Ertaud, R. Boutteau
2022 IEEE Access  
On-road behavior analysis is a key task required for robust autonomous vehicles. Unlike traditional perception tasks, this paper aims to achieve a high-level understanding of road agent activities.  ...  the behavior of road agents.  ...  The authors would like to thank SEGULA Technologies for their collaboration and the research engineers and technicians of the IRSEEM LNA (Autonomous Navigation Laboratory) for their support.  ... 
doi:10.1109/access.2022.3176861 fatcat:yeileoz6ibel7ju5imuntoaqli
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