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Extracting Human-like Driving Behaviors From Expert Driver Data Using Deep Learning

Kyle Sama, Yoichi Morales, Hailong Liu, Naoki Akai, Alexander Carballo, Eijiro Takeuchi, Kazuya Takeda
2020 IEEE Transactions on Vehicular Technology  
Deep learning techniques were used to extract latent features from the collected data.  ...  This paper introduces a method to extract driving behaviors from a human expert driver which are applied to an autonomous agent to reproduce proactive driving behaviors.  ...  Other methods attempt to learn how to drive from human drivers themselves [33] .  ... 
doi:10.1109/tvt.2020.2980197 fatcat:u3zlhuxkbzgrvogec23evfy22i

Introduction of Human Assistance in Self-Driving Car

Vidya Khanna, Jagan Institute of Management Studies, Rohini., Rahul Kakkar, Sahil Ahlawat, Jagan Institute of Management Studies, Rohini., Jagan Institute of Management Studies, Rohini.
2021 Journal of University of Shanghai for Science and Technology  
The present scenery of human being is addicted to automation and machine learning technology like medical, transportation and in IT sector.  ...  From the past few years, updating automation technology day by day and using all aspects in regular human life.  ...  Conclusion The Human assistance Model in self-driven car discussed in the paper provides a detailed information about how we can use human assistance System to get better functionality in self-driving  ... 
doi:10.51201/jusst/21/08425 fatcat:43j7l3r4aremze3vtozjhzrigq

Safe, efficient and socially-compatible decision of automated vehicles: a case study of unsignalized intersection driving [article]

Daofei Li, Ao Liu, Hao Pan, Wentao Chen
2022 arXiv   pre-print
Human-in-the-loop experiments are carried out, in which 24 subjects are invited to drive and interact with AVs deployed with the proposed algorithm and two comparison algorithms.  ...  A game-theoretic decision algorithm considering social compatibility is proposed to handle the interaction with a human-driven truck at an unsignalized intersection.  ...  [11] obtained some reference speed profiles of specific styles based on human driving data clustering, which were used to achieve human-like driving in complex interactions. Chen et al.  ... 
arXiv:2111.02977v2 fatcat:tgqk6gk2krde3fb6vohh2jfwhe

Older Adults with Mild Cognitive Impairments Show Less Driving Errors after a Multiple Sessions Simulator Training Program but Do Not Exhibit Long Term Retention

Normand Teasdale, Martin Simoneau, Lisa Hudon, Mathieu Germain Robitaille, Thierry Moszkowicz, Denis Laurendeau, Louis Bherer, Simon Duchesne, Carol Hudon
2016 Frontiers in Human Neuroscience  
to indicate a lane change, to verify a blind spot, or to engage in a visual search before crossing an intersection).  ...  The aim of the study was to provide support to the claim that individuals with MCI can benefit from a training program and improve their overall driving performance in a driving simulator.  ...  ACKNOWLEDGMENTS This research has received support from the Alzheimer Society of Canada. CH and SD are both Research Scholars from the Fonds de la Recherche du Québec-Santé.  ... 
doi:10.3389/fnhum.2016.00653 pmid:28082883 pmcid:PMC5186807 fatcat:cugu6fnzwbfahdkmsppj26vfei

Modeling risk anticipation and defensive driving on residential roads with inverse reinforcement learning

Masamichi Shimosaka, Takuhiro Kaneko, Kentaro Nishi
2014 17th International IEEE Conference on Intelligent Transportation Systems (ITSC)  
In this work, we provide a new framework of modeling risk anticipation and defensive driving with inverse reinforcement learning (IRL).  ...  Experimental results using actual driver maneuver data over 20 km of residential roads indicate that our approach is successful in terms of providing precise learning models of risk anticipation and defensive  ...  ACKNOWLEDGEMENTS We would like to sincerely thank Mr.  ... 
doi:10.1109/itsc.2014.6957937 dblp:conf/itsc/ShimosakaKN14 fatcat:colfut3a2zctdhpklydx47wcsa

BlindSpotNet: Seeing Where We Cannot See [article]

Taichi Fukuda, Kotaro Hasegawa, Shinya Ishizaki, Shohei Nobuhara, Ko Nishino
2022 arXiv   pre-print
We instead propose to learn to estimate blind spots in 2D, just from a monocular camera. We achieve this in two steps.  ...  The key idea is to reason in 3D but from 2D images by defining blind spots as those road regions that are currently invisible but become visible in the near future.  ...  How can we accomplish 2D blind spot detection? Just like we likely do, we could learn to estimate blind spots directly in 2D.  ... 
arXiv:2207.03870v1 fatcat:nhexzbnqfjd3bffqsts4kx635y

End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners [chapter]

Simon Hecker, Dengxin Dai, Luc Van Gool
2018 Lecture Notes in Computer Science  
In particular, we develop a sensor setup that provides data for a 360-degree view of the area surrounding the vehicle, the driving route to the destination, and low-level driving maneuvers (e.g. steering  ...  Keywords: Autonomous driving · end-to-end learning of driving · route planning for driving · surround-view cameras · driving dataset  ...  driving according to the speed of human driving; and 2) intersection scenarios by human annotation.  ... 
doi:10.1007/978-3-030-01234-2_27 fatcat:5wfbfiegcrbn3c46fwyu4yyaqy

Autonomous Vehicles and Vulnerable Road-Users—Important Considerations and Requirements Based on Crash Data from Two Countries

Andrew Paul Morris, Narelle Haworth, Ashleigh Filtness, Daryl-Palma Asongu Nguatem, Laurie Brown, Andry Rakotonirainy, Sebastien Glaser
2021 Behavioral Sciences  
vulnerable road-users, for a variety of reasons. (2) Crash data were analysed in two countries (Great Britain and Australia) to examine some challenging traffic scenarios that are prevalent in both countries  ...  However, the full effects of offering such systems, which may allow for drivers to become less than 100% engaged with the task of driving, may have detrimental impacts on other road-users, particularly  ...  humans from the responsibility of driving.  ... 
doi:10.3390/bs11070101 fatcat:uvam76rgozahfnqyiu3z3z4qeq

End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners [article]

Simon Hecker, Dengxin Dai, Luc Van Gool
2018 arXiv   pre-print
In particular, we develop a sensor setup that provides data for a 360-degree view of the area surrounding the vehicle, the driving route to the destination, and low-level driving maneuvers (e.g. steering  ...  Finally, we learn a novel driving model by integrating information from the surround-view cameras and the route planner.  ...  driving according to the speed of human driving; and 2) intersection scenarios by human annotation.  ... 
arXiv:1803.10158v2 fatcat:6bgsca7qofhixdvnmwjgzy2idi

Semi-Autonomous Vehicles as a Cognitive Assistive Device for Older Adults

Frank Knoefel, Bruce Wallace, Rafik Goubran, Iman Sabra, Shawn Marshall
2019 Geriatrics  
Losing the capacity to drive due to age-related cognitive decline can have a detrimental impact on the daily life functioning of older adults living alone and in remote areas.  ...  Semi-autonomous vehicles (SAVs) could have the potential to preserve driving independence of this population with high health needs.  ...  Acknowledgments: We would like to acknowledge the coordination support of Karen de Libero and the technical help on the figures of Julie Oleynik.  ... 
doi:10.3390/geriatrics4040063 pmid:31744041 pmcid:PMC6961042 fatcat:xw3oj54m7fecdo3mbnkicglpbi

Learning in the age of algorithmic cultures

Petar Jandrić, Jeremy Knox, Hamish Macleod, Christine Sinclair
2017 E-Learning and Digital Media  
This Editorial describes the main challenges at the intersections between algorithmic cultures and human learning.  ...  It suggests that studies of algorithms and learning are in their infancy and emphasizes that they carry potentials to confirm our existing ideas and surprise us with fresh insights.  ...  In this Special Issue, guest editors Petar Jandric, Jeremy Knox, Hamish Macleod and Christine Sinclair have invited authors to explore the intersections between algorithmic cultures and human learning.  ... 
doi:10.1177/2042753017731237 fatcat:voswbox77vd6xkgxwytffmt54a

Sustainability in the Digital Age [Special Issue Introduction]

Amy Luers, Lyse Langlois, Mathilde Mougeot, Sana Kharaghani, Alexandra Luccioni
2020 IEEE technology & society magazine  
We would also like to thank Andrea Ventimiglia for her support in editing. Finally, we thank Nilufar Sabet-Kassouf for her coordination, research, and editing support. Guest Editor Information  ...  We also thank Jeremy Pitt for the invitation to produce this issue and thank him and the Managing Editor, Terri Bookman, for their patience in pulling this issue together during a difficult time for the  ...  We include articles from researchers working on both the mechanics and ethics of AI and machine learning, analysts working on data and digital governance, scientists studying environmental and human dimensions  ... 
doi:10.1109/mts.2020.2991493 fatcat:v3cb6msyibgxtbknxlas6iftwm

Predicting driving behavior using inverse reinforcement learning with multiple reward functions towards environmental diversity

Masamichi Shimosaka, Kentaro Nishi, Junichi Sato, Hirokatsu Kataoka
2015 2015 IEEE Intelligent Vehicles Symposium (IV)  
In recent years, modeling driving behavior in residential roads through inverse reinforcement learning (IRL) has been attracting attention in intelligent vehicle community thanks to the superiority of  ...  However, it suffers from poor performance in diverse environment due to the fact that the single reward function could not handle all the environment with large diversity.  ...  ACKNOWLEDGMENT We would like to sincerely thank Mr. Keita Sato and Mr.  ... 
doi:10.1109/ivs.2015.7225745 dblp:conf/ivs/ShimosakaNSK15 fatcat:wbtfd5aninh6dgl4lszmzhat24

Seeing Beyond the Line of Site – Controlling Connected Automated Vehicles

Gábor Orosz, Jin I. Ge, Chaozhe R. He, Sergei S. Avedisov, Wubing B. Qin, Linjun Zhang
2017 Mechanical engineering (New York, N.Y. 1919)  
also when the lights will change, which may help it to decide how to approach an intersection [9] .  ...  Connected automated vehicles are able to see around a blind corner when moving toward an intersection, and know whether any vehicle is approaching from other directions.  ... 
doi:10.1115/1.2017-dec-8 fatcat:hznccjl5b5fefjh6zftxaxfdoy

Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning

Vasili Ramanishka, Yi-Ting Chen, Teruhisa Misu, Kate Saenko
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
To achieve systems that can operate in a complex physical and social environment, they need to understand and learn how humans drive and interact with traffic scenes.  ...  A novel annotation methodology is introduced to enable research on driver behavior understanding from untrimmed data sequences.  ...  Learning how humans drive and interact with traffic scenes is a step toward intelligent transportation systems. In this paper, we start from a driver-centric view to describe driver behaviors.  ... 
doi:10.1109/cvpr.2018.00803 dblp:conf/cvpr/RamanishkaCMS18 fatcat:fry4tklws5agho6kk2gl5ynhym
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