Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior
Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with comple...
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Veröffentlicht in: | IEEE transactions on intelligent transportation systems 2021-09, Vol.22 (9), p.5453-5472 |
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creator | Camara, Fanta Bellotto, Nicola Cosar, Serhan Weber, Florian Nathanael, Dimitris Althoff, Matthias Wu, Jingyuan Ruenz, Johannes Dietrich, Andre Markkula, Gustav Schieben, Anna Tango, Fabio Merat, Natasha Fox, Charles |
description | Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behavior as well as detecting and tracking them. This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychological models, from the perspective of an AV designer. This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behavior, from prediction of individual pedestrians' likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behavior, high-level psychological and social modelling of pedestrian behavior still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behavior, but much work is still needed to translate them into quantitative algorithms for practical AV control. |
doi_str_mv | 10.1109/TITS.2020.3006767 |
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Unlike static obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behavior as well as detecting and tracking them. This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychological models, from the perspective of an AV designer. This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behavior, from prediction of individual pedestrians' likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behavior, high-level psychological and social modelling of pedestrian behavior still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behavior, but much work is still needed to translate them into quantitative algorithms for practical AV control.</description><identifier>ISSN: 1524-9050</identifier><identifier>EISSN: 1558-0016</identifier><identifier>DOI: 10.1109/TITS.2020.3006767</identifier><identifier>CODEN: ITISFG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Automobiles ; Autonomous vehicles ; datasets ; detection ; eHMI ; Game theory ; game-theoretic models ; Hidden Markov models ; Human behavior ; Image detection ; Legged locomotion ; Machine learning ; microscopic and macroscopic behavior models ; Modelling ; Pedestrian crossings ; pedestrian interaction ; Pedestrians ; Predictive models ; Psychology ; Review ; sensing ; signalling models ; survey ; Taxonomy ; tracking ; Trajectory ; trajectory prediction</subject><ispartof>IEEE transactions on intelligent transportation systems, 2021-09, Vol.22 (9), p.5453-5472</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c336t-32ac8bb6a8cd4c5a05fe14e69a3118712e56b6901616c4bfcf8440e72481f8183</citedby><cites>FETCH-LOGICAL-c336t-32ac8bb6a8cd4c5a05fe14e69a3118712e56b6901616c4bfcf8440e72481f8183</cites><orcidid>0000-0002-6695-8081 ; 0000-0003-4140-9948 ; 0000-0002-0933-8556 ; 0000-0003-3733-842X ; 0000-0003-2361-549X ; 0000-0002-2655-1228 ; 0000-0001-7950-9608 ; 0000-0003-0244-1582</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9151337$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9151337$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Camara, Fanta</creatorcontrib><creatorcontrib>Bellotto, Nicola</creatorcontrib><creatorcontrib>Cosar, Serhan</creatorcontrib><creatorcontrib>Weber, Florian</creatorcontrib><creatorcontrib>Nathanael, Dimitris</creatorcontrib><creatorcontrib>Althoff, Matthias</creatorcontrib><creatorcontrib>Wu, Jingyuan</creatorcontrib><creatorcontrib>Ruenz, Johannes</creatorcontrib><creatorcontrib>Dietrich, Andre</creatorcontrib><creatorcontrib>Markkula, Gustav</creatorcontrib><creatorcontrib>Schieben, Anna</creatorcontrib><creatorcontrib>Tango, Fabio</creatorcontrib><creatorcontrib>Merat, Natasha</creatorcontrib><creatorcontrib>Fox, Charles</creatorcontrib><title>Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior</title><title>IEEE transactions on intelligent transportation systems</title><addtitle>TITS</addtitle><description>Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behavior as well as detecting and tracking them. This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychological models, from the perspective of an AV designer. This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behavior, from prediction of individual pedestrians' likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behavior, high-level psychological and social modelling of pedestrian behavior still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behavior, but much work is still needed to translate them into quantitative algorithms for practical AV control.</description><subject>Algorithms</subject><subject>Automobiles</subject><subject>Autonomous vehicles</subject><subject>datasets</subject><subject>detection</subject><subject>eHMI</subject><subject>Game theory</subject><subject>game-theoretic models</subject><subject>Hidden Markov models</subject><subject>Human behavior</subject><subject>Image detection</subject><subject>Legged locomotion</subject><subject>Machine learning</subject><subject>microscopic and macroscopic behavior models</subject><subject>Modelling</subject><subject>Pedestrian crossings</subject><subject>pedestrian interaction</subject><subject>Pedestrians</subject><subject>Predictive models</subject><subject>Psychology</subject><subject>Review</subject><subject>sensing</subject><subject>signalling models</subject><subject>survey</subject><subject>Taxonomy</subject><subject>tracking</subject><subject>Trajectory</subject><subject>trajectory prediction</subject><issn>1524-9050</issn><issn>1558-0016</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kN1LwzAUxYMoOKd_gPgS8Lkzt_lo6tv8XGHiwO05pF2yZWzNTNqB_70tmz7dy-Wcczk_hG6BjABI_jAv5l-jlKRkRAkRmcjO0AA4lwkhIM77PWVJTji5RFcxbror4wADtJiZpYlNcLrGH35pthFbH_C4bXztd76N-CW4g6tXeKZDg4viEU_cap1MzcFs_xze4km76xKezFofnA_X6MLqbTQ3pzlEi7fX-fMkmX6-F8_jaVJRKpqEprqSZSm0rJas4ppwa4AZkWsKIDNIDRelyLsGICpW2spKxojJUibBSpB0iO6Pufvgv9uuh9r4NtTdS5VyIYnIs5x3KjiqquBjDMaqfXA7HX4UENXTUz091dNTJ3qd5-7occaYf30OHCjN6C9bgWnX</recordid><startdate>20210901</startdate><enddate>20210901</enddate><creator>Camara, Fanta</creator><creator>Bellotto, Nicola</creator><creator>Cosar, Serhan</creator><creator>Weber, Florian</creator><creator>Nathanael, Dimitris</creator><creator>Althoff, Matthias</creator><creator>Wu, Jingyuan</creator><creator>Ruenz, Johannes</creator><creator>Dietrich, Andre</creator><creator>Markkula, Gustav</creator><creator>Schieben, Anna</creator><creator>Tango, Fabio</creator><creator>Merat, Natasha</creator><creator>Fox, Charles</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Unlike static obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behavior as well as detecting and tracking them. This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychological models, from the perspective of an AV designer. This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behavior, from prediction of individual pedestrians' likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behavior, high-level psychological and social modelling of pedestrian behavior still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behavior, but much work is still needed to translate them into quantitative algorithms for practical AV control.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TITS.2020.3006767</doi><tpages>20</tpages><orcidid>https://orcid.org/0000-0002-6695-8081</orcidid><orcidid>https://orcid.org/0000-0003-4140-9948</orcidid><orcidid>https://orcid.org/0000-0002-0933-8556</orcidid><orcidid>https://orcid.org/0000-0003-3733-842X</orcidid><orcidid>https://orcid.org/0000-0003-2361-549X</orcidid><orcidid>https://orcid.org/0000-0002-2655-1228</orcidid><orcidid>https://orcid.org/0000-0001-7950-9608</orcidid><orcidid>https://orcid.org/0000-0003-0244-1582</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Automobiles Autonomous vehicles datasets detection eHMI Game theory game-theoretic models Hidden Markov models Human behavior Image detection Legged locomotion Machine learning microscopic and macroscopic behavior models Modelling Pedestrian crossings pedestrian interaction Pedestrians Predictive models Psychology Review sensing signalling models survey Taxonomy tracking Trajectory trajectory prediction |
title | Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior |
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