Generating Scenarios with Diverse Pedestrian Behaviors for Autonomous Vehicle Testing

Maria Priisalu, Aleksis Pirinen, Ciprian Paduraru, Cristian Sminchisescu

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceedingPeer review

Sammanfattning

There exist several datasets for developing self-driving car methodologies. Manually collected datasets impose inherent limitations on the variability of test cases and it is particularly difficult to acquire challenging scenarios, e.g. ones involving collisions with pedestrians. A way to alleviate this is to consider automatic generation of safety-critical scenarios for autonomous vehicle (AV) testing. Existing approaches for scenario generation use heuristic pedestrian behavior models. We instead propose a framework that can use state-of-the-art pedestrian motion models, which is achieved by reformulating the problem as learning where to place pedestrians such that the induced scenarios are collision prone for a given AV. Our pedestrian initial location model can be used in conjunction with any goal driven pedestrian model which makes it possible to challenge an AV with a wide range of pedestrian behaviors – this ensures that the AV can avoid collisions with any pedestrian it encounters. We show that it is possible to learn a collision seeking scenario generation model when both the pedestrian and AV are collision avoiding. The initial location model is conditioned on scene semantics and occlusions to ensure semantic and visual plausibility, which increases the realism of generated scenarios. Our model can be used to test any AV model given sufficient constraints.
Originalspråkengelska
Titel på värdpublikationConference on Robot Learning, 8-11 November 2021, London, UK
FörlagML Research Press
Sidor1247-1258
Antal sidor12
StatusPublished - 2022
EvenemangConference on Robot Learning - London, Storbritannien
Varaktighet: 2021 nov. 8 → …
Konferensnummer: 5
https://openreview.net/group?id=robot-learning.org/CoRL/2021/Conference

Publikationsserier

NamnProceedings of Machine Learning Research
FörlagML Research Press
Volym164
ISSN (elektroniskt)2640-3498

Konferens

KonferensConference on Robot Learning
Förkortad titelCoRL
Land/TerritoriumStorbritannien
OrtLondon
Period2021/11/08 → …
Internetadress

Ämnesklassifikation (UKÄ)

  • Robotteknik och automation

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