Risk-constrained interactive safety under behavior uncertainty for autonomous driving

Julian Bernhard, Alois Knoll

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Balancing safety and efficiency when planning in dense traffic is challenging. Interactive behavior planners incorporate prediction uncertainty and interactivity inherent to these traffic situations. Yet, their use of single-objective optimality impedes interpretability of the resulting safety goal. Safety envelopes which restrict the allowed planning region yield interpretable safety under the presence of behavior uncertainty, yet, they sacrifice efficiency in dense traffic due to conservative driving. Studies show that humans balance safety and efficiency in dense traffic by accepting a probabilistic risk of violating the safety envelope. In this work, we adopt this safety objective for interactive planning. Specifically, we formalize this safety objective, present the Risk-Constrained Robust Stochastic Bayesian Game modeling interactive decisions satisfying a maximum risk of violating a safety envelope under uncertainty of other traffic participants' behavior and solve it using our variant of Multi-Agent Monte Carlo Tree Search. We demonstrate in simulation that our approach outperforms baselines approaches, and by reaching the specified violation risk level over driven simulation time, provides an interpretable and tunable safety objective for interactive planning.

Original languageEnglish
Title of host publication32nd IEEE Intelligent Vehicles Symposium, IV 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages63-70
Number of pages8
ISBN (Electronic)9781728153940
DOIs
StatePublished - 11 Jul 2021
Event32nd IEEE Intelligent Vehicles Symposium, IV 2021 - Nagoya, Japan
Duration: 11 Jul 202117 Jul 2021

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings
Volume2021-July

Conference

Conference32nd IEEE Intelligent Vehicles Symposium, IV 2021
Country/TerritoryJapan
CityNagoya
Period11/07/2117/07/21

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