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Rule-based optimal control for autonomous driving

Authors:

Noushin Mehdipour,

Amitai Y. Bin-Nun,

Emilio Frazzoli,

Radboud Duintjer Tebbens,

Calin BeltaAuthors Info & Claims

ICCPS '21: Proceedings of the ACM/IEEE 12th International Conference on Cyber-Physical Systems

Pages 143 - 154

https://doi.org/10.1145/3450267.3450542

Published: 19 May 2021 Publication History

Abstract

We develop optimal control strategies for Autonomous Vehicles (AVs) that are required to meet complex specifications imposed by traffic laws and cultural expectations of reasonable driving behavior. We formulate these specifications as rules, and specify their priorities by constructing a priority structure, called <u>T</u>otal <u>OR</u>der over e<u>Q</u>uivalence classes (TORQ). We propose a recursive framework, in which the satisfaction of the rules in the priority structure are iteratively relaxed based on their priorities. Central to this framework is an optimal control problem, where convergence to desired states is achieved using Control Lyapunov Functions (CLFs), and safety is enforced through Control Barrier Functions (CBFs). We also show how the proposed framework can be used for after-the-fact, pass/fail evaluation of trajectories - a given trajectory is rejected if we can find a controller producing a trajectory that leads to less violation of the rule priority structure. We present case studies with multiple driving scenarios to demonstrate the effectiveness of the proposed framework.

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Cited By

Ge MOhtani KDing MNiu YZhang YTakeda K(2024)Multimodal Trajectory Prediction for Diverse Vehicle Types in Autonomous Driving with Heterogeneous Data and Physical ConstraintsSensors10.3390/s2422732324:22(7323)Online publication date: 16-Nov-2024
https://doi.org/10.3390/s24227323
Zhao RChen ZFan YLi YGao F(2024)Towards Robust Decision-Making for Autonomous Highway Driving Based on Safe Reinforcement LearningSensors10.3390/s2413414024:13(4140)Online publication date: 26-Jun-2024
https://doi.org/10.3390/s24134140
Yang FLi XLiu QLi XLi Z(2024)Learning-Based Hierarchical Decision-Making Framework for Automatic Driving in Incompletely Connected Traffic ScenariosSensors10.3390/s2408259224:8(2592)Online publication date: 18-Apr-2024
https://doi.org/10.3390/s24082592
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Index Terms

Rule-based optimal control for autonomous driving

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cover image ACM Conferences

ICCPS '21: Proceedings of the ACM/IEEE 12th International Conference on Cyber-Physical Systems

May 2021

242 pages

ISBN:9781450383530

DOI:10.1145/3450267

General Chairs:
Martina Maggio
Lund University
,
James Weimer
University of Pennsylvania
,
Program Chairs:
Mohammad Al Faruque
University of California at Irvine
,
Meeko Oishi
University of New Mexico

Copyright © 2021 ACM.

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Published: 19 May 2021

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ICCPS '21

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ICCPS '21: ACM/IEEE 12th International Conference on Cyber-Physical Systems

May 19 - 21, 2021

Tennessee, Nashville

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Cited By

Ge MOhtani KDing MNiu YZhang YTakeda K(2024)Multimodal Trajectory Prediction for Diverse Vehicle Types in Autonomous Driving with Heterogeneous Data and Physical ConstraintsSensors10.3390/s2422732324:22(7323)Online publication date: 16-Nov-2024
https://doi.org/10.3390/s24227323
Zhao RChen ZFan YLi YGao F(2024)Towards Robust Decision-Making for Autonomous Highway Driving Based on Safe Reinforcement LearningSensors10.3390/s2413414024:13(4140)Online publication date: 26-Jun-2024
https://doi.org/10.3390/s24134140
Yang FLi XLiu QLi XLi Z(2024)Learning-Based Hierarchical Decision-Making Framework for Automatic Driving in Incompletely Connected Traffic ScenariosSensors10.3390/s2408259224:8(2592)Online publication date: 18-Apr-2024
https://doi.org/10.3390/s24082592
Qi HHou EYe P(2024)Cognitive Reinforcement Learning: An Interpretable Decision-Making for Virtual DriverIEEE Journal of Radio Frequency Identification10.1109/JRFID.2024.34186498(627-631)Online publication date: 2024
https://doi.org/10.1109/JRFID.2024.3418649
Abouelazm AMichel JZöllner J(2024)A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving2024 IEEE Intelligent Vehicles Symposium (IV)10.1109/IV55156.2024.10588385(156-163)Online publication date: 2-Jun-2024
https://doi.org/10.1109/IV55156.2024.10588385
Karnchanachari NGeromichalos DTan KLi NEriksen CYaghoubi SMehdipour NBernasconi GFong WGuo YCaesar H(2024)Towards learning-based planning: The nuPlan benchmark for real-world autonomous driving2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10610077(629-636)Online publication date: 13-May-2024
https://doi.org/10.1109/ICRA57147.2024.10610077
Nice MGunter GJi JZhang YBunting MBarbour WSprinkle JWork D(2024)A Middle Way to Traffic Enlightenment2024 ACM/IEEE 15th International Conference on Cyber-Physical Systems (ICCPS)10.1109/ICCPS61052.2024.00020(147-156)Online publication date: 13-May-2024
https://doi.org/10.1109/ICCPS61052.2024.00020
Li BWang YMao JIvanovic BVeer SLeung KPavone M(2024)Driving Everywhere with Large Language Model Policy Adaptation2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.01416(14948-14957)Online publication date: 16-Jun-2024
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Yu WZhao CWang HLiu JMa XYang YLi JWang WHu XZhao D(2024)Online legal driving behavior monitoring for self-driving vehiclesNature Communications10.1038/s41467-024-44694-515:1Online publication date: 9-Jan-2024
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Hejase BOzguner U(2023)Lyapunov Stability Regulation of Deep Reinforcement Learning Control with Application to Automated Driving2023 American Control Conference (ACC)10.23919/ACC55779.2023.10155918(4437-4442)Online publication date: 31-May-2023
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