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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.

References

[1]

A. D. Ames, K. Galloway, and J. W. Grizzle. 2012. Control Lyapunov Functions and Hybrid Zero Dynamics. In Proc. of 51rd IEEE Conference on Decision and Control. 6837--6842.

[2]

A. D. Ames, X. Xu, J. W. Grizzle, and P. Tabuada. 2017. Control Barrier Function Based Quadratic Programs for Safety Critical Systems. IEEE Trans. Automat. Control 62, 8 (2017), 3861--3876.

[3]

Z. Artstein. 1983. Stabilization with relaxed controls. Nonlinear Analysis: Theory, Methods & Applications 7, 11 (1983), 1163--1173.

[4]

A. Censi, K. Slutsky, T. Wongpiromsarn, D. Yershov, S. Pendleton, J. Fu, and E. Frazzoli. 2019. Liability, Ethics, and Culture-Aware Behavior Specification using Rulebooks. In 2019 International Conference on Robotics and Automation. 8536--8542.

[5]

A. Collin, A. Bilka, S. Pendleton, and R. D. Tebbens. 2020. Safety of the Intended Driving Behavior Using Rulebooks. In IV Workshop on Ensuring and Validating Safety for Automated Vehicles. 1--7.

[6]

R. Dimitrova, M. Ghasemi, and U. Topcu. 2018. Maximum realizability for linear temporal logic specifications. In International Symposium on Automated Technology for Verification and Analysis. 458--475.

[7]

A. Donzé and O. Maler. 2010. Robust satisfaction of temporal logic over real-valued signals. In International Conference on Formal Modeling and Analysis of Timed Systems. 92--106.

[8]

R. A. Freeman and P. V. Kokotovic. 1996. Robust Nonlinear Control Design. Birkhauser.

[9]

P. Glotfelter, J. Cortes, and M. Egerstedt. 2017. Nonsmooth barrier functions with applications to multi-robot systems. IEEE control systems letters 1, 2 (2017), 310--315.

[10]

ISO. 2019. PAS 21448-Road Vehicles-Safety of the Intended Functionality. International Organization for Standardization (2019).

[11]

H. K. Khalil. 2002. Nonlinear Systems. Prentice Hall, third edition.

[12]

L. Lindemann and D. V. Dimarogonas. 2019. Control barrier functions for signal temporal logic tasks. IEEE Control Systems Letters 3, 1 (2019), 96--101.

[13]

O. Maler and D. Nickovic. 2004. Monitoring temporal properties of continuous signals. In Proc. of International Conference on FORMATS-FTRTFT. Grenoble, France, 152--166.

[14]

N. Mehdipour, C. Vasile, and C. Belta. 2019. Arithmetic-geometric mean robustness for control from signal temporal logic specifications. In American Control Conference. 1690--1695.

[15]

N. Mehdipour, C. I. Vasile, and C. Belta. 2020. Specifying User Preferences using Weighted Signal Temporal Logic. IEEE Control Systems Letters (2020).

[16]

Q. Nguyen and K. Sreenath. 2016. Exponential Control Barrier Functions for Enforcing High Relative-Degree Safety-Critical Constraints. In Proc. of the American Control Conference. 322--328.

[17]

M. Nolte, G. Bagschik, I. Jatzkowski, T. Stolte, A. Reschka, and M. Maurer. 2017. Towards a skill-and ability-based development process for self-aware automated road vehicles. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems. 1--6.

[18]

M. Parseh, F. Asplund, M. Nybacka, L. Svensson, and M. Törngren. 2019. Pre-Crash Vehicle Control and Manoeuvre Planning: A Step Towards Minimizing Collision Severity for Highly Automated Vehicles. In 2019 IEEE International Conference of Vehicular Electronics and Safety (ICVES). 1--6.

[19]

X. Qian, J. Gregoire, F. Moutarde, and A. D. L. Fortelle. 2014. Priority-based coordination of autonomous and legacy vehicles at intersection. In IEEE conference on intelligent transportation systems. 1166--1171.

[20]

A. Rucco, G. Notarstefano, and J. Hauser. 2015. An Efficient Minimum-Time Trajectory Generation Strategy for Two-Track Car Vehicles. IEEE Transactions on Control Systems Technology 23, 4 (2015), 1505--1519.

[21]

S. Shalev-Shwartz, S. Shammah, and A. Shashua. 2017. On a formal model of safe and scalable self-driving cars. preprint in arXiv:1708.06374 (2017).

[22]

K. P. Tee, S. S. Ge, and E. H. Tay. 2009. Barrier lyapunov functions for the control of output-constrained nonlinear systems. Automatica 45, 4 (2009), 918--927.

Digital Library

[23]

J. Tmová, L. I R. Castro, S. Karaman, E. Frazzoli, and D. Rus. 2013. Minimum-violation LTL planning with conflicting specifications. In 2013 American Control Conference. 200--205.

[24]

S. Ulbrich and M. Maurer. 2013. Probabilistic online POMDP decision making for lane changes in fully automated driving. In IEEE Conference on Intelligent Transportation Systems. 2063--2067.

[25]

L. Wang, A. D. Ames, and M. Egerstedt. 2017. Safety barrier certificates for collisions-free multirobot systems. IEEE Transactions on Robotics 33, 3 (2017), 661--674.

Digital Library

[26]

R. Wisniewski and C. Sloth. 2013. Converse barrier certificate theorem. In Proc. of 52nd IEEE Conference on Decision and Control. Florence, Italy, 4713--4718.

[27]

W. Xiao and C. Belta. 2019. Control Barrier Functions for Systems with High Relative Degree. In Proc. of 58th IEEE Conference on Decision and Control. Nice, France, 474--479.

[28]

W. Xiao, C. Belta, and C. G. Cassandras. 2020. Feasibility Guided Learning for Constrained Optimal Control problems. In Proc. of 59th IEEE Conference on Decision and Control. 1896--1901.

[29]

W. Xiao, C. Belta, and C. G. Cassandras. 2021. Adaptive Control Barrier Functions. In provisonally accepted in Transactions on Automatic Control, preprint in arXiv:2002.04577.

Cited By

Antonya CIclodean CRoșu I(2025)Enhancing Roundabout Efficiency Through Autonomous Vehicle CoordinationVehicles10.3390/vehicles70100097:1(9)Online publication date: 24-Jan-2025
https://doi.org/10.3390/vehicles7010009
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
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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.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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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

Antonya CIclodean CRoșu I(2025)Enhancing Roundabout Efficiency Through Autonomous Vehicle CoordinationVehicles10.3390/vehicles70100097:1(9)Online publication date: 24-Jan-2025
https://doi.org/10.3390/vehicles7010009
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
Thomas SSharma APandey RUmanand L(2024)A Novel Monocular Camera-based Modular Reference Generation for Autonomous Vehicles2024 IEEE International Symposium on Smart Electronic Systems (iSES)10.1109/iSES63344.2024.00078(344-349)Online publication date: 16-Dec-2024
https://doi.org/10.1109/iSES63344.2024.00078
Li SPeng KHui FLi ZWei CWang W(2024)A Decision-Making Approach for Complex Unsignalized Intersection by Deep Reinforcement LearningIEEE Transactions on Vehicular Technology10.1109/TVT.2024.340891773:11(16134-16147)Online publication date: Nov-2024
https://doi.org/10.1109/TVT.2024.3408917
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
Kwon HLee GLee JOh S(2024)Safe CoR: A Dual-Expert Approach to Integrating Imitation Learning and Safe Reinforcement Learning Using Constraint Rewards2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS58592.2024.10801577(2893-2898)Online publication date: 14-Oct-2024
https://doi.org/10.1109/IROS58592.2024.10801577
Black MFainekos GHoxha BOkamoto HProkhorov D(2024)CBFkit: A Control Barrier Function Toolbox for Robotics Applications2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS58592.2024.10801431(12428-12434)Online publication date: 14-Oct-2024
https://doi.org/10.1109/IROS58592.2024.10801431
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