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Safety-Driven Interactive Planning for Neural Network-Based Lane Changing

Authors:

Qi ZhuAuthors Info & Claims

ASPDAC '23: Proceedings of the 28th Asia and South Pacific Design Automation Conference

Pages 39 - 45

https://doi.org/10.1145/3566097.3567847

Published: 31 January 2023 Publication History

Abstract

Neural network-based driving planners have shown great promises in improving task performance of autonomous driving. However, it is critical and yet very challenging to ensure the safety of systems with neural network-based components, especially in dense and highly interactive traffic environments. In this work, we propose a safety-driven interactive planning framework for neural network-based lane changing. To prevent over-conservative planning, we identify the driving behavior of surrounding vehicles and assess their aggressiveness, and then adapt the planned trajectory for the ego vehicle accordingly in an interactive manner. The ego vehicle can proceed to change lanes if a safe evasion trajectory exists even in the predicted worst case; otherwise, it can stay around the current lateral position or return back to the original lane. We quantitatively demonstrate the effectiveness of our planner design and its advantage over baseline methods through extensive simulations with diverse and comprehensive experimental settings, as well as in real-world scenarios collected by an autonomous vehicle company.

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Cited By

Roberts AHeidari Iman MBellone MGhasempouri TRaik JMaennel OHamad MSteinhorst S(2024)ADAssure: Debugging Methodology for Autonomous Driving Control Algorithms2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE58400.2024.10546519(1-6)Online publication date: 25-Mar-2024
https://doi.org/10.23919/DATE58400.2024.10546519
Yuan DYu XLi SYin X(2024)Safe-by-construction autonomous vehicle overtaking using control barrier functions and model predictive controlInternational Journal of Systems Science10.1080/00207721.2024.230466555:7(1283-1303)Online publication date: 2-Mar-2024
https://doi.org/10.1080/00207721.2024.2304665
Wang YZhan SJiao RWang ZJin WYang ZWang ZHuang CZhu QKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Enforcing hard constraints with soft barriersProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619930(36593-36604)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619930
Show More Cited By

Index Terms

Safety-Driven Interactive Planning for Neural Network-Based Lane Changing
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Robotics
      1. Robotic autonomy
2. Computing methodologies
  1. Artificial intelligence
    1. Planning and scheduling
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

ASPDAC '23: Proceedings of the 28th Asia and South Pacific Design Automation Conference

January 2023

807 pages

ISBN:9781450397834

DOI:10.1145/3566097

General Chair:
Atsushi Takahashi
Tokyo Institute of Technology

Copyright © 2023 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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SIGDA: ACM Special Interest Group on Design Automation

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New York, NY, United States

Publication History

Published: 31 January 2023

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ASPDAC '23

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SIGDA

ASPDAC '23: 28th Asia and South Pacific Design Automation Conference

January 16 - 19, 2023

Tokyo, Japan

Acceptance Rates

ASPDAC '23 Paper Acceptance Rate 102 of 328 submissions, 31%;

Overall Acceptance Rate 466 of 1,454 submissions, 32%

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ASPDAC '25

Sponsor:
sigda

30th Asia and South Pacific Design Automation Conference

January 20 - 23, 2025

Tokyo , Japan

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Cited By

Roberts AHeidari Iman MBellone MGhasempouri TRaik JMaennel OHamad MSteinhorst S(2024)ADAssure: Debugging Methodology for Autonomous Driving Control Algorithms2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE58400.2024.10546519(1-6)Online publication date: 25-Mar-2024
https://doi.org/10.23919/DATE58400.2024.10546519
Yuan DYu XLi SYin X(2024)Safe-by-construction autonomous vehicle overtaking using control barrier functions and model predictive controlInternational Journal of Systems Science10.1080/00207721.2024.230466555:7(1283-1303)Online publication date: 2-Mar-2024
https://doi.org/10.1080/00207721.2024.2304665
Wang YZhan SJiao RWang ZJin WYang ZWang ZHuang CZhu QKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Enforcing hard constraints with soft barriersProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619930(36593-36604)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619930
Wang YZhan SWang ZHuang CWang ZYang ZZhu QMitra SVenkatasubramanian NDubey AFeng LGhasemi MSprinkle J(2023)Joint Differentiable Optimization and Verification for Certified Reinforcement LearningProceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2023)10.1145/3576841.3585919(132-141)Online publication date: 9-May-2023
https://dl.acm.org/doi/10.1145/3576841.3585919
Jiao RBai JLiu XSato TYuan XChen QZhu Q(2023)Learning Representation for Anomaly Detection of Vehicle Trajectories2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS55552.2023.10342070(9699-9706)Online publication date: 1-Oct-2023
https://doi.org/10.1109/IROS55552.2023.10342070
Liu XJiao RWang YHan YZheng BZhu Q(2023)Safety-Assured Speculative Planning with Adaptive Prediction2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS55552.2023.10341530(9714-9721)Online publication date: 1-Oct-2023
https://doi.org/10.1109/IROS55552.2023.10341530
Liu XLuo YGoeckner AChakraborty TJiao RWang NWang YSato TChen QZhu Q(2023)Invited: Waving the Double-Edged Sword: Building Resilient CAVs with Edge and Cloud Computing2023 60th ACM/IEEE Design Automation Conference (DAC)10.1109/DAC56929.2023.10247809(1-4)Online publication date: 9-Jul-2023
https://doi.org/10.1109/DAC56929.2023.10247809

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