Unveiling the Road Ahead - An MPC-Based Approach for Autonomous Intersection Navigation with Occlusion

Zheyu Zhang, Jingjing Jiang, Wen Hua Chen

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Occluded intersections pose challenges and potential danger for autonomous vehicles (AVs) operating in partly unknown environments. The limited visibility caused by occlusions introduces uncertainties and risks in decision-making processes. This paper proposes a Model Predictive Control (MPC)-based motion planning approach for AVs at occluded intersections, aiming to ensure safety while maximizing travel efficiency. The proposed approach guarantees safety by considering the worstcase scenario, reducing computational complexity and avoiding subjective tolerance of collision risk. It integrates risk assessment into the MPC-based motion planning process, allowing the AV to actively learn the environment and optimize its path. By consistently updating environmental information, the AV gradually eliminates risks. Simulation results demonstrate the effectiveness of the proposed method in achieving safe and efficient navigation at occluded intersections.

Original languageEnglish
Title of host publicationIAVVC 2023 - IEEE International Automated Vehicle Validation Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350322538
DOIs
Publication statusPublished - 2023
Event2023 IEEE International Automated Vehicle Validation Conference, IAVVC 2023 - Austin, United States
Duration: 16 Oct 202318 Oct 2023

Publication series

NameIAVVC 2023 - IEEE International Automated Vehicle Validation Conference, Proceedings

Conference

Conference2023 IEEE International Automated Vehicle Validation Conference, IAVVC 2023
Country/TerritoryUnited States
CityAustin
Period16/10/2318/10/23

Keywords

  • Model Predictive Control (MPC)
  • Motion Planning
  • Occluded Intersections
  • Safety Guarantees

ASJC Scopus subject areas

  • Artificial Intelligence
  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Modelling and Simulation
  • Instrumentation

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