Connectivity Enhanced Safe Neural Network Planner for Lane Changing in Mixed Traffic

Xiangguo Liu, Ruochen Jiao, Bowen Zheng, Dave Liang, Qi Zhu

Research output: Contribution to journalConference articlepeer-review

Abstract

Connectivity technology has shown great potentials in improving the safety and efficiency of transportation systems by providing information beyond the perception and prediction capabilities of individual vehicles. However, it is expected that human-driven and autonomous vehicles, and connected and non-connected vehicles need to share the transportation network during the transition period to fully connected and automated transportation systems. Such mixed traffic scenarios significantly increase the complexity in analyzing system behavior for highly interactive scenarios, e.g., lane changing. It is even harder to ensure system safety when neural network based planners are leveraged. In this work, we propose a connectivity-enhanced neural network based lane changing planner. By cooperating with surrounding connected vehicles, our proposed planner will adapt its planned trajectory according to the analysis of a safe evasion trajectory. We demonstrate the strength of our planner design in improving efficiency and ensuring safety in various mixed traffic scenarios with extensive simulations. We also analyze the system robustness when the communication or coordination is not perfect.

Original languageEnglish (US)
Pages (from-to)2568-2570
Number of pages3
JournalProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume2023-May
StatePublished - 2023
Event22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023 - London, United Kingdom
Duration: May 29 2023Jun 2 2023

Keywords

  • Connected and Autonomous Vehicles
  • Human-driven Vehicles
  • Mixed Traffic
  • Safe Neural Network Planner

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

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