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Cooperative Motion Optimization Based on Risk Degree under Automatic Driving Environment.

Miaomiao Liu1, Mingyue Zhu1, Minkun Yao1

  • 1School of Transportation Science and Engineering, Beihang University, Beijing 100191, China.

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|July 13, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a cooperative motion optimization strategy for autonomous vehicles (AVs) at intersections. The strategy enhances traffic efficiency and safety by adjusting AV trajectories based on collision risk, outperforming traditional methods.

Keywords:
autonomous vehiclescooperative strategymotion optimizationrisk degreetraffic simulation

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Area of Science:

  • Intelligent Transportation Systems
  • Autonomous Vehicle Navigation
  • Traffic Engineering

Background:

  • Traffic congestion at intersections is a major challenge in intelligent transportation systems.
  • Optimizing traffic flow and ensuring safety for autonomous vehicles (AVs) requires advanced strategies.
  • Current methods may not fully address the complexities of AV interactions at intersections.

Purpose of the Study:

  • To develop a cooperative motion optimization strategy for AVs at intersections.
  • To enhance traffic efficiency and safety by minimizing collision risks.
  • To improve the stability of AV acceleration within the transportation network.

Main Methods:

  • Generating AV trajectories considering all possible exit lanes.
  • Implementing a risk-degree-based motion optimization algorithm for collision avoidance.
  • Formulating a cooperative strategy with constraints including car-following, traffic signals, and conflict resolution.

Main Results:

  • A 20.51% and 11.59% improvement in traffic efficiency in typical scenarios compared to First-Come-First-Serve.
  • Significant enhancements in the stability of optimized AV acceleration.
  • Validation of the cooperative strategy through SUMO simulations.

Conclusions:

  • The proposed cooperative motion optimization strategy effectively improves traffic efficiency and safety at intersections.
  • The risk-degree-based approach provides a robust method for collision prevention among AVs.
  • The strategy demonstrates the potential for more stable and efficient autonomous vehicle operations in intelligent transportation systems.