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Updated: Oct 30, 2025

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Intersection Vehicle Turning Control for Fully Autonomous Driving Scenarios.

Zhizhong Ding1, Chao Sun1, Momiao Zhou1

  • 1School of Computer and Information, Hefei University of Technology, Hefei 230009, China.

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Future autonomous driving vehicles (ADVs) can improve safety and reduce costs by leveraging vehicle ad hoc networks (VANETs) for intersection control. This study proposes new V2X communication strategies for advanced vehicle navigation.

Keywords:
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Area of Science:

  • Intelligent Transportation Systems
  • Robotics and Control Systems
  • Networked Vehicle Systems

Background:

  • Current autonomous driving vehicles (ADVs) rely on extensive onboard sensors, leading to high costs and safety concerns.
  • Future transportation may feature roads exclusively with ADVs, necessitating new control strategies.
  • Existing systems struggle with the complexity of mixed human-driven and autonomous traffic.

Purpose of the Study:

  • To develop advanced control strategies for ADVs at intersections, specifically for scenarios with only ADVs present.
  • To enhance driving safety and reduce production costs for ADVs through improved intersection management.
  • To investigate the role of vehicle ad hoc networks (VANETs) in future ADV operations.

Main Methods:

  • Proposing control strategies, models, and algorithms for target lane selection, trajectory planning, and vehicle control at intersections.
  • Utilizing a model predictive control (MPC) paradigm as the upper-layer controller for ADVs.
  • Conducting simulations on the CarSim-Simulink platform with representative intersection scenarios.

Main Results:

  • The proposed methods address the core challenges of vehicle turning control at intersections for all-ADV environments.
  • Model predictive control demonstrates effectiveness in managing ADV trajectories and lane changes.
  • Simulations validate the proposed approach in realistic intersection simulations.

Conclusions:

  • Vehicle-to-everything (V2X) communication via VANETs is crucial for future ADV intersection management.
  • The developed control strategies offer a pathway to significantly improve ADV safety and cost-efficiency.
  • This research lays the groundwork for more sophisticated and reliable autonomous driving systems.