Unifying Decision-Making and Trajectory-Planning in Unsignalized Intersections Using Time-Varying Potential Fields

2026-07-15

AI Summary

This work addresses the decoupling between decision-making and trajectory planning for autonomous vehicles at unsignalized intersections by proposing an end-to-end integrated framework. By formulating a finite-horizon optimal control problem, the method uniquely combines time-varying artificial potential fields, short-term motion prediction, and conflict-zone occupancy coefficients to dynamically mitigate collision risks and generate safe, feasible reference trajectories. Evaluated in multi-vehicle simulation scenarios involving complex unsignalized intersections, the approach demonstrates effective navigation with high efficiency and safety, significantly enhancing overall system coordination and real-time performance.

Abstract

This paper presents a novel framework for integrated Decision-Making (DM) and Trajectory Planning (TP) for automated vehicles at unsignalized intersections. The approach leverages a Finite Horizon Optimal Control Problem (FHOCP) that employs Time-Varying Artificial Potential Fields (TV-APF). By utilizing short-horizon motion prediction and a dedicated conflict-zone occupancy coefficient, the framework suitably accounts for potential collisions within the FHOCP. The proposed method effectively unifies DM and TP, ensuring the generation of a feasible and safe reference trajectory. Simulation results in multi-vehicle traffic scenarios demonstrate the effectiveness of the approach.

Problem

• Decision-Making
• Trajectory Planning
• Unsignalized Intersections
• Autonomous Vehicles
• Collision Avoidance

Innovation

• Time-Varying Artificial Potential Fields
• Finite Horizon Optimal Control
• Unsignalized Intersections
• Integrated Decision-Making and Trajectory Planning
• Conflict-Zone Occupancy