Integrated Decision and Control at Multi-Lane Intersections with Mixed Traffic Flow

TL;DR

This paper presents a learning-based integrated decision and control framework for multi-lane intersections with mixed traffic, improving safety and efficiency by handling realistic traffic lights and diverse traffic participants.

Contribution

It develops a novel reinforcement learning approach that incorporates realistic traffic light models and multiple safety constraints for complex intersections with mixed traffic.

Findings

The proposed method achieves safer and more efficient intersection management.

It reduces computational time by three orders of magnitude compared to MPC.

Simulation results validate the effectiveness of the learned policy.

Abstract

Autonomous driving at intersections is one of the most complicated and accident-prone traffic scenarios, especially with mixed traffic participants such as vehicles, bicycles and pedestrians. The driving policy should make safe decisions to handle the dynamic traffic conditions and meet the requirements of on-board computation. However, most of the current researches focuses on simplified intersections considering only the surrounding vehicles and idealized traffic lights. This paper improves the integrated decision and control framework and develops a learning-based algorithm to deal with complex intersections with mixed traffic flows, which can not only take account of realistic characteristics of traffic lights, but also learn a safe policy under different safety constraints. We first consider different velocity models for green and red lights in the training process and use a finite state machine to handle different modes of light transformation. Then we design different types of distance constraints for vehicles, traffic lights, pedestrians, bicycles respectively and formulize the constrained optimal control problems (OCPs) to be optimized. Finally, reinforcement learning (RL) with value and policy networks is adopted to solve the series of OCPs. In order to verify the safety and efficiency of the proposed method, we design a multi-lane intersection with the existence of large-scale mixed traffic participants and set practical traffic light phases. The simulation results indicate that the trained decision and control policy can well balance safety and tracking performance. Compared with model predictive control (MPC), the computational time is three orders of magnitude lower.