Development of a CAV-based Intersection Control System and Corridor Level Impact Assessment

TL;DR

This paper introduces a decentralized, signal-free intersection control system for connected and autonomous vehicles that uses pixel reservation and deep reinforcement learning to reduce delays and improve safety across multiple intersections.

Contribution

It develops a novel DSCLS model combining pixel reservation and DRL, demonstrating significant delay reductions and safety improvements in simulation compared to traditional control systems.

Findings

Reduces delay by up to 50% in high volume conditions

Improves travel time, fuel consumption, and emissions

Outperforms conventional traffic control methods in simulations

Abstract

This paper presents a signal-free intersection control system for CAVs by combination of a pixel reservation algorithm and a Deep Reinforcement Learning (DRL) decision-making logic, followed by a corridor-level impact assessment of the proposed model. The pixel reservation algorithm detects potential colliding maneuvers and the DRL logic optimizes vehicles' movements to avoid collision and minimize the overall delay at the intersection. The proposed control system is called Decentralized Sparse Coordination System (DSCLS) since each vehicle has its own control logic and interacts with other vehicles in coordinated states only. Due to the chain impact of taking random actions in the DRL's training course, the trained model can deal with unprecedented volume conditions, which poses the main challenge in intersection management. The performance of the developed model is compared with conventional and CAV-based control systems, including fixed traffic lights, actuated traffic lights, and the Longest Queue First (LQF) control system under three volume regimes in a corridor of four intersections in VISSIM software. The simulation result revealed that the proposed model reduces delay by 50%, 29%, and 23% in moderate, high, and extreme volume regimes compared to the other CAV-based control system. Improvements in travel time, fuel consumption, emission, and Surrogate Safety Measures (SSM) are also noticeable.