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.
翻译:本文通过像素保留算法和深度强化学习决策逻辑相结合,并随后对拟议模式进行走廊一级影响评估,为CAV提供无信号的交叉控制系统。像素保留算法检测潜在的碰撞操作和DRL逻辑优化了车辆的机动性,以避免碰撞和尽量减少交叉点的总体延误。拟议的控制系统称为分散式螺旋协调系统(DSCLS),因为每辆车辆都有自己的控制逻辑,并只在协调状态下与其他车辆互动。由于在DRL培训课程中随机行动产生的连锁影响,经过培训的模型可以应对前所未有的数量条件,这对交叉管理构成主要挑战。开发模型的性能与常规和基于CAVA的控制系统相比,包括固定的交通灯、开动式交通灯和最长的Queue Fir(LQF)控制系统,在VISSIM软件中以四个交叉点为基础的走廊中的三个容量系统下。模拟结果显示,拟议的模型将延迟减少50%、29%、23 %的总量,而高容量的CSAS、高容量、高容量和高容量的燃料控制系统。