An Intelligent Path Planning Scheme of Autonomous Vehicles Platoon Using Deep Reinforcement Learning on Network Edge

Abstract

Recent advancements in Intelligent Transportation Systems suggest that the roads will gradually be filled with autonomous vehicles that are able to drive themselves while communicating with each other and the infrastructure. As a representative driving pattern of autonomous vehicles, the platooning technology has great potential for reducing transport costs by lowering fuel consumption and increasing traffic efficiency. In this paper, to improve the driving efficiency of autonomous vehicular platoon in terms of fuel consumption, a path planning scheme is envisioned using deep reinforcement learning on the network edge node. At first, the system model of autonomous vehicles platooning is given on the common highway. Next, a joint optimization problem is developed considering the task deadline and fuel consumption of each vehicle in the platoon. After that, a path determination strategy employing deep reinforcement learning is designed for the platoon. To make the readers readily follow, a case study is also presented with instantiated parameters. Numerical results shows that our proposed model could significantly reduce the fuel consumption of vehicle platoons while ensuring their task deadlines.

Edge-based platoon control

“…The feasibility of platooning in 5G/MEC has been experimentally proven already, e.g., by Lekidis and Bouali [14], who provide a full library of VNFs for platooning and show that URLLC and the C-V2X connectivity can be effectively leveraged to reduce latency for platooning applications. Accordingly, there are already studies available on how to leverage edge network resources to enforce machine learning algorithms, with the aim of optimizing cost and performance of platooning [15]. These work assume that platooning requires very low latency and paths to edge resources have to be shortened as much as possible.…”

A review on key challenges in intelligent vehicles: Safety and driver‐oriented features

“…In this regard, Gao et al [98] proposed a model based on shock wave perception and back propagation neural network perception, which can predict the queue length of waiting vehicles at signalized intersections in real time under mixed traffic conditions. In order to improve the driving efficiency of IVs in the environment of fuel consumption, Chen et al [99] proposed a queue path planning strategy based on deep reinforcement learning of network edge nodes, considering the joint optimization problem of task duration and vehicle fuel consumption. Hao et al [100] proposed a framework combining driving state recognition with queue operation and risk prediction to reduce the interference caused by driving state jitter, so as to improve the evaluation speed, efficiency and fuel economy of multi queue system.…”

Decision-Making Strategy on Highway for Autonomous Vehicles Using Deep Reinforcement Learning

“…The duration of one episode is 100s, and the simulation frequency is 20 Hz. The initial velocity of the surrounding vehicles is randomly chosen from [20,23] m/s, and their behaviors are manipulated by IDM and MOBIL. The next section will discuss these two models in detail.…”

“…The relevant control performance is better than the conventional RL methods in these two findings. Furthermore, the authors in [20,21] considered not only path planning but also the fuel consumption for autonomous vehicles. The related algorithm is deep Q-learning (DQL), and it was proven to accomplish these two-driving missions suitably.…”