The Cooperative Sorting Strategy for Connected and Automated Vehicle Platoons - Online Processing A* Algorithm

Abstract

Along with the fast development of urbanization, the optimization of transportation plays an increasingly important role for solving traffic jam problem. Based on the assumption of fully connected and automated vehicles(CAVs), the optimal control and design of CAVs is one of the most effective ways for improving traffic efficiency as sorted CAVs platoons could help optimise the capacity of intersections. This research focus on optimizing the sequential actions of vehicle platoons for this given aim state. With the help of the perception and localization, multi-line vehicle platoons are abstracted into the permutation. The optimization object is the length of the trajectory from start permutation to the target permutation. A* algorithm wrote in Python could perform better with the help of the hash table and PyPy3. However, the time complexity problem still exists, which blocks it from extensive applications. Model-free Reinforcement Learning (RL) method suffers from the large searching space and low sampling efficiency a lot and Mento Carlo Tree Search (MCTS) lacks suitable criteria. In this essay, online processing A* (OPA*) algorithm is original promoted for solving time complexity problem with the sacrification of the optimality. The comparison and evaluation of OPA* and A* methods mainly focus on the performance and time consumption. OPA* could give stable and scalable results which make it possible for industrial usage.