Coordinated Motion Planning for Heterogeneous Autonomous Vehicles Based on Driving Behavior Primitives

Abstract

Heterogeneous autonomous vehicle (HAV) coordinated motion planning must guide each vehicle out of the conflict zone based on the differences in vehicle platform characteristics. Decomposing complex driving tasks into primitives is an effective way to improve algorithm efficiency. Hence, the purpose of this paper is to complete the coordinated motion planning tasks through offline driving behavior primitive (DBP) library generation, online extension and selection of DBPs. The proposed algorithm applies dynamic movement primitives and singular value decomposition to learn driving behavior patterns from driving data, integrates them into a model-based optimization generation method as constraints, and builds a DBP library by fusing driving data and vehicle model. Based on the generated DBP library and primitive association probabilities learned from labeled driving segments via stochastic context-free grammar, the planning method completes the independent DBP extension of each vehicle in the conflict zone, generates an interaction DBP tree, and uses the mixed-integer linear programming algorithm to optimally select the primitives to be executed. This study demonstrates that the generated DBP library not only expands the types of primitives, but also distinguishes the characteristics of HAVs. We also present how to utilize the DBP libraries to obtain coordinated motion planning results with spatiotemporal information in the form of DBP extension and selection. The results obtained by real vehicle platforms and simulation show that the proposed method can accomplish coordinated motion planning tasks without relying on specific scene elements and highlight the unique motion characteristics of HAVs.