Driving safety zone model oriented motion planning framework for autonomous truck platooning

Hong Wang; Lei Song; Zichun Wei; Liming Peng; Jun Li; Ehsan Hashemi

doi:10.1016/j.aap.2023.107225

Driving safety zone model oriented motion planning framework for autonomous truck platooning

Accid Anal Prev. 2023 Dec:193:107225. doi: 10.1016/j.aap.2023.107225. Epub 2023 Sep 22.

Authors

Hong Wang¹, Lei Song², Zichun Wei³, Liming Peng⁴, Jun Li⁵, Ehsan Hashemi⁶

Affiliations

¹ School of Vehicle and Mobility, Tsinghua University, Beijing 10081, China. Electronic address: hong_wang@tsinghua.edu.cn.
² School of Vehicle and Mobility, Tsinghua University, Beijing 10081, China. Electronic address: songl22@mails.tsinghua.edu.cn.
³ Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China. Electronic address: zichun.wei@connect.polyu.hk.
⁴ Department of Vehicle Engineering, Hefei University of Technology, Hefei 230009, China. Electronic address: alaric.peng@gmail.com.
⁵ School of Vehicle and Mobility, Tsinghua University, Beijing 10081, China. Electronic address: lijun1958@tsinghua.edu.cn.
⁶ Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada. Electronic address: ehashemi@ualberta.ca.

PMID: 37742439
DOI: 10.1016/j.aap.2023.107225

Abstract

A driving-safety-zone-model-oriented motion planning framework (DSZMF) is proposed for autonomous platoons in heterogeneous driving environments with complex driving behaviors and interactions between human-driven and autonomous vehicles. As an extension of the responsibility-sensitive-safety (RSS) model, the driving safety zone model ensures that autonomous truck platoons adhere to explicit and implicit traffic rules as rational traffic participants. It consists of three zones created by safe distances and artificial potential field (APF), namely the restricted zone, the coordinated zone, and pre-cautionary zone. The Rational Traffic Participant (RTP) module is created by using a Finite State Machine (FSM) to provide an optimized platooning behavioral strategy based on the dynamic states of surrounding vehicles. Furthermore, the distributed model predictive controllers are utilized for motion planning, while the H infinity controller is developed to maintain the string stability of the autonomous platoon. The proposed DSZMF generates behavioral decisions by thoroughly considering the driving safety zone model, string stability, and multiple vehicle dynamics constraints. Finally, three critical scenarios are co-simulated for case studies, and the simulation results demonstrate that the DSZMF improves the safe time integration rate over the existing MCF by 18.9%, 11.1%, and 11.6% in three scenarios, respectively. In addition, DSZMF increases the minimum longitudinal and lateral Time to Collision (TTC) values to reduce collision risks. The case studies validate the efficacy of the proposed method for safety assurance and collaborative control of the autonomous platoon.

Keywords: Autonomous platooning; Driving safety zone; Motion control; Safety assurance.