An optimal hierarchical framework of the trajectory following by convex optimisation for highly automated driving vehicles. Issue 9 (2nd September 2019)

• Record Type: Journal Article
• Authors: Cao, Haotian; Zhao, Song; Song, Xiaolin; Bao, Shan; Li, Mingjun; Huang, Zhi; Hu, Chuan
• Is Part Of: Vehicle system dynamics. Volume 57:Issue 9(2019)
• Page Start: 1287
• Page End: 1317
• Publication Date: 2019-09-02
• Subjects: Trajectory planning -- convex optimisation -- linearised MPC -- nonlinear vehicle dynamics -- automated driving; Motor vehicles -- Dynamics -- Periodicals; Electronic journals; 629.231
• DOI: 10.1080/00423114.2018.1497185
• Languages: English
• ISSNs: 0042-3114

Abstract

ABSTRACT: This paper presents a hierarchical trajectory following framework for SAE-level 2/3 automatic driving by convex optimisation, which aims for daily common driving manoeuvres with low traffics on the highway, such as free driving, adaptively following and overtaking. More specifically, it includes several combined functionalities of lane keeping, adaptive following, active obstacle avoidance and speed adaptation. The trajectory-planning module would generate the optimal path and speed profiles for the structure road (e.g. highway) with different geometric shapes, by using the natural cubic spline and convex optimisation approach. While modules of trajectory following or adaptive following are realised by a linearised time-varying model predictive control method. It is expected that the proposed framework could improve the driving safety, as well as the driving efficiency and driving comforts simultaneously. Thus, typical scenarios are designed to verify the effectiveness of the proposed trajectory following framework. Simulation results demonstrate that it could successfully deal with daily manoeuvres on a straight or curved highway without human driver's interventions.