Research on multidimensional evaluation of tracking control strategies for self-driving vehicles

Abstract

Performance evaluation is a necessary stage in development of tracking control strategy of autonomous vehicle system, which determines the scope of application and promotes further improvement. At present, most of the tracking control strategies include performance evaluation. However, performance evaluation criteria differ from work to work, lacking comprehensive evaluation system. This article proposes a multidimensional integrated tracking control evaluation system based on subjective and objective weighting, taking into account the tracking accuracy, driving stability, and ride comfort. Through the co-simulation of CarSim and Simulink, qualitative analysis and quantitative analysis based on multidimensional evaluation system of five coupled longitudinal and lateral control strategies (lateral: pure pursuit feedforward control, dynamic-model-based optimal curvature control (dynamic feedforward control), Stanley feedback control, kinematics feedback control, and dynamic feedback control; longitudinal: the incremental proportion–integration–differentiation control) under typical operating conditions are carried out to analyze the operating range and robustness of each tracking control strategy. The results show that the Stanley tracking control strategy and the dynamic feedback tracking control strategy have a wide range of applications and robustness. The consistency of qualitative analysis results and the quantitative analysis results verify the validity and feasibility of the evaluation system.