Data-Enabled Human-Machine Cooperative Driving Decoupled from Various Driver Steering Characteristics and Vehicle Dynamics

2024-01-2333

04/09/2024

Features
Event
WCX SAE World Congress Experience
Authors Abstract
Content
Human driving behavior's inherent variability, randomness, individual differences, and dynamic vehicle-road situations give human-machine cooperative (HMC) driving considerable uncertainty, which affects the applicability and effectiveness of HMC control in complex scenes. To overcome this challenge, we present a novel data-enabled game output regulation approach for HMC driving. Firstly, a global human-vehicle-road (HVR) model is established considering the varied driver's steering characteristic parameters, such as delay time, preview time, and steering gain, as well as the uncertainty of tire cornering stiffness and variable road curvature disturbance. The robust output regulation theory has been employed to ensure the global DVR system's closed-loop stability, asymptotic tracking, and disturbance rejection, even with an unknown driver's internal state. Secondly, an interactive shared steering controller has been designed to provide personalized driving assistance. Two control subsystems, active front-wheel steering (AFS) and active rear-wheel steering (ARS) systems, are emulated as a dynamic non-zero-sum game to explore a more flexible balance between the dual objectives of path-tracking accuracy and vehicle stability. Finally, the control policy iterative equalities of the AFS and ARS systems are constructed utilizing the coupled game Riccati equation and Kronecker product. Adaptive dynamic programming (ADP) has been employed to iteratively update and learn the optimal shared strategy without relying on accurate knowledge of driver steering characteristics and vehicle dynamics. Simulations demonstrate the convergence and adaptability of the proposed strategy in different road scenarios. In addition, our shared control scheme can effectively assist drivers with different characteristics to achieve ideal steering control performance and reduce their driving workload.
Meta TagsDetails
DOI
https://doi.org/10.4271/2024-01-2333
Pages
11
Citation
Guo, H., Shi, W., Zhang, J., and Liu, J., "Data-Enabled Human-Machine Cooperative Driving Decoupled from Various Driver Steering Characteristics and Vehicle Dynamics," SAE Technical Paper 2024-01-2333, 2024, https://doi.org/10.4271/2024-01-2333.
Additional Details
Publisher
Published
Apr 09
Product Code
2024-01-2333
Content Type
Technical Paper
Language
English