Adaptive authority allocation-based driver-automation shared control for autonomous vehicles

2021 ◽  
Vol 160 ◽  
pp. 106301
Author(s):  
Ming Yue ◽  
Chao Fang ◽  
Hongzhi Zhang ◽  
Jinyong Shangguan
Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4647
Author(s):  
Anh-Tu Nguyen ◽  
Jagat Jyoti Rath ◽  
Chen Lv ◽  
Thierry-Marie Guerra ◽  
Jimmy Lauber

This paper proposes a new haptic shared control concept between the human driver and the automation for lane keeping in semi-autonomous vehicles. Based on the principle of human-machine interaction during lane keeping, the level of cooperativeness for completion of driving task is introduced. Using the proposed human-machine cooperative status along with the driver workload, the required level of haptic authority is determined according to the driver’s performance characteristics. Then, a time-varying assistance factor is developed to modulate the assistance torque, which is designed from an integrated driver-in-the-loop vehicle model taking into account the yaw-slip dynamics, the steering dynamics, and the human driver dynamics. To deal with the time-varying nature of both the assistance factor and the vehicle speed involved in the driver-in-the-loop vehicle model, a new ℓ∞ linear parameter varying control technique is proposed. The predefined specifications of the driver-vehicle system are guaranteed using Lyapunov stability theory. The proposed haptic shared control method is validated under various driving tests conducted with high-fidelity simulations. Extensive performance evaluations are performed to highlight the effectiveness of the new method in terms of driver-automation conflict management.


Author(s):  
Huateng Wu ◽  
Hanbing Wei ◽  
Zheng Liu ◽  
Jin Xu

Since the large-scale application of fully autonomous vehicles is difficult to be commercialized in the short term, human-vehicle shared control (HVSC) is a promising technique. To implement the control authority allocation and observe the driver characteristic, it is essential to develop an efficient HVSC dynamic model with the driver’s neuromuscular characteristic (NMS). To further our previous research, a simplified HVSC dynamic model is proposed in this paper. This model simplifies the non-critical NMS parameters such as muscle spindle feedback, which has no significant feedback effect while retaining essential NMS characteristics such as stretch reflection and intrinsic properties. The model consists of a model predictive controller (MPC) coupled with a driver NMS model and a 2 DOF vehicle model. The stability is proved by Lyapunov stability theory. Moreover, a field experiment was conducted for validation of the model. The V-Box is utilized to measure the vehicle’s state signals, such as steering wheel angle and pedal stroke. Subsequently, the adaptive genetic algorithm (AGA) is employed to identify the model parameters based on the experimental results. The comparison between the experiment and the model output shows that the proposed model can accurately represent the driver’s NMS characteristics and vehicle dynamic parameters. This paper will serve as a theoretical basis for the control authority allocation for L3 class autonomous vehicles.


Author(s):  
Joseph G. Walters ◽  
Xiaolin Meng ◽  
Chang Xu ◽  
Hao (Julia) Jing ◽  
Stuart Marsh
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