Adaptive Authority Allocation Approach for Shared Steering Control System

Li, Xueyun; Wang, Yiping; Su, Chuqi; Gong, Xinle; Huang, Jin; Yang, Dengke

doi:10.1109/tits.2022.3157737

Cited by 18 publications

(5 citation statements)

References 33 publications

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“…According to the relationship between the human and the autonomous controller, the system framework can be divided into parallel system framework and tandem system framework. 18 Based on the parallel system framework, the system framework is proposed, as shown in Figure 1.…”

Section: Mathematical Model For Ssc Systemmentioning

confidence: 99%

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Human-centered shared steering control system design for obstacle avoidance scenarios

Gong

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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To minimize the human–machine conflict in the shared steering control system and protect the driving intention of the driver, a human-centered shared steering control system is proposed in obstacle avoidance scenarios. First, a framework is proposed based on the parallel shared steering control system framework, which includes the trajectory reconstruction, driver model, authority allocation model, autonomous controller, and vehicle. And mathematical model of the sub-modules in the shared steering control system is constructed. Then, the trajectory reconstruction method considering the driving intention of the driver is designed. A trigger mechanism with human–machine conflict as input is designed for trajectory reconstruction. More specifically, to protect the driving intention of the driver, the steering input of the driver is used as the constraint to reconstruct the trajectory. Finally, the effectiveness of the proposed method is verified by simulation. The simulation results show that the proposed method can effectively reduce human–machine conflict while the driving intention of the driver is protected. However, the trajectory tracking performance and lateral stability of the vehicle may be may sacrificed within a limited extent.

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Section: Mathematical Model For Ssc Systemmentioning

confidence: 99%

“… e y and e φ are used to determine the variables J 1 and J 2 , respectively. J 1 and J 2 can be expressed as 18…”

Section: Mathematical Model For Ssc Systemmentioning

confidence: 99%

Human-centered shared steering control system design for obstacle avoidance scenarios

Gong

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…The driver input and system input are added with certain weights and then transmitted to the SBW system. In this SSC structure, the human driver and automated system co-exist in the control loop and are equally important (6,11,12). Zhang et al (13) proposed a method of equally distributing the control authority of the driver and automated system and achieved good lane-keeping results.…”

Section: Literature Reviewmentioning

confidence: 99%

Human–Machine Shared Steering Control Under High-Speed Emergency Obstacle Avoidance Scenarios

Wang,

Gong,

et al. 2023

Transportation Research Record: Journal of the Transportation R

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The allocation of controlling and driving authority is an important and difficult aspect of human–machine shared steering control (SSC). This paper addresses the SSC problem in high-speed emergency obstacle avoidance scenarios. A parallel SSC framework containing a dynamic driving authority allocation model and a path tracking controller is developed, where the human driver and controller can control the vehicle simultaneously. First, fuzzy logic is adopted in the SSC framework to actively adjust the driving authority between the human driver and the controller. The driver steering state and path tracking error are considered to reduce the negative impact of a driver’s mis-operation and weaken any human–machine conflict. Subsequently, the path tracking controller in the proposed SSC system is designed based on a nonlinear vehicle lateral model to improve the accuracy of the controller, particularly when the vehicle is facing large lateral acceleration. To address the nonlinear control problem, the Udwadia–Kalaba approach is employed and the Lyapunov stability of the controller is proved. Finally, the effectiveness of the proposed SSC system is proved through simulation results, which show that the vehicle has excellent path tracking performance in high-speed obstacle avoidance scenarios. In addition, the system can resolve the human–machine conflict problem.

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“…As a scheme that can optimize the solution of control problems under multiobjective conditions, MPC has been applied to the parallel steering coordination control strategy [17]. In [18], a parallel steering coordination control strategy based on MPC showed better performance relative to the rule-based design strategy under tracking accuracy, human-machine conflict, driver steering load and controller steering load index. In the above shared steering control strategy, the multi-objective steering coordination control strategy needs to build a complex human-machine steering system model, which is difficult to adapt to multi-dimensional vehicle state input.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective parallel human-machine steering coordination control strategy of intelligent vehicles path tracking based on deep reinforcement learning

Wang

Feng

et al. 2023

Preprint

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In the parallel steering coordination control strategy for path tracking, it is difficult to match the current driver steering model using the fixed parameters with the actual driver, and the designed steering coordination control strategy under a single objective and simple conditions is difficult to adapt to the multi-dimensional state variables’ input. In this paper, we propose a deep reinforcement learning algorithm-based multi-objective parallel human-machine steering coordination strategy for path tracking considering driver mis-operation and external disturbance. Firstly, the driver steering mathematical model is constructed based on the driver preview characteristics and steering delay response, and the driver characteristic parameters are fitted after collecting the actual driver driving data. Secondly, considering that the vehicle is susceptible to the influence of external disturbances during the driving process, the Tube MPC (Tube Model Predictive Control) based path tracking steering controller is designed based on vehicle system dynamics error model. After verifying that the driver steering model meets the driver steering operation characteristics, DQN (Deep Q-network), DDPG (Deep Deterministic Policy Gradient) and TD3 (Twin Delayed Deep Deterministic Policy Gradient) deep reinforcement learning algorithms are utilized to design a multi-objective parallel steering coordination strategy which satisfies the multi-dimensional state variables’ input of the vehicle. Finally, the tracking accuracy, lateral safety, human-machine conflict and driver steering load evaluation index are designed in different driver operation states and different road environments, and the performance of the parallel steering coordination control strategies with different deep reinforcement learning algorithms and fuzzy algorithms are compared by simulations and hardware in the loop experiments. The results show that the parallel steering collaborative strategy based on deep reinforcement learning algorithm can more effectively assist the driver in tracking the target path under lateral wind interference and driver misoperation, and the TD3 based coordination control strategy has better overall performance.

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Adaptive Authority Allocation Approach for Shared Steering Control System

Cited by 18 publications

References 33 publications

Human-centered shared steering control system design for obstacle avoidance scenarios

Human-centered shared steering control system design for obstacle avoidance scenarios

Human–Machine Shared Steering Control Under High-Speed Emergency Obstacle Avoidance Scenarios

Multi-objective parallel human-machine steering coordination control strategy of intelligent vehicles path tracking based on deep reinforcement learning

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