Shared Steering Control Research Articles

Driver–Automated Cooperation Driving Authority Optimization Framework for Shared Steering Control

In this paper, we introduce the preview-follower theory for modeling the trajectory-tracking controller of an automated system using model predictive control (MPC). The primary contribution of this research lies in enhancing tracking accuracy and driving safety when the driver and automated system share similar driving intentions, while also enabling a rapid transfer of driving authority to the human driver in cases of differing intentions. To verify the effectiveness of the proposed driving authority optimization framework, both simulation and driver-in-the-loop experiments were conducted under conditions of consistent and inconsistent driving intentions between the human driver and the autonomous driving system. The results of both experiments demonstrated that the proposed shared steering cooperative control and driving authority optimization framework not only significantly improves vehicle tracking accuracy but also promptly aligns with the driver’s intentions.

A Robust Cooperative Game Theory-Based Human–Machine Shared Steering Control Framework

A Robust Cooperative Game Theory-Based Human–Machine Shared Steering Control Framework

Human-Machine Authority Allocation in Indirect Cooperative Shared Steering Control With TD3 Reinforcement Learning

Human-Machine Authority Allocation in Indirect Cooperative Shared Steering Control With TD3 Reinforcement Learning

Toward Consumer Acceptance of Cooperative Driving Systems: A Human-Centered Shared Steering Control Approach Within a Hierarchical Framework

Toward Consumer Acceptance of Cooperative Driving Systems: A Human-Centered Shared Steering Control Approach Within a Hierarchical Framework

Human-Vehicle Shared Steering Control for Obstacle Avoidance: A Reference-Free Approach With Reinforcement Learning

Human-Vehicle Shared Steering Control for Obstacle Avoidance: A Reference-Free Approach With Reinforcement Learning

The indirect shared steering control under double loop structure of driver and automation

The indirect shared steering control under double loop structure of driver and automation

Authority Allocation Strategy for Shared Steering Control Considering Human-Machine Mutual Trust Level

Authority Allocation Strategy for Shared Steering Control Considering Human-Machine Mutual Trust Level

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

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.

Human–Machine Shared Steering Control for Vehicle Lane Changing Using Adaptive Game Strategy

Human–machine shared control of intelligent vehicles is considered an important technology during the industrial application of autonomous driving systems. Among the engineering practices in driver assistance systems, shared steering control is one of the important applications for the human–machine interaction. However, how to deal with human–machine conflicts during emergency scenarios is the main challenge for the controller’s design. Most shared control approaches usually generate machine-oriented results without enough attention to the driver’s reaction. By taking the human driver and machine system as two intelligent agents, this paper proposes a game-based control scheme to achieve a dynamic authority allocation during the lane changing maneuver. Based on the modeling of predicted trajectories of the human driver, a human-intention-based shared steering control is designed to achieve dynamic Nash game equilibrium. Moreover, a human-oriented shared steering mechanism is employed to not only benefit from automated machine assistance, but also make full play of human contributions. Using quantitative comparative analysis in lane changing scenarios with different human–machine conflicts, a better performance by considering both driving comfort and safety is achieved.

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

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.

Adaptive authority allocation for shared steering control considering social behaviours: a hybrid fuzzy approach with game-theoretical vehicle interaction model

In a typical traffic scenario, automated vehicles are required to interact with surrounding traffic participants, e.g. human-driven vehicles, pedestrians, etc. In this paper, a novel adaptive authority allocation strategy considering the social behaviours of surrounding vehicles is proposed for the shared steering control (SSC) of automated vehicles. First, a Koopman-based potential-field-driven distributed model predictive control (K-PF-DMPC) method is proposed for the modelling of vehicle interaction to describe surrounding vehicle's social behaviour. This method effectively deals with the nonlinearity embedded in the non-cooperative game-based vehicle interaction model, so that the analytical form of Nash equilibrium can be derived for a fast online solution. Then, the SSC system of the automated vehicle is implemented by a weighted summation method. To balance the driving performance and intervention degree while considering the surrounding vehicles' social behaviours, a hybrid fuzzy strategy (HFS) is proposed for allocating the control authority between the driver and automation. The value of authority allocation coefficient is calculated by fusing the outputs from two shared fuzzy controllers, i.e. the enhanced and weakened shared fuzzy controllers, in accordance to the belief of the surrounding vehicle's type. Several numerical simulations and driver-in-the-loop simulator experiments are conducted for validation. Results show that the proposed strategy can provide appropriate steering intervention to the driver owing to the consideration of the surrounding vehicle's social behaviour and obtains the best driver-automation collaboration performance compared with the comparative groups.

Driver and automation cooperation approach for share steering control system

The interferences and drivers' maloperations are important factors affecting vehicle driving safety. This paper investigates the problem of authority allocation to weaken the impact of interferences and drivers’ maloperations on the shared steering control system. Based on the parallel framework of the shared steering control system, an extended framework including the upper level and the lower lever is proposed. The lower lever is used to realize the shared steering control, which includes the driver model, trajectory tracking controller and vehicle model. To improve the robustness of the system, the uncertainty of vehicle dynamics parameters is considered in the trajectory tracking controller, including tire cornering stiffness and longitudinal velocity. The upper level is used to calculate the authority level of the driver and controller required by the lower lever, which consists of an authority dynamic allocation model and an authority allocation decision strategy. The role of the authority dynamic allocation model is to calculate the reference allocation level of the driver and controller dynamically. When the driver's operation and vehicle working states are trustworthy, the reference allocation levels of the driver and controller will be followed. Conversely, a decision result will be gained by the authority allocation decision strategy to replace the reference allocation levels, and the sum of the authority levels of the driver and the automation will not be fixed as 1. The simulation results show that the proposed approach can effectively improve vehicle driving safety, anti-interference and reliability, and can effectively reduce the impact of crosswind and driver's maloperation on vehicle safety, and alleviate the driver's operation load.

Shared Steering Control With Predictive Risk Field Enabled by Digital Twin

A core issue of safe human-machine cooperative driving lies in dynamic assessment of the rapidly evolving driving risks. Given that, this paper proposes a shared controller for safe and human-friendly cooperative driving based on predictive risk assessment enabled by Digital Twin technologies. In the digital world, we create a fine-grained digital replica of the driving scene comprising historical motions of vehicles, as well as roadway geometries and topologies. On the basis of that, spatial-temporal interactive features are obtained with a deep learning-based model and subsequently decoded to predict future trajectories of each target vehicle in the neighborhood of the ego-vehicle. In the physical world, the predicted trajectories of neighboring vehicles are integrated into the risk distribution to construct predictive risk fields. A novel shared controller in the framework of multi-objective MPC is designed to minimize the driving risk while matching driver's commands, so that safe cooperative driving is achieved in a smooth and minimal-intervention manner. The results of driver-in-the-loop simulation experiments demonstrate the enabling role of the Digital Twin in improving the assessment of risk in highly dynamic scenes through taking the motion trends of dynamic agents into account. The results also show the superiority of the Digital Twin-based shared controller in terms of implementing cooperation in time while honoring the driver's commands whenever possible.

Adaptive Authority Allocation Approach for Shared Steering Control System

To improve the reliability of the shared steering control system, and weaken the influence of uncertain driver behavior on driving safety, an extended shared steering control system with an authority adaptive allocation model is proposed. The system framework consists of upper-level and lower-level. The upper-level is an adaptive authority allocation model, which is composed of a reference model and an authority dynamic allocation model. The reference authority level can be calculated by the reference model, which is used as the reference of the authority dynamic allocation model. The authority levels of the autonomous controller and driver are gained by the adaptive authority allocation model. The lower level is the human-machine shared steering control, which includes the driver and autonomous controller. Using the authority level obtained by the upper-level, the lower-level can realize the coordinated control of human and machine. The effectiveness of the proposed approach is verified by simulation. The results show that relative to the numerical function model, the proposed approach can effectively weaken the impact of driver misoperation and poor preview time, the trajectory tracking ability and handling stability are improved significantly, and the human-machine conflict and the workload of the controller are weakened.

Driver-automation shared steering control considering driver neuromuscular delay characteristics based on stackelberg game

To promote the intelligent vehicle safety and reduce the driver steering workload, stackelberg game theory is adopted to design the shared steering control strategy that takes the driver neuromuscular delay characteristics into account. First, a shared steering control framework with adjustable driving weight is proposed, and a coupling interaction model considering the driver neuromuscular delay characteristics is constructed by using the stackelberg game theory. Moreover, the driver-automation optimal control strategy is deduced theoretically when the game equilibrium is reached. Finally, simulation and virtual driving tests are carried out to verify the superiority of the proposed method. The results illustrate that the raised method can enhance the vehicle safety with low driving weight intervention, and it can achieve better auxiliary effect with less control cost. In addition, the driver-in-the-loop test results show that the proposed strategy can achieve better performance in assisting drivers with low driving skills.

Human-Machine Shared Steering Control for Vehicle Lane Keeping Systems via a Fuzzy Observer-Based Event-Triggered Method

This paper is concerned with the human-machine shared control issue for vehicle lane keeping systems via a new fuzzy observer-based event-triggered method. In order to capture system nonlinear and uncertain characteristics such as nonlinear tire dynamics, varying velocity and driver behavioral uncertainties, Takagi-Sugeno fuzzy approach is employed to model the global driver-vehicle-road system. After system modeling, the fuzzy observer-based control structure is considered because a full states information is not available in practical driving environment. Then, most existing human-machine shared control methods are based on the periodic sampling communication mechanism. However, since the network bandwidth is limited, the above mechanism may cause oversampling and communication congestion. Thus, an adaptive event-triggered mechanism is introduced between the observer and the controller to mitigate the communication burden and improve the bandwidth utilization. Based on Lyapunov functional theory, a set of sufficient conditions are given to calculate desired human-machine shared controllers. Finally, simulation tests are implemented on Matlab/Simulink-CarSim platform and simulation results illustrate that the proposed method can achieve a favorable improvement in the lane keeping capability, the driver handling comfort and the network bandwidth utilization.

Variable Impedance-based Human-machine Interaction Method Using Reinforcement Learning for Shared Steering Control of Intelligent Vehicle

Variable Impedance-based Human-machine Interaction Method Using Reinforcement Learning for Shared Steering Control of Intelligent Vehicle

Driver-Automated Cooperation Driving Authority Optimization Frame for the Shared Steering Control

Driver-Automated Cooperation Driving Authority Optimization Frame for the Shared Steering Control

Effect of Fixed and sEMG-Based Adaptive Shared Steering Control on Distracted Driver Behavior.

Driver distraction is a well-known cause for traffic collisions worldwide. Studies have indicated that shared steering control, which actively provides haptic guidance torque on the steering wheel, effectively improves the performance of distracted drivers. Recently, adaptive shared steering control based on the forearm muscle activity of the driver has been developed, although its effect on distracted driver behavior remains unclear. To this end, a high-fidelity driving simulator experiment was conducted involving 18 participants performing double lane change tasks. The experimental conditions comprised two driver states: attentive and distracted. Under each condition, evaluations were performed on three types of haptic guidance: none (manual), fixed authority, and adaptive authority based on feedback from the forearm surface electromyography of the driver. Evaluation results indicated that, for both attentive and distracted drivers, haptic guidance with adaptive authority yielded lower driver workload and reduced lane departure risk than manual driving and fixed authority. Moreover, there was a tendency for distracted drivers to reduce grip strength on the steering wheel to follow the haptic guidance with fixed authority, resulting in a relatively shorter double lane change duration.

Shared steering control: How strong and how prompt should the intervention be for a better driving experience?

The lane keeping assistance system, a representative advanced driver assistance system, comprises a shared control that cooperates with the driver to achieve a common goal. The steering experience of the driver may vary significantly depending on the auto-steering control strategy of the system. In this study, we examined the driving experience with various steering control strategies. Nine control strategies (three torque amounts × three deviations in starting control) were established as prototypes. Eighteen drivers participated in the evaluation of each strategy in a highway environment on a driving simulator. A two-way repeated measure ANOVA was used to assess the effects of the system. Both the objective measures (standard deviation of lane position, steering reversal rate, and root mean square of lateral speed) and subjective measures (pleasure and arousal of emotion, trust, disturbance, and satisfaction) were evaluated and analyzed. The results showed that a torque amount of 3 Nm evoked feelings of high disturbance and negative emotional responses. A deviation in starting control (DEV) of 0.80 m yielded unstable lane keeping performances and evoked negative effects on pleasure, trust, and satisfaction. A regression model for the driver satisfaction recommended a torque of 2.32 Nm and a DEV of 0.27 m as the optimal design parameters. This proposed strategy is expected to improve the experience design of lateral semi-autonomous vehicles.