Shared Steering Control Research Articles

Shared steering control for human–machine co-driving system with multiple factors

To guarantee the best driving experience and status of the driver, further improve the driving safety of human–machine co-driving vehicles, reduce the conflict between the driver and controller, and weaken the impact of the driver's uncertain behaviours, a human–machine co-driving system with a dynamic weight allocation model is designed. First, a human–machine co-driving system is built with a fixed allocation coefficient. Evaluation indexes based on the degree of participation of the driver and driving safety are proposed. Subsequently, several important influencing factors affecting weight allocation are analysed, including driving characteristics, driving states, and changes in the controller parameters. The results show that the impact of these factors can be weakened by the designed system. However, a good driving experience of the driver cannot be guaranteed. In addition, a conflict between the driver and controller still exists. Next, a model of dynamic weight allocation considering the volatility of the driving characteristics and states of the driver is proposed. Further, the human–machine co-driving system is modified by considering the influence of changes in controller parameters and external interference. Finally, the validity of the designed model of dynamic weight allocation and the modified system were verified by simulation. The results show that the modified system could improve the driving experience and safety better than a system with a fixed allocation coefficient. In addition, the modified system has a better anti-interference ability and lower sensitivity to interference.

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

To avoid crashes caused by driver error in avoiding obstacles, a driver-centered steering assist controller with an adaptive authority allocation system is proposed for cooperative control purposes. To begin with, a concept of space collision risk (SCR) is introduced creatively for assessing the vehicle’s safety status in which the relative distance and the relative angle between the vehicle and the obstacle are taken into consideration. Meanwhile, the SCR-based authority allocation system is addressed to allocate steering authorities between the human driver and the assist controller adaptively to reduce SCR to zero as soon as possible. After that, an autonomous steering controller based on the model predictive control (MPC) technique and the artificial potential field (APF) method, considering not only the vehicle stability constraints but also roads and obstacles constraints, is developed to aid the human driver when necessary. In the end, the proposed algorithm is simulated in the CarSim-Simulink co-simulation platform under a series of typical scenarios, which shows the feasibility and effectiveness of the presented shared steering control method.

Shared Steering Control for Lane Keeping and Obstacle Avoidance Based on Multi-Objective MPC

This paper presents a shared steering control framework for lane keeping and obstacle avoidance based on multi-objective model predictive control. One of the control objectives is to track the reference trajectory, which is updated continuously by the trajectory planning module; whereas the other is to track the driver’s current steering command, so as to consider the driver’s intention. By adding the two control objectives to the cost function of an MPC shared controller, a smooth combination of the commands of the driver and the automation can be achieved through the optimization. The authority of the driver and the automation is allocated by adjusting the weights of the objective terms in the cost function, which is determined by the proposed situation assessment method considering the longitudinal and lateral risks simultaneously. The results of the CarSim-Matlab/Simulink joint simulations show that the proposed shared controller can assist the driver to complete the tasks of lane keeping and obstacle avoidance smoothly while maintaining a good level of vehicle stability.

Robust steering assistance control for tracking large-curvature path considering uncertainties of driver’s steering behavior

A human-machine shared steering control is presented in this paper for tracking large-curvature path, considering uncertainties of driver’s steering characteristics. A driver-vehicle-road (DVR) model is proposed in which uncertain characteristic parameters are defined to describe the human driver’s steering behaviors in tracking large-curvature path. Then the radial basis function neural network (RBF) is used to estimate parameters of different drivers’ characteristics and to obtain the boundaries of these parameters. Parameter uncertainties of the driver’s steering characteristics and time-varying vehicle speed of the DVR model are handled with the Takagi-Sugeno (T-S) fuzzy logic. And these parameter uncertainties are considered in the design of the shared steering controller. Then based on the DVR model, a T-S fuzzy full-order dynamic compensator with D-pole assignment is designed to assist driver’s steering for tracking path with large curvature. Simulation results show that the proposed controller can provide individual levels of steering assistance in path following according to driver’s proficiency, and can improve driving comfort significantly.

Adaptive Driver-Automation Shared Steering Control via Forearm Surface Electromyography Measurement

Shared steering control has been developed to reduce driver workload while keeping the driver in the control loop. A driver could integrate visual sensory information from the road ahead and haptic sensory information from the steering wheel to achieve better driving performance. Previous studies suggest that, compared with adaptive automation authority, fixed automation authority is not always appropriate with respect to human factors. This paper focuses on designing an adaptive shared steering control system via sEMG (surface electromyography) measurement from the forearm of the driver, and evaluates the effect of the system on driver behavior during a double lane change task. The shared steering control was achieved through a haptic guidance system which provided active assistance torque on the steering wheel. Ten subjects participated in a high-fidelity driving simulator experiment. Two types of adaptive algorithms were investigated: haptic guidance decreases when driver grip strength increases (HG-Decrease), and haptic guidance increases when driver grip strength increases (HG-Increase). These two algorithms were compared to manual driving and two levels of fixed authority haptic guidance, for a total of five experimental conditions. Evaluation of the driving systems was based on two sets of dependent variables: objective measures of driver behavior and subjective measures of driver workload. The results indicate that the adaptive authority of HG-Decrease yielded lower driver workload and reduced the lane departure risk compared to manual driving and fixed authority haptic guidance.

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

Due to the critical defects of techniques in fully autonomous vehicles, man-machine cooperative driving is still of great significance in today&#x02BC s transportation system. Unlike the previous shared control structure, this paper introduces a double loop structure which is applied to indirect shared steering control between driver and automation. In contrast to the tandem indirect shared control, the parallel indirect shared control put the authority allocation system of steering angle into the framework to allocate the corresponding weighting coefficients reasonably and output the final desired steering angle according to the current deviation of vehicle and the accuracy of steering angles. Besides, the active disturbance rejection controller &#x0028 ADRC &#x0029 is also added in the frame in order to track the desired steering angle fleetly and accurately as well as restrain the internal and external disturbances effectively which including the steering friction torque, wind speed and ground interference etc. Eventually, we validated the advantages of double loop framework through three sets of double lane change and slalom experiments, respectively. Exactly as we expected, the simulation results show that the double loop structure can effectively reduce the lateral displacement error caused by the driver or the controller, significantly improve the tracking precision and keep great performance in trajectory tracking characteristics when driving errors occur in one of driver and controller.

Haptic shared steering control with an adaptive level of authority based on time-to-line crossing

Traditional driver-automation interaction trades control over the vehicle back and forth between driver and automation. Haptic shared control offers an alternative by continuously sharing the control through torques on the steering wheel and pedals. When designing additional feedback torques, part of the design choice lies in the stiffness around the neutral steering point: also called the Level of Haptic Authority (LoHA), which is usually static and tuned to balance safety benefits (better at high LoHA) with conflicts torques in case of different intentions between automation and driver (higher conflict torques with increased LoHA). In this paper we explore the idea of situation-adaptive LoHA: in this case during lane-keeping by changing the LoHA based on time to lane crossing (TLC). Consequently, when safety margins are high (e.g., when driving on a wide road) the LoHA is low, but the LoHA would only increase when safety margins decrease. We propose two alternative design approaches to apply the LoHA: symmetrically and asymmetrically (i.e., only increase of LoHA in the direction of the low TLC). We compared these design in an explorative driving simulator study (n=14) to driving with two static LoHA designs (low and high). We found that compared to the high LoHA controller, both adaptive LoHA controllers designs resulted in similar safety margins, but at decreased conflict torques. Hence, a TLC-based adaptive LoHA controller seems to be an effective approach to mitigate conflicts while maintaining the safety benefits associated with HSC.

Trust and Acceptance of Adaptive and Conventional Collision Avoidance Systems

A simulator driving experiment was conducted to test the implementation of an adaptive driver support system for avoiding collisions caused by hazardous lane changes. Conventional (nonadaptive) collision avoidance systems with fixed function allocation might be effective from a safety perspective, but they are not always desirable from human factors perspective due to problems related to performance, trust, and acceptance. We therefore propose an adaptive collision avoidance system to provide support depending on the situation in consideration of the driver’s ability to handle the situation and the time to collision. Four driving modes were compared: no support, a haptic shared steering control system with an adaptive haptic authority based on time to collision, a cooperative steering control system that decoupled the driver’s steering input and controlled the vehicle’s direction temporarily, and an adaptive steering control system in which both haptic and cooperative control systems were integrated to provide different automated interventions at the steering wheel depending on the situation. The simulation participants were subjected to various potentially hazardous lane change maneuvers that provided significant differences in their abilities to perceive the risk and handle the situation. Compared to the no-support condition, all systems were more effective in accident prevention. The adaptive system was more effective than the haptic system and less effective than the cooperative system in terms of safety. However, driver performance, trust, and acceptability were significantly improved with the adaptive system compared to the nonadaptive systems.

Shared steering control combined with driving intention for vehicle obstacle avoidance

This paper presents a framework of shared steering control for vehicle obstacle avoidance, which is combined with the driver’s driving intention and risk assessment. The driving intention is recognized by a support vector machine model through the steering wheel angle, the relative lateral offset, and lateral velocity to the road center line. The elastic band theory is adopted to plan the obstacle avoidance path, which takes the driving intention and environment information into consideration. A linear model predictive control algorithm based on a bicycle vehicle model is used to realize path following. The risk assessment of current traffic situation is calculated by time to collision and the time required from the starting position of lane changing to the obstacle to collide. The shared steering control strategy is designed by the risk assessment and driving intention, simultaneously, so that the cooperative coefficient and the driving mode would be determined. In order to validate the framework proposed in this paper, the shared steering control system is tested on four obstacle avoidance scenarios, including the static and moving obstacles on a straight road and a curvy road. The results show that the shared steering control system could help the human driver avoid obstacles safely. Besides, both the path and steering wheel angle curve of the shared steering control system are smooth, and the vehicle stability performance also maintains well through a suitable shared strategy.

Sensor Reduction for Driver-Automation Shared Steering Control via an Adaptive Authority Allocation Strategy

This paper presents a new shared control method for lane keeping assist (LKA) systems of intelligent vehicles. The proposed method allows the LKA system to effectively share the control authority with a human driver by avoiding or minimizing the conflict situations between these two driving actors. To realize the shared control scheme, the unpredictable driver-automation interaction is explicitly taken into account in the control design via a fictive driver activity variable. This latter is judiciously introduced into the driver–road–vehicle system to represent the driver's need for assistance in accordance with his/her real-time driving activity. Using Lyapunov stability arguments, Takagi–Sugeno fuzzy model-based design conditions are derived to handle not only the time-varying driver activity variable, but also a large variation range of vehicle speed. Both simulation and hardware experiments are presented to demonstrate that the proposed control strategy together with a linear matrix inequality design formulation provide an effective tool to deal with the challenging shared steering control issue. In particular, a fuzzy output feedback control scheme is exploited to achieve the shared control goal without at least two important vehicle sensors. These physical sensors are widely employed in previous works to measure the lateral speed and the steering rate for the control design and real-time implementation. The successful results of this idea of sensor-reduction control has an obvious interest from practical viewpoint.

Predictive shared steering control for driver override in automated driving: A simulator study

Recent research efforts on automated driving reveal the need to address driver’s interaction with vehicle automation. This paper focuses on the design of an override mode for automated driving systems, a topic little studied in literature. In this paper, a shared control framework for driver’s override of automatic steering control is proposed. This framework formulates the control transfer between the driver and the system as a constrained optimization problem which is solved online by a model predictive controller. At a higher level in the framework, the system detects driver’s lane-change intention so that the low-level controller can actively assist the driver in a lane-change maneuver. The whole framework was evaluated in a user test based on a driving simulator. The test results show that the proposed shared control framework has the advantage of allowing the driver to regain control with ease while ensuring the smoothness of control transition.

Data-driven Shared Steering Control Design for Lane Keeping

In this paper, we propose a shared steering control strategy for semi-autonomous vehicles by taking into account the interaction between the driver and his/her vehicle. The copilot controller cooperates with the driver and help maintain the vehicle in the central position of a lane. Taking advantage of the small-gain theory, the design procedure does not rely on the perfect knowledge of the model and states for the driver. An adaptive dynamic programming method is introduced to develop a shared controller in real-time using online measurable input-output data from vehicle sensors. The efficacy of the proposed shared steering controller is demonstrated by computer-based simulations.

Driver-Automation Cooperative Approach for Shared Steering Control Under Multiple System Constraints: Design and Experiments

This paper addresses the shared lateral control between a human driver and a lane keeping assist system of intelligent vehicles for both lane keeping and obstacle avoidance. This control issue is very challenging in today's automotive industry due to the human–machine interaction involved in the control design. In this paper, we propose a new approach to consider such an interaction via a fictive driver activity parameter introduced into the road–vehicle system. Hence, the steering assistance actions can be computed according to the driver's real-time behaviors. The Takagi–Sugeno fuzzy control approach is proposed to deal with the time-varying driver activity parameter and vehicle speed. Especially, the concept of robust invariant set is exploited using Lyapunov arguments to handle theoretically both system state and control input limitations. Considering these system constraints in the control design procedure aims to improve the driver's safety and comfort. Experimental tests with a human driver and an advanced interactive dynamic driving simulator are conducted to show the effectiveness of the proposed method.

Shared Steering Control Using Safe Envelopes for Obstacle Avoidance and Vehicle Stability

Steer-by-wire technology enables vehicle safety systems to share control with a driver through augmentation of the driver's steering commands. Advances in sensing technologies empower these systems further with real-time information about the surrounding environment. Leveraging these advancements in vehicle actuation and sensing, the authors present a shared control framework for obstacle avoidance and stability control using two safe driving envelopes. One of these envelopes is defined by the vehicle handling limits, whereas the other is defined by spatial limitations imposed by lane boundaries and obstacles. A model predictive control (MPC) scheme determines at each time step if the current driver command allows for a safe vehicle trajectory within these two envelopes, intervening only when such a trajectory does not exist. In this way, the controller shares control with the driver in a minimally invasive manner while avoiding obstacles and preventing loss of control. The optimal control problem underlying the controller is inherently nonconvex but is solved as a set of convex problems allowing for reliable real-time implementation. This approach is validated on an experimental vehicle working with human drivers to negotiate obstacles in a low friction environment.

Effectiveness of a Risk Predictive Shared Steering Control Based on Potential Risk Prediction of Collision with Vulnerable Road Users

Elderly drivers are at particular risk for accidents due to age-related visual, cognitive, and physical impairments. This study proposes a risk predictive shared steering control based on potential risk prediction of collision with vulnerable road users, such as cyclists on urban roads. Under the driving simulator experiment, we investigated its acceptance and the effectiveness of providing the auditory alert and visual information, and implementing the shared steering control in the hidden risk situation.

Shared steering control between a human and an automation designed for low curvature road

In order to facilitate driving and prevent accidents due to lane departure, this paper focuses on the development of an assistance device for lane keeping of a passenger electric vehicle. The goal is to develop a shared control mode based on the anticipation of risk and the prediction of the most probable actions of the driver. The paper is mainly articulated around two axes: the cybernetic modelling of the driver in his task of lateral control of the vehicle and the design of a shared steering control using a Driver-Vehicle-Road (DVR) model. Since many vehicle/road interaction factors (such as road adhesion, aerodynamic forces) and actuator dynamics are very poorly known, the proposed work addresses shared control resulting from applying the linear-quadratic control (LQR) synthesis to the global model (DVR). Innovative criteria were used for assessing the time to lane crossing, the level of sharing between the driver and lateral assistance, as well as their cooperative or conflicting behaviour. All simulation tests are carried out on a 5.8 km track that has several pretty tight radius turns (up to 150 m). The implementation of LQR controller has been made for a longitudinal speed set at 75 km/h. The LQR is sufficient to keep the vehicle under control on a road with low radius curvature. Preliminary simulation results in MATLAB/Simulink are presented to explain the concept.

Shared Steering Control Between a Driver and an Automation: Stability in the Presence of Driver Behavior Uncertainty

This paper presents an advanced driver assistance system (ADAS) for lane keeping, together with an analysis of its performance and stability with respect to variations in driver behavior. The automotive ADAS proposed is designed to share control of the steering wheel with the driver in the best possible way. Its development was derived from an H2-Preview optimization control problem, which is based on a global driver–vehicle–road (DVR) system. The DVR model makes use of a cybernetic driver model to take into account any driver–vehicle interactions. Such a formulation allows 1) considering driver assistance cooperation criteria in the control synthesis, 2) improving the performance of the assistance as a cooperative copilot, and 3) analyzing the stability of the whole system in the presence of driver model uncertainty. The results have been experimentally validated with one participant using a fixed-base driving simulator. The developed assistance system improved lane-keeping performance and reduced the risk of a lane departure accident. Good results were obtained using several criteria for human–machine cooperation. Poor stability situations were successfully avoided due to the robustness of the whole system, in spite of a large range of driver model uncertainty.