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.

This paper presents a novel shared control concept for lane keeping assist (LKA) systems of intelligent vehicles. The core idea is to combine system perception with robust control so that the proposed strategy can successfully share the control authority between human drivers and the LKA system. This shared control strategy is composed of two parts, namely an operational part and a tactical part. Two local optimal-based controllers with two predefined objectives (i.e., lane keeping and conflict management) are designed in the operational part. The control supervisor in the tactical part aims to provide a decision-making signal which allows for a smooth transition between two local controllers. The control design is based on a human-in-the-loop vehicle system to improve the mutual driver-automation understanding, thus reducing or avoiding the conflict. The closed-loop stability of the whole driver-vehicle system can be rigorously guaranteed using the Lyapunov stability argument. In particular, the control design is formulated as an LMI optimization which can be easily solved with numerical solvers. The effectiveness of the proposed shared control method is clearly demonstrated through various hardware experiments with human drivers.

This paper addresses the driver-automation shared driving control for lane keeping and obstacle avoidance of automated vehicles in highway traffic. The proposed shared control framework is established from a novel cooperative trajectory planning algorithm and a fuzzy steering controller. Based on polynomial functions, the cooperative trajectory planning is formulated by judiciously exploiting the information on the maneuver decision, the conflict management and the driver monitoring. As a result, the planned trajectory of the vehicle is continuously adapted according to the driver's actions and intentions. By means of Lyapunov stability arguments, sufficient conditions in terms of linear matrix inequalities are given to design a Takagi-Sugeno fuzzy model-based controller. This robust steering controller provides a necessary assistive torque to track the vehicle planned trajectory. The new shared driving control framework allows reducing effectively the driver-automation conflict issue while offering the driver more freedom to swerve within a predefined lane. The advantages of the proposed approach are evaluated using both objective and subjective results, experimentally obtained from several human drivers and an advanced interactive dynamic driving simulator.

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.

The control of electric-power-assisted steering (EPAS) systems is a challenging problem due to multiple objectives and the need for several pieces of information to implement the control. The control objectives are to generate assist torque with fast responses to driver's torque commands, ensure system stability, attenuate vibrations, transmit the road information to the driver, and improve the steering wheel returnability and free-control performance. The control must also be robust to modeling errors and parameter uncertainties. To achieve these objectives, a new control strategy is introduced in this paper. A reference model is used to generate an ideal motor angle that can guarantee the desired performance, and then, a sliding-mode control is used to track the desired motor angle. This reference model is built using a dynamic mechanical EPAS model, which is driven by the driver torque, the road reaction torque, and the desired assist torque. To implement the reference model with a minimum of sensors, a sliding-mode observer with unknown inputs and robust differentiators are employed to estimate the driver torque, the road reaction torque, and the system's states. With the proposed control strategy, there is no need for different algorithms, rules for switching between these algorithms, or fine-tuning of several parameters. In addition, our strategy improves system performance and robustness and reduces costs. The simulation results show that the proposed control structure can satisfy the desired performance.

This paper provides a new solution for path following control of autonomous ground vehicles. H₂ control problem is considered to attenuate the effect of the road curvature disturbance. To this end, we formulate a standard model from the road-vehicle dynamics, the a priori knowledge on the road curvature, and the path following specifications. This standard model is then represented in a Takagi-Sugeno fuzzy form to deal with the time-varying nature of the vehicle speed. Based on a static output feedback scheme, the proposed method allows avoiding expensive vehicle sensors while keeping the simplest control structure for real-time implementation. The concept of D-stability is exploited using Lyapunov stability arguments to improve the transient behaviors of the closed-loop vehicle system. In particular, the physical upper and lower bounds of the vehicle acceleration are explicitly considered in the design procedure via a parameter-dependent Lyapunov function to reduce drastically the design conservatism. The proposed H₂ design conditions are expressed in terms of linear matrix inequalities (LMIs) with a single line search parameter. The effectiveness of the new path following control method is clearly demonstrated with both theoretical illustrations and hardware experiments under real-world driving situations.