This paper presents a shared control driver assistance system based on the driving intention identification and situation assessment to avoid obstacles. A constrained linear-time-varying model predictive controller (LTV-MPC) is designed to follow the obstacle-avoidance path which is obtained by artificial potential method in real time. A human driver's driving intention and the desired maneuver are recognized by the inductive multi-label classification with unlabeled data (iMLCU) approach that is trained based on the lateral offset and lateral velocity to the road center line. In addition, the situation assessment of collision risk is represented by the time to collision (TTC) and the performance evaluation is designed according to lateral deviation. All of them are employed for the design of the shared control fuzzy controller. The cooperative coefficient, denoting the control authority between the controller and a human driver, is determined by three fuzzy controllers in different conditions, which are the consistent, the advanced inconsistent, and the lagged inconsistent fuzzy controller, respectively. More importantly, there are two scenarios studies are provided to verify the proposed system. The results prove that the shared control driver assistance system can successfully help drivers to avoid obstacles and obtains great vehicle stability performance in different scenarios.

This paper proposes a novel driver-automation shared control based on a potential-field-driven model predictive controller (PF-MPC) and a two-layer fuzzy strategy (TLFS) to address driver-automation conflicts and control authority allocation issues. The PF-MPC approach based on the driver-vehicle model is introduced to deal with obstacles avoidance and driver-automation conflicts. The potential field is constructed to evaluate the driving risk by considering the driving environment and vehicle states, meanwhile, it is also involved in the optimized objective in the PF-MPC controller for obstacle avoidance. The tuning weight is designed to adjust the trade-off between the motion planning-related cost and driver-related cost to reduce driver-automation conflicts. To further alleviate the conflict and control authority allocation between the human driver and PF-MPC controller, the TLFS for shared control is designed based on the evaluation of the driving risk level and conflict situation, and the values of the tuning weight and cooperative coefficient are determined using the fuzzy control method. Moreover, comparative studies are conducted to verify the driving safety and conflict management performance of the proposed shared control method on a straight road and a curvy road. The results show that the proposed shared control method can help drivers avoid obstacles safely and alleviate the driver-automation conflicts in different driving conditions.

This paper presents a hierarchical trajectory following framework for SAE-level 2/3 automatic driving by convex optimisation, which aims for daily common driving manoeuvres with low traffics on the highway, such as free driving, adaptively following and overtaking. More specifically, it includes several combined functionalities of lane keeping, adaptive following, active obstacle avoidance and speed adaptation. The trajectory-planning module would generate the optimal path and speed profiles for the structure road (e.g. highway) with different geometric shapes, by using the natural cubic spline and convex optimisation approach. While modules of trajectory following or adaptive following are realised by a linearised time-varying model predictive control method. It is expected that the proposed framework could improve the driving safety, as well as the driving efficiency and driving comforts simultaneously. Thus, typical scenarios are designed to verify the effectiveness of the proposed trajectory following framework. Simulation results demonstrate that it could successfully deal with daily manoeuvres on a straight or curved highway without human driver's interventions.