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.

Automated driving has received a broad of attentions from the academia and industry, since it is effective to greatly reduce the severity of potential traffic accidents and achieve the ultimate automobile safety and comfort. This paper presents an optimal model-based trajectory following architecture for highly automated vehicle in its driving tasks such as automated guidance or lane keeping, which includes a velocity-planning module, a steering controller and a velocity-tracking controller. The velocity-planning module considering the optimal time-consuming and passenger comforts simultaneously could generate a smooth velocity profile. The robust sliding mode control (SMC) steering controller with adaptive preview time strategy could not only track the target path well, but also avoid a big lateral acceleration occurred in its path-tracking progress due to a fuzzy-adaptive preview time mechanism introduced. In addition, an SMC controller with input–output linearisation method for velocity tracking is built and validated. Simulation results show this trajectory following architecture are effective and feasible for high automated driving vehicle, comparing with the Driver-in-the-Loop simulations performed by an experienced driver and novice driver, respectively. The simulation results demonstrate that the present trajectory following architecture could plan a satisfying longitudinal speed profile, track the target path well and safely when dealing with different road geometry structure, it ensures a good time efficiency and driving comfort simultaneously.

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.

Autonomous vehicle technology is greatly valued nowadays, and an active collision avoidance system is one of the key parts for autonomous driving. This study presents a comprehensive architecture of an active collision avoidance system for an autonomous vehicle, which is integrated with a decision-making module, a path-planning module, a lateral-path-following module and a fuzzy adaptive following module (longitudinal motion) to deal with potential hazards on a straight road or a curved road. In order to make the planned path for overtaking manoeuvres safer, an improved harmonic velocity potential approach for path planning is presented, which innovatively enhances the effect of an obstacle potential on a road by adding a scale term, so that it can generate a smooth path for a vehicle-overtaking manoeuvre. All the potentials which are used for vehicle lane keeping or lane changing are well designed. The lateral-path-following module is based on the constrained linear model predictive control approach, which ensures that the host vehicle can follow the planned path precisely. Furthermore, when the overtaking manoeuvre is not suitable, the fuzzy adaptive following module is utilized to ensure that the host vehicle can adaptively keep a safe distance from the preceding vehicle. Tactical decisions, such as overtaking, accelerating or decelerating, are determined by the decision-making module. Finally, several typical scenarios with low traffic on a straight road or a curved road are simulated to verify the effectiveness and the feasibility of the active collision avoidance system. The simulation results show that the host vehicle can make a successful collision avoidance manoeuvre without the intervention of a human driver in different situations.