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Collision avoidance and stabilisation are two of the most crucial concerns when an autonomous vehicle finds itself in emergency situations, which usually occur in a short time horizon and require large actuator inputs, together with highly nonlinear tyre cornering response. In order to avoid collision while stabilising autonomous vehicle under dynamic driving situations at handling limits, this paper proposes a novel emergency steering control strategy based on hierarchical control architecture consisting of decision-making layer and motion control layer. In decision-making layer, a dynamic threat assessment model continuously evaluates the risk associated with collision and destabilisation, and a path planner based on kinematics and dynamics of vehicle system determines a collision-free path when it suddenly encounters emergency scenarios. In motion control layer, a lateral motion controller considering nonlinearity of tyre cornering response and unknown external disturbance is designed using tyre lateral force estimation-based backstepping sliding-mode control to track a collision-free path, and to ensure the robustness and stability of the closed-loop system. Both simulation and experiment results show that the proposed control scheme can effectively perform an emergency collision avoidance manoeuvre while maintaining the stability of autonomous vehicle in different running conditions. [ABSTRACT FROM AUTHOR]

In the past decade, several publications have shown that it is advisable to design an advanced driver assistance system using a shared control structure. This paper is concerned with the modelling and verification of an interactive steering control strategy between a driver and an active front steering (AFS) controller to investigate the complex interactions between human driver and an AFS system. Using game theory as a general framework, a more comprehensive mathematical model system of interactive steering control potentially applicable to explore human drivers’ behaviours in shared control of intelligent vehicles is presented and discussed in this paper. The effects of different information patterns, namely the open-loop pattern and the closed-loop feedback pattern on modelling shared steering control between driver and AFS have been investigated. Simulation and hardware-in-loop implementation results prove the validity of steering interactive modelling in different game information patterns. Specifically, the results show that, in the Nash equilibrium strategy situation, the driver and the AFS controller may become more rational and reasonable in the process of completing the same dynamic task in the closed-loop feedback information patterns compared to the open-loop ones; and the differences between feedback Nash and feedback Stackelberg may depend on the step size of discretisation. [ABSTRACT FROM AUTHOR]

As a significant active safety technology towards fully autonomous driving, driver-automation co-piloting provides a new thinking for enhancing vehicle active safety and relieving driver's workload. However, the preview pattern divergence between driver and steering assistance system (SAS) may give rise to serious steering conflicts, which reduces driver's faith in SAS, cause him/her to turn off it, and even result in fatal traffic accidents. Focusing on mimicking the driver's steering behaviour, this paper proposes a human-like steering assistance scheme by adaptively varying the preview time and assistance gain of the SAS controller. Driver's preview behaviour of the road curvature is numerically identified using simulator tests along a multi-curves road, based on which a nominal preview-scheduled driver model is established. Based on the nominal driver model, a co-piloting system featuring human-like preview behaviour and a time-varying assistance penalty is proposed to formulate the steering torque-overlay dynamics of both agents in path-tracking. Next, a linear parameter varying (LPV)/H∞ controller satisfying pole placement is integrated to ensure the robustness and stability within the entire parameter space. This enables the steering assistance scheme to have a scheduled preview pattern like a human driver. Results of driving simulator experiment indicate that the proposed steering assistance scheme is able to mitigate driver-SAS conflicts and improve driver's path-tracking performance. [ABSTRACT FROM AUTHOR]