Active safety systems are of significant importance for autonomous vehicles operating in safety-critical situations like an obstacle-avoidance manoeuvre with high vehicle speed or poor road condition. However, a conventional electronic stability control system, may not always yield desired path following and yaw stability performance in such circumstances merely through brake intervention. This paper pursues a detailed investigation on utilising model predictive control (MPC) and torque vectoring for path following and yaw stability control of over-actuated autonomous electric vehicles (AEVs) in dangerous double lane change manoeuvre. The control problem of the AEV is formulated based on MPC by utilising active front steering and torque vectoring, and constraints are imposed explicitly on yaw rate and sideslip angle to ensure yaw stability. Four MPC-based controllers are designed based on double-track vehicle models. Specifically, they include two one-level controllers, i.e. one with torque vectoring and one with equal torque allocation, and two two-level controllers, i.e. one with optimisation-based torque allocation and one with rule-based allocation. These controllers are assessed extensively, with respect to passing velocity, tracking accuracy, tyre utilisation and robustness. The effect of horizon length on the control performance and computational efficiency is also investigated.

Active safety systems must be used to manipulate the dynamics of autonomous vehicles to ensure safety. To this end, accurate vehicle information, such as the longitudinal and lateral velocities, is crucial. Measuring these states, however, can be expensive, and the measurements can be polluted by noise. The available solutions often resort to Bayesian filters, such as the Kalman filter, but can be vulnerable and erroneous when the underlying assumptions do not hold. With its clear merits in handling nonlinearities and uncertainties, moving horizon estimation (MHE) can potentially solve the problem and is thus studied for vehicle state estimation. This paper designs an unscented Kalman filter, standard MHE, modified MHE, and recursive least squares MHE to estimate critical vehicle states, respectively. All the estimators are formulated based upon a highly nonlinear vehicle model that is shown to be locally observable. The convergence rate, accuracy, and robustness of the four estimation algorithms are comprehensively characterized and compared under three different driving maneuvres. For MHE-based algorithms, the effects of horizon length and optimization techniques on the computational efficiency and accuracy are also investigated.

Active safety systems contribute significantly to the safe driving of autonomous vehicles in hazardous circumstances. However, conventional active safety systems that mainly depend on braking intervention may not yield the desired vehicle behaviour in critical situations where the tyre forces tend to saturate. Over-actuation through individually cambering the wheels provides a possibility of overcoming this difficulty, as extra lateral tyre forces can potentially be produced. This paper presents active camber for improving the path following and yaw stability performance of over-actuated autonomous electric vehicles (AEVs). With a modified Dugoff tyre model, the camber effect on the lateral tyre force is modelled as an effective linear component. The modified tyre model, together with a double-track vehicle model, is utilised for active camber of the AEVs. The camber controller is developed in the framework of model predictive control (MPC), including both actuator- and safety-related constraints. The camber controller is investigated at different camber rates and road friction levels, in terms of path following, yaw stability and vehicle velocity gain.

Advanced active safety systems play a crucial role in ensuring the safe driving of vehicles in critical conditions such as an obstacle avoidance manoeuvre. However, conventional techniques relying mainly on braking interventions may not result in the desired vehicle response in such situations. Over-actuation through the control of individual motion actuators could potentially improve the safety performance of vehicles. This study evaluates various configurations of motion actuators for path following and yaw stability control of vehicles in critical driving scenarios. The configurations include active front steering (S), active front steering + torque vectoring (ST), active front steering + active camber (SC) and active front steering + torque vectoring + active camber (STC). The evaluation is achieved based on a nonlinear model predictive control formulation, which considers yaw stability and the physical limits of motion actuators. This problem formulation uses a double-track vehicle model, combined with the Dugoff tyre model and its variant with the camber effect, to model the vehicle dynamics. The actuator configurations are evaluated regarding the passing velocity, tracking accuracy, safety distance and robustness to reference trajectory variation. The results indicate that the integrated control of STC performs the best among all the four configurations while S performs the worst. Furthermore, SC is generally superior to ST.