Autonomous driving and electrification make over-actuation technologies more feasible and advantageous. Integrating autonomous driving with over-actuation allows for the effective use of their respective strengths, e.g., for studying energy and time optimal control. To model AVs, several vehicle coordinate systems have been used, e.g., Cartesian, Frenet and spatial coordinates. The present study aims to achieve energy and time optimal control of autonomous vehicles by using Frenet frame modelling and over-actuation. This study enhances the existing Frenetbased modeling by incorporating double-track dynamic vehicle models and torque vectoring. The problem is formulated in an optimal control framework, with carefully designed cost function terms and constraints. Two control strategies are examined, one for minimising travel time and the other for jointly optimising energy consumption and travel time. The results indicate that by considering both energy and time in the formulation, the energy consumption can be apparently reduced while the travel time is merely slightly increased.

The advent of autonomous vehicles (AVs) enables the utilisation of by-wire technologies, which can be employed for various purposes, including four-wheel steering (4WS). The integration of 4WS into AVs has the potential to enhance vehicle performance in terms of manoeuvrability, stability and path tracking. This research aims to investigate the application of 4WS in the trajectory tracking of AVs in critical driving conditions. The trajectory tracking problem is formulated within the framework of model predictive control (MPC). The performance of 4WS is examined through comparative analyses with two alternative steering configurations, namely, active front steering (AFS) and a combination of active front and rear steering (AFS+ARS). This evaluation is performed by using three types of reference trajectories, which are generated based on the AFS, AFS+ARS and 4WS configurations. The findings indicate that the 4WS configuration enhances vehicle safety, passing velocity and tracking accuracy.

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.

Trajectory planning is a crucial component of autonomous driving systems. However, using simple vehicle models for trajectory planning may result in unrealistic reference trajectories, especially in critical driving conditions, endangering the safe driving of autonomous vehicles. This study explores the effect of model complexity on the trajectory planning performance of autonomous vehicles in critical driving scenarios. Five trajectory planners of various levels of model complexity, including Planner STK (single-track kinematic model), Planner STDL (single-track dynamic vehicle model with a linear tyre model), Planner STD (single-track dynamic vehicle model with a simplified Pacejka tyre model), Planner DTB (double-track vehicle model with the brush tyre model), and Planner DTMlt (double-track vehicle model with load transfer consideration and the Pacejka tyre model), are designed. The trajectory planners are formulated as optimal control problems, where constraints for obstacle avoidance, yaw stability and the physical limits on vehicle actuators are explicitly considered. These planners are assessed in two severe driving manoeuvres, i.e. the double-lane change and single-lane change manoeuvres. Results indicate that Planner DTMlt outperforms DTB with higher passing velocity as well as smaller peak yaw rate and sideslip angle, and that Planners STD, STDL and STK are not suitable for use in these critical driving scenarios.

Accurate trajectory planning is crucial for safe driving of autonomous vehicles in critical driving manoeuvres. However, using simple models for trajectory planning in such conditions may result in unrealistic or unsafe trajectories. This paper evaluates various combinations of models, i.e., over-actuation and vehicle and tyre models, for trajectory planning in critical conditions. The trajectory planning is formulated as an optimal control problem, where constraints concerning safety and actuator physical limits are explicitly considered. The results show that, by exploring torque vectoring, the most advanced planner (with the Pacejka tyre model and load transfer) outperformed the others with higher passing velocities and safer trajectories. Moreover, torque vectoring contributed to more consistent passing velocities during trajectory planning and tracking as well as safer trajectories, even with a simpler Dugoff tyre model. Furthermore, the two planners that do not consider torque vectoring had inconsistent or lower passing velocities and less safe trajectories.

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.

Steering feedback is one essential aspect to provide real world information, and can influence driving performance during remote driving. In this work, the classical feedback models based on physical characteristics (Physical Model) and modular characteristics (Modular Model) of the steering system are constructed separately, and the influences of it on the remote drivers are studied. Objective and subjective measurement methods are separately used for evaluating the performance of the feedback models. In the subjective assessment, a multi-level assessment method is used for studying the influence of steering models on driver’s intuitive feeling. In the objective assessment, lane following accuracy, steering reversal rates, vehicle speed, time consumption, and throttle engagement are studied for different feedback models and scenarios. Moreover, the human biological information of electroencephalogram and heart rate variability are measured for studying the workload differences. The results showed that the physical model gave drivers a better steering characteristic feel and confidence in remote driving while the modular model could provide better real world feel. Returnability was an important parameter in remote driving, and the level of feedback force and returnability speed could be lower in remote driving compared to real car driving. It was also found that drivers had a higher workload in remote driving compared to real car driving.

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.

This paper explores active camber for path following and yaw stability control of over-actuated autonomous electric vehicles (AEVs). The camber effect on tyre force is modelled with a modified Dugoff tyre model, where the influence of tyre slip on camber stiffness is considered. Additionally, a nonlinear vehicle model including the longitudinal, lateral and yaw motion of the vehicle and the rotational motion of the wheels is utilised. The control problem of the AEVs is formulated with model predictive control, where both actuator- and safety-related constraints are considered. Comparative studies show that with four-wheel camber control the path following and yaw stability performance of the AEV can be considerably improved.

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 characterised and compared under three different driving manoeuvres. For MHE-based algorithms, the effects of horizon length and optimisation techniques on the computational efficiency and accuracy are also investigated.