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.

A direct yaw moment control (DYC) for energy-efficiency and a DYC for stability of electric vehicles (EVs) are proposed. The DYC for energy-efficiency is active during non-safety-critical cornering manoeuvres to improve the energy-efficiency of EVs. The DYC for stability is active during safety-critical manoeuvres to keep the vehicle stable. A combination of the DYC for energy-efficiency and the DYC for stability is studied. A stability judgement based on the yaw rate and slip angle is designed for evaluating the criticality of the vehicle's working state. A switching principle for alternating between the DYC for energy-efficiency and the DYC for stability is designed. During non-safety-critical cornering manoeuvres, it is shown that the DYC for energy efficiency can save considerable percentage of energy compared to both equal torque driving and the DYC for stability. During cornering manoeuvres containing both non-safety-critical parts and safety-critical parts, the simulation results in this work show that the combination of the DYC for energy-efficiency and the DYC for stability can give 12% to 18% energy savings compared to the DYC for stability only for the vehicle and manoeuvres studied.

This literature survey explores the domain of remote driving of road vehicles within autonomous vehicles, focusing on challenges and state-of-the-art solutions related to driving feedback, latency, support control, as well as remote driving platform and real applications. The advancement towards Level-5 autonomy faces challenges, including sensor reliability and diverse scenario feasibility. Currently, remote driving is identified as vital for commercialization, however, it comes with challenges like low situational awareness, latency, and a lack of comprehensive feedback mechanisms. Solutions proposed include enhancing visual feedback, developing haptic feedback, employing prediction techniques, and use control methods to support driver. This paper reviews the existing literature on remote driving in these fields, revealing research gaps and areas for future studies. Additionally, this paper reviews the industry applications of remote driving and shows the state-of-art use cases.

This review addresses efforts made within the field of vehicle dynamics to contribute to the energy efficient operation of road and rail vehicles. Selected investigations of the research community to develop technology solutions to reduce energy consumption are presented. The study explores the impact and potential of relatively mature technologies such as regenerative braking, but also recent research directions that are seeking to develop solutions such as regenerative suspensions, a concept common to both transport modes. Specifically for road vehicles, the study includes rear wheel steering, camber control and torque vectoring, and for rail vehicles active steering and light-weighting. Operationally, there would appear to be great potential for rail vehicles in the wider adoption of connected driver advisory systems, and automatic train control systems, including the application of machine learning techniques to optimise train speed trajectories across a route. Similarly, for road vehicles, predictive and cooperative eco-driving strategies show potential for significant energy savings for connected autonomous vehicles.