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.

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.

During development of new vehicles finding correlation links between subjective assessments and objective metrics is an important part in the vehicle evaluation process. Studying different correlation links is of importance in order to make use of the gained knowledge in the front end of development, during testing and for new systems. Both subjective assessments using the rating scale of 1-10 from expert drivers and objective metrics from different tests measured by a steering robot were collected by standard testing protocols at an automotive manufacturer. This paper evaluates driver ratings and analyse correlations by using Regression Analysis and Neural Networks through a case study approach. Links have been identified and are compared to related research.QC 20131007iCOMS

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.