Motion sickness is a common phenomenon, with close to two-thirds of the population experiencing it in their lifetime. With the advent of automated vehicles in the market, it is anticipated to become an even greater problem as the passengers face a lack of predictability of motion and loss of control over the vehicle. This could nullify the host of possible benefits that automated vehicles propose to offer, and therefore affect their acceptance among potential users.

It is well known that the nauseogenicity of imposed motion is dependent on the frequency content of endured accelerations, with low-frequency accelerations being the primary contributor. This thesis presents a motion planning algorithm targeted towards minimization of motion sickness among passengers of automated vehicles, through targeted reduction of low-frequency accelerations. A Deep Reinforcement Learning (DRL) framework was utilised along with the design of a custom environment and a reward function which incorporates a measure of nauseogenicity of the planned trajectories. The frequency shaping effect of the reward function was evaluated by comparing against a DRL agent trained to optimize general motion comfort described by total acceleration energy. It was found that the nauseogenicity was reduced by 9.6% with the proposed DRL agent.

Further, on-road trials were performed with human drivers to establish a benchmark of driving comfort. The performance of the DRL agent was compared to human drivers as well as against an optimization-based motion planner that computationally maximizes the reward function. The DRL planner displayed comparable performance to the human drivers, and was within 10 to 15% of the discomfort levels of the optimization-based planner for a range of travel times. Meanwhile, the DRL planner offered notable improvements in computational efficiency, taking 1-2 ms to generate a sub-optimal trajectory, as opposed to approximately 5 s as taken by the optimization-based planner.

Among the trends that are going to shape the automotive industry in the coming years, autonomous vehicles stand out as having the potential to completely change the automotive industry as we know it. One of the critical tasks in this framework includes robust execution of the steering control action to maintain a pre-defined path.

The main shortcomings of the state of the art steering controllers are controller robustness, cross-comparison and performance validation. To address this aspect, firstly, this study focuses on implementing a sensor model that geometrically measures the lateral and the heading error of the vehicle with respect to the path. Secondly, the focus was to implement existing lateral controllers like Stanley, Path Control with Preview (PCwP), Linear Quadratic Regulator (LQR), Immersion and Invariance (II), Passivity Based Control (PBC) and evaluate path-tracking performance.

The Sliding Mode Control (SMC) methodology has proven effective in dealing with complex dynamical systems affected by disturbances, uncertainties and unmodeled dynamics. However, the application of SMC and its algorithms to lateral control in vehicles is not effectively analysed. Finally, this master thesis includes a novel design of three variants of Sliding Mode Control; namely Sliding Mode Control with Super Twisting Algorithm, Modified Super Twisting Algorithm and Non-singular Terminal Modified Super Twisting algorithm. These algorithms were then tested against external disturbances such as localisation error, cross-wind, and parametric variations. This thesis successfully illustrates in detail, the aspects involved in path-tracking control. Results effectively suggest a betterment of the novel steering controllers designed, over the previously existing Sliding Mode Control Super Twisting algorithm and other bench-marking controllers at higher speeds. The work ends with vital conclusions
and recommendations for future researchers in this domain to further increase robustness and achieve better performance.