This study highlights the challenge of motion sickness (MS) in autonomous vehicles (AVs), providing a comprehensive review of assessing, predicting, and preventing this issue with a special focus on vehicle dynamics and control-based approaches. Unlike previous studies, this review bridges the gap between MS prediction models and vehicle dynamics-based mitigation strategies by presenting an integrated perspective. Effective mitigation requires accurate and reliable prediction. In this context, motion-based prediction approaches, recognised for their practicality, cost-effectiveness, and promising results, are examined in detail with particular focus on ISO-based methods and sensory conflict theory-based models. The importance of identifying MS triggers and validating these models experimentally is also emphasised, alongside recent trends in customised approaches addressing individual variability in MS susceptibility. The study then investigates mitigation strategies centred on vehicle dynamics and control systems, due to their potential for directly controlling motion triggers, calling for tailored and integrated approaches. Furthermore, the critical role of trajectory planning and tracking algorithms in mitigating MS is reviewed, emphasising their potential through optimal control and the incorporation of MS metrics into cost functions. Additionally, integrating trajectory planning with active chassis systems is identified as a promising direction for reducing MS. The study concludes by underscoring the importance of optimised, personalised, integrated and connected vehicle dynamics and control-based methods to effectively mitigate MS in AVs. Finally, a future horizons approach, supported by a vision roadmap, is introduced as a means to address current challenges, define research directions, and ultimately advance the adoption of AVs with minimum MS.

Teleoperation is considered as a viable option to control fully automated vehicles (AVs) of Level 4 and 5 in special conditions. However, by bringing the remote drivers in the loop, their driving experience should be realistic to secure safe and comfortable remote control. Therefore, the remote control tower should be designed such that remote drivers receive high quality cues regarding the vehicle state and the driving environment. In this direction, the steering feedback could be manipulated to provide feedback to the remote drivers regarding how the vehicle reacts to their commands. However, until now, it is unclear how the remote drivers' steering feel could impact occupant's motion comfort. This paper focuses on exploring how the driver feel in remote (RD) and normal driving (ND) are related with occupants' motion comfort. More specifically, different types of steering feedback controllers are applied in (a) the steering system of a Research Concept Vehicle-model E (RCV-E) and (b) the steering system of a remote control tower. An experiment was performed to assess driver feel when the RCV-E is normally and remotely driven. Subjective assessment and objective metrics are employed to assess drivers' feel and occupants' motion comfort in both remote and normal driving scenarios. The results illustrate that motion sickness and ride comfort are dominated by steering velocity variations in remote driving, while throttle input variations dominate in normal driving. The results demonstrate that motion sickness and steering velocity increase both around 25% from normal to remote driving.

Remote driving plays an essential role in coordinating automated vehicles in some challenging situations. Due to the changed driving environment, the experiences and behaviors of remote drivers would undergo some changes compared to conventional drivers. To study this, a continuous real-life and remote driving experiment is conducted under different driving conditions. In addition, the effect of steering force feedback (SFF) on the driving experience is also investigated. In order to achieve this, three types of SFF modes are compared. According to the results, no SFF significantly worsens the driving experience in both remote and real-life driving. Additionally, less force and returnability on steering wheel are needed in remote driving, and the steering force amplitude appears to influence the steering velocity of remote drivers. Furthermore, there is an increase in lane following deviation during remote driving. Remote drivers are also prone to driving at lower speeds and have a higher steering reversal rate. They also give larger steering angle inputs when crossing the cones in a slalom manoeuvre and cause the car to experience larger lateral acceleration. These findings provide indications on how to design SFF and how driving behavior and experience change in remote driving.