Real-time monitoring of mental workload (MWL) is critical for designing adaptive human-machine systems. This study introduces a subject-independent EEG classifier trained on spectral power ratios (delta, theta, alpha, beta) from frontal, parietal, and occipital regions. Using a controlled arithmetic task with labeled difficulty levels (easy/hard), a Gaussian Naive Bayes model achieved 75.4% accuracy (LOSOCV) in distinguishing MWL states. Validated in a driving simulator, the model detected higher MWL in urban (overload) vs. rural (underload) scenarios (p < 0.05), aligning with NASA-TLX subjective ratings. Temporal analysis revealed declining MWL over time, reflecting cognitive adaptation, followed by a fatigue-driven rise in prolonged overload tasks. The framework eliminates the need for individualized calibration, offering a scalable solution for real-world applications like automotive safety and virtual reality. By bridging controlled lab settings and naturalistic environments, this work advances EEG-based MWL monitoring for adaptive systems in high-stakes domains like driving and aviation.

Remote driving, as a backup system for automated vehicles, can play a vital role in their commercialization. However, delay is one of the major challenges in the practical application of remote driving. It not only degrades the stability of remote driven vehicles (RDVs) but also introduces delayed driving feedback, such as motion cueing feedback, to remote drivers. This can result in an unpleasant driving experience. This study proposes a square root cubature Kalman filter-based predictor (SRCKP) to compensate for driving feedback delays in remote driving. The SRCKP reduces the limitations of both model-based and model-free predictors (MFPs). Additionally, this article presents an overshoot compensator to address the overshoot problem associated with traditional MFPs. Furthermore, a packet loss predictor (PLP) is designed to mitigate the influence of packet loss during data transmission. Both simulation and hardware-in-the-loop (HIL) experiments during comprehensive driving scenarios are conducted to verify the effectiveness and robustness of the proposed method. The findings indicate that, compared with MFPs, the SRCKP reduces the L₂-norm error by up to 81.2% in simulations and by up to 54.0% in HIL experiments for the best-case conditions.

Driverless multipurpose vehicles (DMVs) are an emerging vehicle concept for urban heavy-duty transport. However, little is known about their effect on urban road transport systems. Thus, the aim of this study is to analyse the total fleet energy consumption of DMVs for specific transport operations in urban logistics compared to heavy- duty battery and combustion vehicles. A novel electric vehicle routing problem was used to simulate in total 96 case-studies of operations with varying network and vehicle fleet properties. We found that the combustion vehicle fleets consumed significantly more energy for the same operation compared to the electric vehicle fleets. Although the DMV fleet and battery electric vehicle fleet showcased similar energy consumption for most case-studies, there were several operations where the DMV fleet consumed less energy and required a smaller fleet size. This study highlights the potential benefits of DMV fleets in urban logistics operations in terms of reducing total fleet energy consumption and fleet size.

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.

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.

Remote driving plays a vital role in coordinating automated vehicles in challenging situations. Data transmission latency, however, can cause several problems in remote driving. Firstly, it can degrade the performance of remote-controlled vehicles, evident in issues like lane-following deviation and vehicle stability. Additionally, the remote control tower's driving feedback is affected by delayed vehicle signals, leading to delayed driving experience. To address this, a model-free-based predictor is employed to compensate for the delay in remote driving. This approach does not require any dynamic model of the system and only needs tuning of two parameters to reduce communication delay. This study enhances the previous work by mitigating the amplitude of overshoot around peak points. It leverages the principle of the second-order derivative to predict the signal's peak time and uses it to address the predictor's overshoot issue. The effectiveness of the proposed method is validated using real car data from multiple participants in two scenarios, including Slalom and lane-following. Simulation results indicate that the proposed method can reduce prediction error by nearly 25% compared to previous works. Moreover, the solutions in this study are capable of managing not only delays in remote driving vehicles but also in traditional mechanical systems, such as CAN bus delays in conventional cars.

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.

Driving feedback is an important way of providing remote drivers with physical world information during teleoperation. In this study, a teleoperation experiment is conducted to explore how sound, vibration and motion-cueing feedback influence the drivers’ driving experience and behaviour. To this end, four types of driving feedback modes are used as variables to investigate this, including no feedback, motion-cueing feedback, sound and vibration feedback, and a combination of sound, vibration, and motion-cueing feedback. A prototype of teleoperation platform is first built, which includes a teleoperated vehicle and a driving station capable of generating sound, vibration, and motion-cueing feedback. Then, the scenario with disturbances is built to investigate how the driving behaviour changes under various driving feedback modes. Both subjective and objective assessments are used in this study. For driving experience, the driving feeling, such as presence feeling, road surface feeling, etc, are explored. For driving behaviour, the throttle reversal rate is investigated. Furthermore, the relationship between throttle reversal rate and driving experience is studied. The results show that the combined feedback mode could provide drivers with the highest rated driving experience; the motion-cueing feedback could provide better road surface feeling while the sound and vibration feedback could provide better speed feeling. The throttle reversal rate with motion-cueing feedback is higher than without it, which may be caused by the increased road surface feeling provided by motion cues.

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.

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.