Recent trends in mobility and transportation underscore the growing importance of promoting alternative, flexible, and environmentally friendly modes of transport-such as cycling-that not only contribute significantly to users' health and well-being but also enable urban concepts like the 15-minute city. For cyclists, travel time is a critical factor influencing both route selection and the decision to choose cycling as a preferred mode of transportation. This paper presents an energy-based model of a synthetic population of free cyclists to analyze their speed profile characteristics, summarized in terms of average speeds. The proposed model is intended for use in evaluating infrastructure planning, optimizing green wave traffic light systems, and assessing the health benefits of cycling. The model is built on general data (not fitted to a certain dataset) and accounts for key factors such as rolling resistance and aerodynamic drag-both of which vary with ambient temperature-along with acceleration, road gradient, and other influences, including free rolling and deceleration or stopping at intersections and traffic lights. The results reveal a distribution of cycling speeds that show good agreement with field observations.

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.

Transportation and mobility are experiencing a significant transformation the recent years, which is evident in road (vehicles and bicycles) and rail vehicles. This transformation includes the introduction of automated vehicles (AVs), the increase of active transportation modes (e.g. cycling and walking) and the extended use of trains for commuting to work or travelling. However, despite this great transition, there are significant challenges that can hamper the wide use of these transport means, with comfort being one of them. In this paper, we explore physical comfort in these transport modes, examining ride comfort and motion sickness definitions and assessment, environmental influences, occupant postures, human body dynamics, and postural control strategies for adapting to motion. We conclude that while established comfort guidelines exist for conventional vehicles, substantial gaps persist in understanding and evaluating comfort in emerging modes like bicycles and automated vehicles with varied seating. Further research into modelling human body dynamics and the central nervous system's role in postural control, especially for cyclists and non-conventional postures, is essential for designing future transportation systems that prioritise comfort and health.

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.

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.

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.

This study investigated the effects of load case (varying extra load in addition to a single passenger) and tyre pressure (50, 200, 400 kPa) on the whole-body vibration of children being transported in a cycle carrier. The experiments were performed on a special test bed designed for this purpose. Since previous work indicated non-linear influence of the tyres, static and dynamic tyre stiffness were measured separately. Overall, vibration total values were in a similar level as the results of previous on road experiments. The tyres seemed to have some eigenfrequency that was rather independent of the vertical load but affected the pattern of the vibration experienced by the carrier passengers.

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.

The range of an electrically assisted bicycle, which is constrained by the rider's cycling ability and the battery capacity, is heavily influenced by rolling resistance. Furthermore, the magnitude of rolling resistance affects commuters' motivation to decide whether to cycle or to choose another way to commute. This paper presents a way to simulate the transient rolling resistance of bicycle tyres as a function of ambient temperature. The significance of the change in driving resistance at different ambient temperatures is demonstrated through the range simulation of an electrically assisted bicycle at varying ambient temperatures. A representative driving cycle for bicycle commuters was created, enabling comparison of dynamic behaviour in a standardised set, to evaluate the effect of ambient temperature on the battery capacity and the increase in driving resistances. To the authors' knowledge, this kind of model has not previously been created for bicycles. The model calculates tyre temperature based on the heat transfer, considering the heating-i. e., rolling resistance-and cooling effects-i. e., convective and radiative cooling. The decrease in tyre temperature results in an increase in rolling resistance and a decrease in the battery capacity, which was considered in the simulations. The results show significantly increased energy demand at a very low ambient temperature (down to -30 degrees C) compared to + 20 degrees C. The novelty of this article is simulating energy expenditure of bicycle dynamically as a function of ambient temperature. This model includes a temperature-dependent transient bicycle rolling resistance model as well as a battery capacity model. The findings provide researchers with a better comprehension of parameters affecting energy expenditure of bicycles at different ambient or tyre temperatures. The models can be used as a tool during the design process of bicycles to quantify the required battery capacities at different climates. In addition, traffic planners can use the model to assess the effect of changes in infrastructure on motivation to utilise bicycles.

Driving feedback is an important factor that can affect the perceptions of remote drivers of the surrounding environment during teleoperation. This paper focuses on investigating the influence of motion-cueing, sound and vibration feedback on driving behaviour and experience. A prototype teleoperation station is developed with feedback from audio, vibration actuators, and motion cues. Using this prototype, the experiment is carried out in two scenarios: a low-speed disturbance scenario with 30 participants and a dynamic driving scenario with 22 participants. Objective and subjective assessment methods are used to evaluate driving behaviour and experience separately. The results indicate that the combination of motion-cueing, sound and vibration feedback provides the most favourable driving experience for the participants. Specifically, sound and vibration feedback enhance drivers' sense of speed, while motion-cueing feedback helps in road surface sensing, leading to increased throttle reversal rate in the low-speed disturbance scenario. However, it is noteworthy that motion-cueing feedback does not significantly improve driving performance in the dynamic driving scenario of this study.