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 paper 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 theproposed method. The findings indicate that, compared to 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.

The Centre for ECO2 Vehicle Design (ECO2) is a Swedish multilateral competence centre based at KTH Royal Institute of Technology with a focus on the development of resource effcient vehicle for sustainable transport. This report covers the first 18 months of ECO2’s new agreement period (Jul 2024 – Dec 2029), marking a period of organizational renewal, research excellence, and strategic positioning for future growth. During this period, ECO2 successfully completed three PhD defences, produced 33 peer-reviewed journal publications, 50 conference papers, and strengthened its research portfolio with 12 projects multiple project applications. The new five-year centre agreement was finalized, securing partner and KTH financing co-financing and establishing enhanced governance structures. ther highlights include the holding of the second Resource Efficient Vehicles conference in Graz with 10 ECO2 contributions, establishing a collaboration agreement with Fraunhofer IAO, and expanding industrial partnerships through new doctoral projects. ECO2’s research continues to address critical attributes of vehicles as part the transport system – effciency, accessibility and resilience, which contribute sustainable transport and other major societal challenges.

Electric and hybrid passenger vehicles offer the ability to recover braking energy and optimize power distribution, enabling significant improvements in efficiency compared to conventional vehicles. In this study, we quantify and compare the energy-saving potential of three optimization methods—mixedinteger linear programming (MILP), nonlinear programming (NLP), and dynamic programming (DP)—for a battery-electric vehicle following the Urban Dynamometer Driving Schedule (UDDS). A simplified longitudinal EV model with aerodynamic, rolling, and gravitational resistances is used to simulate vehicle dynamics, battery charging/discharging, and regenerative braking. The reference case enforces exact tracking of the UDDS speed profile, including late, hard braking at stops. Over a receding horizon of five seconds, MILP linearizes resistive forces and solves a linear program (≈16 ms per step), while NLP retains full nonlinear dynamics with a quadratic speed-deviation penalty (≈1.7 ms per step). DP serves as an offline global benchmark across a discretized state-of-charge and velocity grid. Results show that, relative to the baseline, NLP achieves the largest net-energy reduction (31.1 %), followed by DP (23.5 %) and MILP (18.2 %). NLP’s ability to model exact aerodynamic drag and smoothly coast into stops yields the highest savings but requires more computational effort. MILP offers a compromise between efficiency and real-time feasibility on automotive ECUs, although its hard speed bounds induce oscillatory speed errors. DP, while globally optimal under exact speed tracking, is best suited as an offline reference. These findings suggest that hybrid approaches—such as DP-trained lookup tables or variable-efficiency NLP surrogates—could approach global optimality while meeting real-time constraints. Future work will validate these methods on  additional driving cycles and incorporate variable motor/regenerative efficiency maps to better align with real-world performance. 

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.

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.