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.

Subjective vehicle stability evaluation is generally conducted during closed-loop driving in which the driver controls the vehicle through transient manoeuvres and evaluate how the vehicle responds to steering inputs, especially how the slip angle builds up. To conduct such evaluation in moving base, driving simulators require that the fed vehicle response to steering inputs is representative. Limited simulator workspace often requires motion scaling, introducing errors in planar dynamics This work, therefore, investigates how the scaling of the planar motion and the slip filtering should be performed in a driving simulator, including the relative relationships between lateral acceleration, yaw rate and slip rate. Two strategies were developed based on the scaling of planar circular motion: one retaining radius information, and the other retaining velocity information. Both strategies avoided filtering the slip rate, as simulations show that the slip rate should be separated from the high-pass filtering process in cueing algorithms and that the scaling should be equal to that of the yaw rate to avoid false cues. A subjective assessment was conducted, and the results indicate advantages for retaining radius information and conclusively the advantage of unfiltered slip information in motion cueing for stability evaluation.

Rolling resistance dictates a large part of the energy consumption of trucks. Therefore, it is necessary to have a sound understanding of the parameters affecting rolling resistance. This article proposes a semi-physical thermodynamic tyre rolling resistance model, which captures the essential properties of rolling resistance, such as transient changes due to temperature effects and the strain-amplitude dependency of the viscous properties. In addition, the model includes cooling effects from the surroundings. Both tyre temperature and rolling resistance are obtained simultaneously in the simulation model for each time step. The nonlinear viscoelasticity in rubber is modelled using the Bergström–Boyce model, where the viscous creep function is scaled with temperature changes. The cooling of the tyre is considered with both convective and radiative cooling. Moreover, the article explains different material parameters and their physical meaning. Additionally, examples of how the model could be used in parameter studies are presented.

Electric and hybrid propulsion systems for articulated vehicles have been gaining increased attention, with the aim to decrease exhaust particle emissions. However, the more environmentally-friendly electric or hybrid articulated vehicles are expected to have increased nonexhaust pollution-related sources because of their significantly increased mass compared with conventional vehicles. One of the main sources of nonexhaust pollution is tire wear, which could potentially cancel the benefits of removing the exhaust through electrification. Tire wear is mainly affected by internal (tire structure and shape) and external (suspension configuration, speed, road surface, etc.) factors. This work focuses on suspension systems and, more specifically, on the ability of active and semiactive suspensions to decrease tire wear in an articulated vehicle. In this direction, an articulated vehicle model that incorporates the tread in its modeling is built to study tire wear during cornering over a class C road. A novel active suspension design based on the H approach is suggested in this work and is compared with passive, semiactive, and other active suspension systems. The suspension systems are also compared mainly with regard to tire wear levels but also with other vehicle performance aspects (i.e., comfort and road holding). The Hop active suspension design is the most effective in decreasing tire wear, with decreases of about 8% to 11%, but without neglecting the rest of the objectives.

Background: Rolling resistance and aerodynamic losses cause a significant part of a truck’s energy consumption. Therefore there is an interest from both vehicle manufacturers and regulators to measure these losses to understand, quantify and reduce the energy consumption of vehicles. On-road measurements are particularly interesting because it enables testing in various ambient conditions and road surfaces with vehicles in service. Objective: Common driving loss measurement devices require unique instrumented measurement wheels, which hinders effective measurements of multiple tyre sets or measurements of vehicles in service. For this purpose, the objective is to develop a novel load-sensing device for measuring braking or driving torque. Methods: The strength of the measurement device is calculated using finite element methods, and the output signal is simulated using virtual strain gauge simulations. In addition to the signal simulation, the device is calibrated using a torsional test rig. Results: The simulation results confirm that the device fulfils the strength requirements and is able to resolve low torque levels. The output signal is simulated for the novel cascaded multi-Wheatstone bridge using the strains extracted from the finite element analysis. The simulations and measurements show that the measurement signal is linear and not sensitive to other load directions. The device is tested on a truck, and the effort of mounting the device is comparable to a regular tyre change. Conclusions: A novel driving loss measurement device design is presented with an innovative positioning of strain gauges decoupling the parasitic loads from the driving loss measurements. The design allows on-road testing using conventional wheels without requiring special measurement wheels or instrumentation of drive shafts, enabling more affordable and accurate measurements.

Driving simulators are increasingly important in vehicle development, benefiting from hardware and motion cueing algorithm (MCA) advancements. However, current state-of-the-art MCAs are optimising with regards to a vehicle fixed vestibular system, ignoring active and passive head movements during manoeuvres. Research shows that drivers actively move their heads to focus their gaze and passively stabilising it during involuntary trunk movements, resulting in significant differences between vehicle and head yaw angles. Humans isolate trunk and head movement in the range of 0.1–1.0 Hz, suggesting neck-driven gaze stabilisation. This behaviour is not accounted for in current MCAs, warranting an investigation. This study develops a driver gaze model to enhance motion cueing strategies and compares it to existing methods. Findings indicate significant discrepancies between vehicle and estimated head yaw rate in winter testing with high slip angles, and that omitting vestibular models and separating the slip angle in the yaw feedback improves the motion cueing with regards to induced head movements. Further, the results shows that there is a clear relationship between motion cueing, visual feedback, and induced driver head movement. In conclusion, driver gaze models can improve motion cueing strategies in driving simulators, and thus the study highlights the need for considering driver gaze behaviour and provides insights for tuning and optimising MCAs.

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.

The automotive industry is heading towards a more objective approach to vehicle testing, but subjective evaluation is still an important part of the development process. Subjective evaluation in physical testing has environmental implications and is dependent on ambient conditions. A more repeatable, faster, safer and more cost-effective tool for subjective evaluation is to use moving base driving simulators. The motion cueing algorithms (MCA) maps the movement of the vehicle into the limited space of the simulator. The choice of reference point, that is, where on the vehicle to sample the motion to feed to the MCA and the alignment of the axis of rotation of the simulator cabin is still an open topic. This paper investigates the choice of reference point and corresponding simulator longitudinal axis of rotation in roll using two methods. The first method uses a linearised model of the combined system of vehicle, simulator and vestibular models. The second method, to position the cabin longitudinal axis of rotation, is based on offline optimisation. The linear model can capture important characteristics of the specific forces and rotations that are fed to the driver through the motion cueing algorithms and offers a method to objectively analyse and potentially tune the motion cueing. The analysis is further complemented with a subjective evaluation of corresponding settings. The results from the linear model, the offline optimisation and the subjective evaluation shows that a reference point at the driver’s head has a clear advantage over the full frequency range compared to a reference point in the chassis roll axis and that the positioning of the cabin longitudinal axis of rotation has a significant effect on the perceived vehicle characteristics.

Knowing the tire pressure during driving is essential since it affects multiple tire properties such as rolling resistance, uneven wear, and how prone the tire is to tire bursts. Tire temperature and cavity pressure are closely tied to each other; a change in tire temperature will cause an alteration in tire cavity pressure. This article gives insights into which tire temperature measurement position is representative enough to estimate pressure changes inside the tire, and whether the pressure changes can be assumed to be nearly isochoric. Climate wind tunnel and road measurements were conducted where tire pressure and temperature at the tire inner liner, the tire shoulder, and the tread surface were monitored. The measurements show that tires do not have a uniform temperature distribution. The ideal gas law is used to estimate the tire pressure from the measured temperatures. The results indicate that of the compared temperature points, the inner liner temperature is the most accurate for estimating tire pressure changes (average error 0.63%), and the pressure changes during driving are nearly isochoric. This conclusion can be drawn because the ratio between inner liner temperature and tire pressure is nearly constant, and the pressure can be simulated well using the isochoric gas law.

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.