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Driving simulation has become a very useful tool for vehicle design and research in industry and educational institutes. This paper provides a review of driving simulator components, including the vehicle dynamics model, the motion system, and the virtual environment, and how they interact with the human perceptual system in order to create the illusion of the driving. In addition, a sample of current state-of-the-art vehicle simulators and algorithms are described. Finally, current applications are discussed, such as driver-centered studies, chassis and powertrain design, and autonomous systems development.

Researchers are using driving simulators to design and assess the automated driving and driver assistant systems, due to the safe nature of experimentation in the virtual environment. The motion cues and accelerations felt by the drivers are essential for an accurate perception of the events and the response of the drivers. In this paper, the vehicle dynamic model and the Motion Cueing Algorithm used for the simulation is described in detail, then driver’s performance and subjective assessments was studied for the braking, chicane and overtaking maneuver in the 3 different gear shifting scenario. The study demonstrates that the presence of the motion cueing feedback in the driving simulation was satisfactory and gave realistic cues for the participants independent of the gear shifting system, however no significant effect was found from the driver’s behavior due to different gear shifting system.

This paper describes a driving simulation experiment, executed on the Daimler Driving Simulator (DDS), in which a filter-based and an optimization-based motion cueing algorithm (MCA) were compared using a newly developed motion cueing quality rating method. The goal of the comparison was to investigate whether optimization-based MCAs have, compared to filter-based approaches, the potential to improve the quality of motion simulations. The paper describes the two algorithms, discusses their strengths and weaknesses and describes the experimental methods and results. The MCAs were compared in an experiment where 18 participants rated the perceived motion mismatch, i.e., the perceived mismatch between the motion felt in the simulator and the motion one would expect from a drive in a real car. The results show that the quality of the motion cueing was rated better for the optimization-based MCA than for the filter-based MCA, indicating that there exists a potential to improve the quality of the motion simulation with optimization-based methods. Furthermore, it was shown that the rating method provides reliable and repeatable results within and between participants, which further establishes the utility of the method.

La version editeur de cet article est disponible à l'adresse suivante : http://onlinelibrary.wiley.com/doi/10.1002/oca.2081/abstract; International audience; This study proposes a method and an experimental validation to analyze dynamics response of the simulator's cabin and platform with respect to the type of the control used in the hexapod driving simulator. In this article, two different forms of motion platform tracking control are performed as a classical motion cueing algorithm and a discrete-time linear quadratic regulator (LQR) motion cueing algorithm. For each situation, vehicle dynamics and motion platform level data are registered from the driving simulation software. In addition, the natural frequencies of the roll accelerations are obtained in real-time by using FFT. The data are denoised by using wavelet 1D transformation. The results show that by using discrete-time LQR algorithm, the roll acceleration amplitudes that correspond to the natural frequencies and the total roll jerk have decreased at the motion platform level. Also, the natural frequencies have increased reasonably by using the discrete LQR motion cueing (1.5-2.2 Hz) compared with using the classical algorithm (0.4-1.5 Hz) at the motion platform, which is an indicator of motion sickness incidence avoidance. The literature shows that lateral motion (roll, yaw, etc.) in the frequency range of 0.1-0.5 Hz induces motion sickness. Furthermore, using discrete-time LQR motion cueing algorithm has decreased the sensation error (motion platform-vehicle (cabin) levels) two times in terms of total roll jerk. In conclusion, discrete-time LQR motion cueing has reduced the simulator sickness more than the classical motion cueing algorithm depending on sensory cue conflict theory.Copyright © 2013 John Wiley & Sons, Ltd.

Two different topics are covered in the thesis. Model Predictive Control applied to the Motion Cueing Problem In the last years the interest about dynamic driving simulators is increasing and new commercial solutions are arising. Driving simulators play an important role in the development of new vehicles and advanced driver assistance devices: in fact, on the one hand, having a human driver on a driving simulator allows automotive manufacturers to bridge the gap between virtual prototyping and on-road testing during the vehicle development phase; on the other hand, novel driver assistance systems (such as advanced accident avoidance systems) can be safely tested by having the driver operating the vehicle in a virtual, highly realistic environment, while being exposed to hazardous situations. In both applications, it is crucial to faithfully reproduce in the simulator the driver's perception of forces acting on the vehicle and its acceleration. This has to be achieved while keeping the platform within its limited operation space. Such strategies go under the name of Motion Cueing Algorithms. In this work, a particular implementation of a Motion Cueing algorithm is described, that is based on Model Predictive Control technique. A distinctive feature of such approach is that it exploits a detailed model of the human vestibular system, and consequently differs from standard Motion Cueing strategies based on Washout Filters: such feature allows for better implementation of tilt coordination and more efficient handling of the platform limits. The algorithm has been evaluated in practice on a small-size, innovative platform, by performing tests with professional drivers. Results show that the MPC-based motion cueing algorithm allows to effectively handle the platform working area, to limit the presence of those platform movements that are typically associated with driver motion sickness, and to devise simple and intuitive tuning procedures. Moreover, the availability of an effective virtual driver allows the development of effective predictive strategies, and first simulation results are reported in the thesis. Control Techniques for a Hybrid Sport Motorcycle Reduction of the environmental impact of transportation systems is a world wide priority. Hybrid propulsion vehicles have proved to have a strong potential to this regard, and different four-wheels solutions have spread out in the market. Differently from cars, and even if they are considered the ideal solution for urban mobility, motorbikes and mopeds have not seen a wide application of hybrid propulsion yet, mostly due to the more strict constraints on available space and driving feeling. In the thesis, the problem of providing a commercial 125cc motorbike with a hybrid propulsion system is considered, by adding an electric engine to its standard internal combustion engine. The aim for the prototype is to use the electrical machine (directly keyed on the drive shaft) to obtain a torque boost during accelerations, improving and regularizing the supplied power while reducing the emissions. Two different control algorithms are proposed 1) the first is based on a standard heuristic with adaptive features, simpler to implement on the ECU for the prototype; 2) the second is a torque-split optimal-control strategy, managing the different contributions from the two engines. A crucial point is the implementation of a Simulink virtual environment, realized starting from a commercial tool, VI-BikeRealTime, to test the algorithms. The hybrid engine model has been implemented in the tool from scratch, as well as a simple battery model, derived directly from data-sheet characteristics by using polynomial interpolation. The simulation system is completed by a virtual rider and a tool for build test circuits. Results of the simulations on a realistic track are included, to evaluate the different performance of the two strategies in a closed loop environment (thanks to the virtual rider). The results from on-track tests of the real prototype, using the first control strategy, are reported too.

The use of large displacement yaw cueing is becoming more common as a part of the motion cueing in driving simulators. It is expected that driving behaviour, comfort and realism will be positively affected by adding a yaw table, especially during low-speed turning manoeuvres. We used TNO's advanced motion platform Desdemona to explore the effects of yaw motion during highway and urban turning manoeuvres: 14 participants drove the simulator with and without yaw motion. Questionnaires results showed that the simulation was rated as quite realistic. Effects of yaw motion on the subjective ratings were not found. In terms of driving behaviour, we found statistically significant effects of yaw motion in curve driving, especially in small-radius curves. With yaw motion present, driving behaviour became more cautious, and (compared against the literature) more realistic. This suggests that adding yaw motion to a driving simulator improves the external validity for low-speed corner driving manoeuvres.

The use of large displacement yaw cueing is becoming more common as a part of the motion cueing in driving simulators. It is expected that driving behaviour, comfort and realism will be positively affected by adding a yaw table, especially during low-speed turning manoeuvres. We used TNO's advanced motion platform Desdemona to explore the effects of yaw motion during highway and urban turning manoeuvres: 14 participants drove the simulator with and without yaw motion. Questionnaires results showed that the simulation was rated as quite realistic. Effects of yaw motion on the subjective ratings were not found. In terms of driving behaviour, we found statistically significant effects of yaw motion in curve driving, especially in small-radius curves. With yaw motion present, driving behaviour became more cautious, and (compared against the literature) more realistic. This suggests that adding yaw motion to a driving simulator improves the external validity for low-speed corner driving manoeuvres.