Driving simulators are effective tools for training, virtual prototyping, and safety assessment which can minimize the cost and maximize road safety. Despite the aim of a realistic motion generation for the impression of real-world driving, motion simulators are bound in a limited workspace. Motion cueing algorithms (MCAs) aim to plan an acceptable motion feeling for drivers, without infringing the simulated boundaries. Recently, model predictive control (MPC) has been widely used in MCAs; however, the tuning process for finding the best weights of the MPC optimization is still a challenge. As there are several objectives for the optimization without any standard weighting for solution evaluations, a nonbiased scalarization of solutions for the purpose of comparison is impossible. In this paper, a clear method for obtaining the best MPC weighting has been proposed. This method searches for the best tune of MPC cost function weights, reduces the user burden for weight tuning while receiving feedback from the user satisfaction. The MPC-based MCA weights are optimized using a multiobjective genetic algorithm (GA) considering objectives, such as minimization of motion inputs (linear acceleration and angular velocity), input rates, output displacements and the sensed motion errors. Any process based on trial-and-error has been omitted. The adjusted weights have to satisfy a set of predefined conditions related to maximum tolerated error and maximum displacement. The obtained Pareto-front is used for decision making via an interactive GA (IGA), aiming for maximization of the decision maker’s satisfaction. A Web interface is developed to interact with the IGA and to influence the region of searching. Simulation results show the superiority of the proposed method compared with the previous empirical tuning method. The sensed motion error is minimized using the proposed method and with the same available workspace, a more realistic motion can be rendered to the driver.

Multiobjective and Interactive Genetic Algorithms for Weight Tuning of a Model Predictive Control-Based Motion Cueing Algorithm

Publisher Summary Driving simulators are now a major tool, arguably the major tool, for research on driver performance and behavior. Using two major journals—Transportation Research Part F and Human Factors—as the benchmark, it can be seen that studies based on simulator research constitute a major proportion of the published papers in the driving domain. Simulators provide the opportunity to investigate driving under controlled conditions in a manner that is unparalleled by the alternatives. Real-world studies lack the equivalent control element, whereas test tracks offer a very depleted and inflexible driving environment. Simulator capability, particularly in terms of the graphics performance of PC-based systems, has grown very fast in recent years, and the advent of small-scale and relatively low-cost motion systems means that it may soon become a standard for a midrange simulator to be equipped with six degrees of freedom of motion. The number of research simulators worldwide continues to increase, and simulator studies constitute an increasing proportion of the research literature on driving performance and behavior. Simulators may not be total replicates of the real world, and indeed they cannot be. But they offer the researcher of driver behavior an advantage that real-world studies cannot match: the ability to control experimental conditions and create prescripted scenarios.

Driving Simulators as Research Tools in Traffic Psychology

Driving simulators are widely used in many different applications, such as driver training, vehicle development, and medical studies. To fully exploit the potential of such devices, it is crucial to develop platform motion control strategies that generate realistic driving feelings. 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 paper 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. The algorithm has been evaluated experimentally 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 to driver motion sickness, and to devise simple and intuitive tuning procedures.

A real time implementation of MPC based Motion Cueing strategy for driving simulators

Advanced driving simulators aim at rendering the motion of a vehicle with maximum fidelity, which requires increased mechanical travel, size, and cost of the system. Motion cueing algorithms reduce the motion envelope by taking advantage of limitations in human motion perception, and the most commonly employed method is just to scale down the physical motion. However, little is known on the effects of motion scaling on motion perception and on actual driving performance. This paper presents the results of a European collaborative project, which explored different motion scale factors in a slalom driving task. Three state-of-the-art simulator systems were used, which were capable of generating displacements of several meters. The results of four comparable driving experiments, which were obtained with a total of 65 participants, indicate a preference for motion scale factors below 1, within a wide range of acceptable values (0.4-0.75). Very reduced or absent motion cues significantly degrade driving performance. Applications of this research are discussed for the design of motion systems and cueing algorithms for driving simulation.

Motion Scaling for High-Performance Driving Simulators

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 OEMs 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 drivers perception of forces acting on the vehicle and its acceleration. The strategy used to operate the simulator platform within its limited working space to provide the driver with the most realistic perception goes under the name of motion cueing. In this paper we describe a novel approach to motion cueing design that is based on Model Predictive Control techniques. Two features characterize the algorithm, namely, the use of a detailed model of the human vestibular system and a predictive strategy based on the availability of a virtual driver. Differently from classical schemes based on washout filters, such features allows a better implementation of tilt coordination and to handle more efficiently the platform limits.

Mehmet Dagdelen

Papers

Model-based predictive motion cueing strategy for vehicle driving simulators