Modeling and Optimization for Critical Vehicle Maneuvers

Abstract: As development in sensor technology, situation awareness systems, and computational hardware for vehicle systems progress, an opportunity for more advanced and sophisticated vehicle safety-systems arises. With the increased level of available information---such as position on the road, road curvature and knowledge about surrounding obstacles---future systems could be seen utilizing more advanced controls, exploiting at-the-limit behavior of the vehicle. Having this in mind, optimization methods have emerged as a powerful tool for offline vehicle-performance evaluation, providing inspiration to new control strategies, and by direct implementation in on-board systems. This will, however, require a careful choice of modeling and objectives, since the solution to the optimization problem will rely on this.With emphasis on vehicle modeling for optimization-based maneuvering applications, a vehicle-dynamics testbed has been developed. Using this vehicle in a series of experiments, most extensively in a double lane-change maneuver, verified the functionality and capability of the equipment. Further, a comparative study was performed, considering vehicle models based on the single-track model, extended with, e.g., tire-force saturation, tire-force lag and roll dynamics. The ability to predict vehicle behavior was evaluated against measurement data from the vehicle testbed.A platform for solving vehicle-maneuvering optimization-problems has been developed, with state-of-the-art optimization tools, such as JModelica.org and Ipopt. This platform is utilized for studies concerning the influence different vehicle-model configurations have on the solution to critical maneuvering problems. In particular, different tire modeling approaches, as well as vehicle-chassis models of various complexity, are investigated. Also, the influence different road-surface conditions---e.g., asphalt, snow and ice---have on the solution to time-optimal maneuvers is studied. The results show that even for less complex models---such as a single-track model with a Magic Formula based tire-model---accurate predictions can be obtained when compared to measurement data. The general observation regarding vehicle modeling for the time-critical maneuvers is similar; even the least complex models can be seen to capture certain characteristics analogous to those of higher complexity.Analyzing the results from the optimization problems, it is seen that the overall dynamics, such as resultant forces and yaw moment, obtained for different model configurations, correlates very well. For different road surfaces, the solutions will of course differ due to the various levels of tire-forces being possible to realize. However, remarkably similar vehicle paths are obtained, regardless of surface. These are valuable observations, since they imply that models of less complexity could be utilized in future on-board optimization-algorithms, to generate, e.g., yaw moment and vehicle paths. In combination with additional information from enhanced situation-awareness systems, this enables more advanced safety-systems to be considered for future vehicles.