Trajectory planning for automated driving in dynamic environments
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Date
2021
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Abstract
Considering the last decades, the trend in the automotive industry to continuously increase the level of automation of vehicles is evident. A lot of research and development effort has been invested to improve upon driving safety and comfort in traffic. Nowadays, advanced driver assistance systems, and the development of automated driving functions in particular, represent one of the main areas of innovation in automotive engineering. In order to cope with challenges arising from complex dynamic environments the automated vehicle needs to perform comprehensive cognitive tasks that come along with the presence of other traffic participants and the necessity to adhere to prevailing traffic regulations. As a consequence, the automated driving task is decomposed into several sub problems. In the functional architecture of automated vehicles, motion planning that addresses the generation of a comfortable and safe trajectory is a key component that directly affects the overall driving performance.
This thesis is about the development of a trajectory planning approach suitable to deal with dynamic environments. A two level hierarchical trajectory planning framework is proposed that unites the capability of optimality and spline interpolation and explicitly considers the aspect of contradicting planning objectives. The framework is designed to work in receding horizon fashion by performing cyclic replanning and hence accounts for the dynamic character of the environment. The hierarchization into two separate levels of optimization leads to an approach that covers basic driving functionality on low level, while required high level behavior is still prioritized. The presented framework relies on a spline-based trajectory representation with an underlying optimal interpolation strategy. The optimal trajectory with respect to a certain situation is found by joint optimization on high and low level. A continuous and a discrete trajectory optimization variant to generate an optimal trajectory with respect to high level objectives are presented that basically differ in the definition of possible solutions in terms of the optimal decision variables. Constraints like drivability incorporated by exploiting the flatness property of the applied vehicle model and accurate collision avoidance checking are considered explicitly to comply to essential requirements for automated driving. To evaluate the quality of the trajectory in terms of the associated driving behavior, several objectives are defined. For dedicated objectives a curvilinear frame is used, which enables a precise formulation of the desired vehicle behavior with respect to driving applications in structured environments. Hence, this measure permits to formulate objectives independent of road curvature, extending the scope of the applied trajectory planning approach to a wide range of scenarios. Evaluation works out the distinct characteristic features of the two presented high level optimization approaches, showing the achieved performance at the example of typical (highway) traffic scenarios. It is shown that both, the continuous as well as the discrete approach, are suitable to solve the trajectory generation problem supporting the idea of creating a generic trajectory planning framework for automated driving.
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Keywords
Automated driving, Motion planning, Trajectory optimization