Motion planning for autonomous vehicles is a challenging task, especially in dynamic environments. The motion of the vehicle itself needs to be considered while the vehicle needs to react to its surroundings at the same time. Sampling-based algorithms proved to be suitable to cope with these challenges. However, the performance of these algorithms is highly dependent on the sampling heuristics, which in turn are often hand crafted and thus need a large amount of tuning. Therefore, we developed two approaches based on deep learning to learn these heuristics for sampling-based motion planning in dynamic environments. The first approach predicts a discrete probability distribution for each point in time of the future trajectory, whereas the second approach directly predicts a variety of trajectories by using dropout sampling. Both approaches are based on an environment representation encoded as a grid-based tensor. The learned heuristics are integrated into an existing planning framework based on particle swarm optimization and are evaluated in several situations. This shows how to combine the strengths of machine learning based approaches and the traceability of rule- or model-based approaches. The evaluation demonstrates that, in total, we were able to improve on our current heuristics. However, none of the approaches performed consistently better in all scenarios evaluated.
Optimization of Sampling-Based Motion Planning in Dynamic Environments Using Neural Networks
2020 IEEE Intelligent Vehicles Symposium (IV) ; 2110-2117
2020-10-19
617963 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch