2 research outputs found
Safe learning-based optimal motion planning for automated driving
This paper presents preliminary work on learning the search heuristic for the
optimal motion planning for automated driving in urban traffic. Previous work
considered search-based optimal motion planning framework (SBOMP) that utilized
numerical or model-based heuristics that did not consider dynamic obstacles.
Optimal solution was still guaranteed since dynamic obstacles can only increase
the cost. However, significant variations in the search efficiency are observed
depending whether dynamic obstacles are present or not. This paper introduces
machine learning (ML) based heuristic that takes into account dynamic
obstacles, thus adding to the performance consistency for achieving real-time
implementation.Comment: 3 pages, 1 figure, 1 pseudocode, extended abstract accepted to ICML /
IJCAI / AAMAS 2018 Workshop on Planning and Learning (PAL-18
A novel approach to model exploration for value function learning
Planning and Learning are complementary approaches. Planning relies on
deliberative reasoning about the current state and sequence of future reachable
states to solve the problem. Learning, on the other hand, is focused on
improving system performance based on experience or available data. Learning to
improve the performance of planning based on experience in similar, previously
solved problems, is ongoing research. One approach is to learn Value function
(cost-to-go) which can be used as heuristics for speeding up search-based
planning. Existing approaches in this direction use the results of the previous
search for learning the heuristics. In this work, we present a search-inspired
approach of systematic model exploration for the learning of the value function
which does not stop when a plan is available but rather prolongs search such
that not only resulting optimal path is used but also extended region around
the optimal path. This, in turn, improves both the efficiency and robustness of
successive planning. Additionally, the effect of losing admissibility by using
ML heuristic is managed by bounding ML with other admissible heuristics.Comment: Presented at RSS 2019 workshop CLea