This thesis explores the benefits machine learning algorithms can bring to online planning and scheduling for autonomous vehicles in off-road situations. Mainly, we focus on typical problems of interest which include computing itineraries that meet certain objectives, as well as computing scheduling strategies to execute synchronized maneuvers with other vehicles. We present a range of learning-based heuristics to assist different itinerary planners. We show that these heuristics allow a significant increase in performance for optimal planners. Furthermore, in the case of approximate planning, we show that not only does the running time decrease, the quality of the itinerary found also becomes almost always better. Finally, in order to synthesize strategies to execute synchronized maneuvers, we propose a novel type of scheduling controllability and a learning-assisted algorithm. The proposed framework achieves significant improvement on known benchmarks in this controllability type over the performance of state-of-the-art works in a related controllability type. Moreover, it is able to find strategies on complex scheduling problems for which previous works fail to do so.

中文翻译：

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学习自动驾驶汽车的越野机动计划

本论文探讨了机器学习算法可以为越野情况下的自动驾驶汽车的在线规划和调度带来的好处。我们主要关注感兴趣的典型问题，包括计算满足特定目标的行程，以及计算与其他车辆执行同步机动的调度策略。我们提供了一系列基于学习的启发式方法，以帮助不同的行程规划者。我们表明，这些启发式方法可以显着提高最佳规划器的性能。此外，在近似规划的情况下，我们表明不仅运行时间减少了，找到的行程的质量也几乎总是更好。最后，为了综合执行同步机动的策略，我们提出了一种新型的调度可控性和一种学习辅助算法。相对于相关可控性类型的最新作品的性能，所提出的框架在这​​种可控性类型的已知基准上实现了显着改进。此外，它能够找到处理复杂调度问题的策略，而以前的工作无法做到这一点。