  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.
