We propose a new scheme to learn motion planning constraints from human driving trajectories. Behavioral and motion planning are the key components in an autonomous driving system. The behavioral planning is responsible for high-level decision making required to follow traffic rules and interact with other road participants. The motion planner role is to generate feasible, safe trajectories for a self-driving vehicle to follow. The trajectories are generated through an optimization scheme to optimize a cost function based on metrics related to smoothness, movability, and comfort, and subject to a set of constraints derived from the planned behavior, safety considerations, and feasibility. A common practice is to manually design the cost function and constraints. Recent work has investigated learning the cost function from human driving demonstrations. While effective, the practical application of such approaches is still questionable in autonomous driving. In contrast, this paper focuses on learning driving constraints, which can be used as an add-on module to existing autonomous driving solutions. To learn the constraint, the planning problem is formulated as a constrained Markov Decision Process, whose elements are assumed to be known except the constraints. The constraints are then learned by learning the distribution of expert trajectories and estimating the probability of optimal trajectories belonging to the learned distribution. The proposed scheme is evaluated using NGSIM dataset, yielding less than 1\% collision rate and out of road maneuvers when the learned constraints is used in an optimization-based motion planner.
翻译:行为和运动规划是自主驾驶系统的关键组成部分。行为规划是自主设计成本功能和限制的常见做法。最近的工作调查了从人驾驶演示中学习成本功能。虽然这种方法的实际应用在自主驾驶中仍然有问题。与此相反,本文的重点是学习驾驶限制,这可以用作现有自主驾驶解决方案的附加模块。为了了解制约因素,规划问题被发展成一个制约的Markov决定程序,其要素被假定为除制约之外的其他要素。随后,通过学习如何使用最佳机动性计划来评估收益率。