论文标题
部分可观测时空混沌系统的无模型预测
Lane-Level Route Planning for Autonomous Vehicles
论文作者
论文摘要
我们提出了一种算法，鉴于在车道级细节中代表道路网络的算法，该算法计算了一条路线，该路线可以最大程度地减少到达给定目的地的预期成本。这样一来，我们的算法使我们能够为不仅要遵循哪种道路，还可以在构成这些道路的车道之间进行更改时，为遇到的复杂权衡而解决，以减少丢失左出口的可能性，而不是不必要地驱动最左边的车道。这种路由问题自然可以作为马尔可夫决策过程（MDP）提出，其中车道变更动作具有随机结果。但是，已知MDP通常是为了解决一般的解决方案。在本文中，我们表明 - 在合理的假设下 - 我们可以使用类似Dijkstra的方法来解决此随机问题，并受益于其有效的$ O（n \ log n）$运行时间。这使自动驾驶汽车能够表现出车道选择行为，因为它有效地计划了通往其目的地的最佳途径。
We present an algorithm that, given a representation of a road network in lane-level detail, computes a route that minimizes the expected cost to reach a given destination. In doing so, our algorithm allows us to solve for the complex trade-offs encountered when trying to decide not just which roads to follow, but also when to change between the lanes making up these roads, in order to -- for example -- reduce the likelihood of missing a left exit while not unnecessarily driving in the leftmost lane. This routing problem can naturally be formulated as a Markov Decision Process (MDP), in which lane change actions have stochastic outcomes. However, MDPs are known to be time-consuming to solve in general. In this paper, we show that -- under reasonable assumptions -- we can use a Dijkstra-like approach to solve this stochastic problem, and benefit from its efficient $O(n \log n)$ running time. This enables an autonomous vehicle to exhibit lane-selection behavior as it efficiently plans an optimal route to its destination.
