面向不确定性环境的自动驾驶运动规划：机遇与挑战

运动规划算法作为自动驾驶系统中的重要研究内容，愈发受到研究者们关注.然而目前多数算法仅考虑在确定性结构化环境中的应用，忽视动态交通环境中潜在的不确定性因素.文中面向不确定性环境，将运动规划算法总结为两类：部分可观测马尔可夫决策过程(POMDP)和概率占用栅格图(POGM),从理论基础、求解算法、实际应用三方面进行介绍.基于当前置信状态，POMDP计算使未来折扣奖励最大的策略.POGM使用概率表征对应栅格上的占用情况，衡量车流动态变化的可能性，良好表征不确定性情况.最后，总结不确定性环境中当前运动规划问题面临的主要挑战和未来可能的研究方向.

Motion Planning under Uncertainty for Autonomous Driving:Opportunities and Challenges

Motion planning algorithm, as an important part of autonomous driving systems, draws increasing attention from researchers. However, most existing motion planning algorithms only consider their application in deterministic structured environments, neglecting potential uncertainties in dynamic traffic environments. In this paper, motion planning algorithms are divided into two categories for the uncertain environment: partially observable Markov decision process and probability occupancy grid map.The two categories are introduced for three aspects: theoretical foundation, solution algorithm and practical application. The strategy with the maximum discounted reward in the future is calculated by partially observable Markov decision process based on the current confidence state. Probability occupancy grid map utilizes probability to represent the occupancy status of corresponding grids, measuring the possibility of dynamic changes in traffic flow, and well representing the uncertainty. Finally, the main challenges and future research directions for motion planning in uncertain environments are summarized .