GRIT：快速、可解释和可验证的目标识别，使用自动驾驶的学习决策树
GRIT: Fast, Interpretable, and Verifiable Goal Recognition with Learned Decision Trees for Autonomous Driving
摘要：
对于自动驾驶汽车来说，重要的是要有能力推断其他车辆的目标（目标识别），以便安全地与其他车辆进行交互并预测其未来的轨迹。这是一个困难的问题，特别是在城市环境中，许多车辆之间的相互作用。目标识别方法必须快速，以便实时的运行并做出准确的推断。由于自动驾驶是安全关键的，因此重要的是要有人类可解释的方法，并且可以正式验证安全性。现有的自动驾驶汽车目标识别方法无法满足快速、准确、可解释和可验证的所有四个目标。我们提出了目标识别与解释树（GRIT），目标识别系统，实现这些目标。GRIT利用车辆轨迹数据训练的决策树。我们在两个数据集上评估了GRIT，结果表明GRIT实现了快速的推理速度和与两个深度学习基线、基于规划的目标识别方法和GRIT消融相当的准确性。我们表明，学习树是人类可解释的，并演示如何GRIT的属性可以正式验证使用可满足性模理论（SMT）求解器。
英文摘要：
It is important for autonomous vehicles to have the ability to infer the goals of other vehicles (goal recognition), in order to safely interact with other vehicles and predict their future trajectories. This is a difficult problem, especially in urban environments with interactions between many vehicles. Goal recognition methods must be fast to run in real time and make accurate inferences. As autonomous driving is safety-critical, it is important to have methods which are human interpretable and for which safety can be formally verified. Existing goal recognition methods for autonomous vehicles fail to satisfy all four objectives of being fast, accurate, interpretable and verifiable. We propose Goal Recognition with Interpretable Trees (GRIT), a goal recognition system which achieves these objectives. GRIT makes use of decision trees trained on vehicle trajectory data. We evaluate GRIT on two datasets, showing that GRIT achieved fast inference speed and comparable accuracy to two deep learning baselines, a planning-based goal recognition method, and an ablation of GRIT. We show that the learned trees are human interpretable and demonstrate how properties of GRIT can be formally verified using a satisfiability modulo theories (SMT) solver.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
