When used in autonomous driving, goal recognition allows the future behaviour of other vehicles to be more accurately predicted. A recent goal recognition method for autonomous vehicles, GRIT, has been shown to be fast, accurate, interpretable and verifiable. In autonomous driving, vehicles can encounter novel scenarios that were unseen during training, and the environment is partially observable due to occlusions. However, GRIT can only operate in fixed frame scenarios, with full observability. We present a novel goal recognition method named Goal Recognition with Interpretable Trees under Occlusion (OGRIT), which solves these shortcomings of GRIT. We demonstrate that OGRIT can generalise between different scenarios and handle missing data due to occlusions, while still being fast, accurate, interpretable and verifiable.
翻译：当用于自主驾驶时,目标识别可使其他车辆的未来行为得到更准确的预测。最近的一种自主车辆目标识别方法GRIT已被证明是快速、准确、可解释和可核查的。在自主驾驶中,车辆可能遇到在训练期间看不见的新情况,环境由于隔离而部分可见。然而,GRIT只能以固定的框架情景运作,并且完全易于理解。我们提出了一个新的目标识别方法,名为“在封闭状态下对可解释树的识别目标 ” (OGRIT),它解决了GRIT的这些缺陷。我们证明,OGRIT可以对不同情景进行概括,并处理由于隔离而缺失的数据,同时仍然快速、准确、可解释和可核查。
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