Goal recognition (GR) involves inferring the goals of other vehicles, such as a certain junction exit, which can enable more accurate prediction of their future behaviour. In autonomous driving, vehicles can encounter many different scenarios and the environment may be partially observable due to occlusions. We present a novel GR method named Goal Recognition with Interpretable Trees under Occlusion (OGRIT). OGRIT uses decision trees learned from vehicle trajectory data to infer the probabilities of a set of generated goals. We demonstrate that OGRIT can handle missing data due to occlusions and make inferences across multiple scenarios using the same learned decision trees, while being computationally fast, accurate, interpretable and verifiable. We also release the inDO, rounDO and OpenDDO datasets of occluded regions used to evaluate OGRIT.

翻译：目标识别（GR）涉及推断其他车辆的目标（如某个路口出口），从而能够更准确地预测其未来行为。在自动驾驶中，车辆可能遇到多种不同的场景，且由于遮挡，环境可能部分可观测。我们提出一种名为“遮挡条件下可解释树目标识别”（OGRIT）的新型GR方法。OGRIT使用从车辆轨迹数据中学习的决策树来推断一组生成目标的概率。我们证明，OGRIT能够处理因遮挡导致的数据缺失，并利用相同的学习决策树跨多个场景进行推断，同时具有计算快速、准确、可解释和可验证的特点。我们还发布了用于评估OGRIT的遮挡区域数据集inDO、rounDO和OpenDDO。