论文标题
通过闭塞自动驾驶的可验证目标识别
Verifiable Goal Recognition for Autonomous Driving with Occlusions
论文作者
论文摘要
目标识别（GR）涉及推断其他车辆的目标，例如某个交界处出口，这可以更准确地预测其未来行为。在自动驾驶中，车辆可能会遇到许多不同的情况，并且由于阻塞而可能会部分观察到环境。我们提出了一种新颖的GR方法，名为目标识别，并在遮挡（OGRIT）下具有可解释的树。 Ogrit使用从车辆轨迹数据中学到的决策树来推断一组生成目标的概率。我们证明，由于阻塞，Ogrit可以处理丢失的数据，并使用相同的学术决策树在多种情况下进行推断，同时在计算上快速，准确，可解释和可验证。我们还发布了用于评估Ogrit的遮挡区域的Indo，Rounto和Openddo数据集。
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.
