Pedestrians and drivers interact closely in a wide range of environments. Autonomous vehicles (AVs) correspondingly face the need to predict pedestrians’ future trajectories in these same environments. Traditional model-based prediction methods have been limited to making predictions in highly structured scenes with signalized intersections, marked crosswalks, or curbs. Deep learning methods have instead leveraged datasets to learn predictive features that generalize across scenes, at the cost of model interpretability. This letter aims to achieve both widely applicable and interpretable predictions by proposing a risk-based attention mechanism to learn when pedestrians yield, and a model of vehicle influence to learn how yielding affects motion. A novel probabilistic method, Off the Sidewalk Predictions (OSP), uses these to achieve accurate predictions in both shared spaces and traditional scenes. Experiments on urban datasets demonstrate that the realtime method achieves state-of-the-art performance.

中文翻译：

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远离人行道：自动驾驶汽车共享空间中的行人预测

行人和司机在各种环境中密切互动。自动驾驶汽车 (AV) 相应地需要在这些相同的环境中预测行人的未来轨迹。传统的基于模型的预测方法仅限于在具有信号交叉口、标记人行横道或路缘的高度结构化场景中进行预测。相反，深度学习方法以模型可解释性为代价，利用数据集来学习跨场景泛化的预测特征。这封信旨在通过提出一种基于风险的注意力机制来学习行人何时让步，以及一个车辆影响模型来了解让步如何影响运动，从而实现广泛适用和可解释的预测。一种新颖的概率方法，Off the Sidewalk Predictions (OSP)，使用这些在共享空间和传统场景中实现准确预测。在城市数据集上的实验表明，实时方法实现了最先进的性能。