论文标题
在人迹罕至的人行道上：自动驾驶汽车共享空间中的行人预测
Off The Beaten Sidewalk: Pedestrian Prediction In Shared Spaces For Autonomous Vehicles
论文作者
论文摘要
行人和驾驶员在广泛的环境中紧密相互作用。自动驾驶汽车（AV）相应地面临着在这些相同环境中预测行人未来轨迹的需求。传统的基于模型的预测方法仅限于在高度结构化的场景中进行信号交叉点，明显的人行横道或路缘的预测。相反，深度学习方法利用数据集来学习以模型解释性为代价的跨场景的预测功能。本文旨在通过提出一种基于风险的注意机制来学习何时屈服的风险的注意机制，以及一种了解屈服如何影响运动的模型，以实现广泛适用和可解释的预测。在人行道预测（OSP）中，一种新颖的概率方法使用这些方法在共享空间和传统场景中都能实现准确的预测。 Urban数据集上的实验表明，实时方法实现了最先进的性能。
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 paper 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.
