Motion prediction of vehicles is critical but challenging due to the uncertainties in complex environments and the limited visibility caused by occlusions and limited sensor ranges. In this paper, we study a new task, safety-aware motion prediction with unseen vehicles for autonomous driving. Unlike the existing trajectory prediction task for seen vehicles, we aim at predicting an occupancy map that indicates the earliest time when each location can be occupied by either seen and unseen vehicles. The ability to predict unseen vehicles is critical for safety in autonomous driving. To tackle this challenging task, we propose a safety-aware deep learning model with three new loss functions to predict the earliest occupancy map. Experiments on the large-scale autonomous driving nuScenes dataset show that our proposed model significantly outperforms the state-of-the-art baselines on the safety-aware motion prediction task. To the best of our knowledge, our approach is the first one that can predict the existence of unseen vehicles in most cases. Project page at {\url{https://github.com/xrenaa/Safety-Aware-Motion-Prediction}}.
翻译:由于复杂环境中的不确定性以及隔离和有限的传感器范围造成的可见度有限,对车辆的机动性预测至关重要,但具有挑战性。在本文件中,我们研究了一项新的任务,即对无人驾驶的无人驾驶车辆进行安全意识运动预测。与现有对被观察车辆的轨迹预测任务不同,我们的目标是预测一个占用图,其中显示每个地点的最早时间,既可见又看不见的车辆;预测无人驾驶车辆的能力对自主驾驶的安全性至关重要。为了应对这一具有挑战性的任务,我们提出了一个安全觉醒的深层次学习模型,其中有三个新的损失功能来预测最早的占用图。关于大规模自主驾驶的nuScenses数据集的实验表明,我们提议的模型大大超过了安全意识机动性预测任务的最新基线。据我们所知,我们的方法是第一个能够预测大多数情况下存在隐蔽车辆的方法。<https://github.com/xrenaa/Safty-Aware-Moti-Pretition-Pretitionlate>项目网页。