论文标题
腕表：对平行自主权的自信意图认可
CARPAL: Confidence-Aware Intent Recognition for Parallel Autonomy
论文作者
论文摘要
对于高级驾驶员援助系统来说，预测驾驶员意图是一项艰巨而至关重要的任务。对预测的传统信心措施通常会忽略预测轨迹的方式影响下游决策以确保安全驾驶。在本文中，我们提出了一个新颖的多任务意图识别神经网络，该网络不仅可以预测概率驱动器轨迹，还可以预测与给定下游任务的预测相关的效用统计。我们建立了平行自主权的决策标准，该标准考虑了驾驶员轨迹预测在实时决策中的作用，通过推理估计的特定于任务的效用统计。我们通过考虑可能导致不安全决定的下游计划任务中的不确定性来进一步提高系统的鲁棒性。我们在现实的城市驾驶数据集上测试了我们的在线系统，并与基线方法相比，证明了其在召回和后果指标方面的优势，并证明了其在干预和警告用例中的有效性。
Predicting driver intentions is a difficult and crucial task for advanced driver assistance systems. Traditional confidence measures on predictions often ignore the way predicted trajectories affect downstream decisions for safe driving. In this paper, we propose a novel multi-task intent recognition neural network that predicts not only probabilistic driver trajectories, but also utility statistics associated with the predictions for a given downstream task. We establish a decision criterion for parallel autonomy that takes into account the role of driver trajectory prediction in real-time decision making by reasoning about estimated task-specific utility statistics. We further improve the robustness of our system by considering uncertainties in downstream planning tasks that may lead to unsafe decisions. We test our online system on a realistic urban driving dataset, and demonstrate its advantage in terms of recall and fall-out metrics compared to baseline methods, and demonstrate its effectiveness in intervention and warning use cases.
