To operate in open-ended environments where humans interact in complex, diverse ways, autonomous robots must learn to predict their behaviour, especially when that behavior is potentially dangerous to other agents or to the robot. However, reducing the risk of accidents requires prior knowledge of where potential collisions may occur and how. Therefore, we propose to gain this information by analyzing locations and speeds that commonly correspond to high-risk interactions within the dataset, and use it within training to generate better predictions in high risk situations. Through these location-based and speed-based re-weighting techniques, we achieve improved overall performance, as measured by most-likely FDE and KDE, as well as improved performance on high-speed vehicles, and vehicles within high-risk locations. 2023 IEEE International Conference on Robotics and Automation (ICRA)

翻译：为使自主机器人在人类以复杂多样方式交互的开放环境中运行，其必须学会预测人类行为，尤其是在该行为可能对其他智能体或机器人自身构成危险时。然而，降低事故风险需要预先掌握潜在碰撞可能发生的位置与方式。为此，我们提出通过分析数据集中通常对应于高风险交互的位置与速度来获取此类信息，并在训练中利用这些信息以在高风险情境下生成更优的预测。通过基于位置和基于速度的重新加权技术，我们在最可能终点误差和核密度估计指标上实现了整体性能提升，同时针对高速车辆及高风险区域内的车辆也取得了更优的预测表现。2023年IEEE机器人与自动化国际会议（ICRA）