论文标题
Liranet：使用时空雷达融合的端到端轨迹预测
LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion
论文作者
论文摘要
在本文中，我们提出了Liranet，这是一种新型的端到端轨迹预测方法，它利用雷达传感器信息以及广泛使用的激光雷达和高清图（HD）地图。汽车雷达提供丰富的互补信息，可进行更长距离的车辆检测以及瞬时径向速度测量。但是，有些因素使LiDar和雷达信息的融合构成了挑战，例如雷达测量值相对较低，它们的稀疏性以及与LiDAR缺乏精确的时间同步。为了克服这些挑战，我们提出了一种有效的时空雷达特征提取方案，该方案在多个大型数据集上实现了最先进的性能。further，通过纳入雷达信息，我们显示出高加速度的对象的预测错误减少了52％，而在较长范围内，预测误差降低了16％。
In this paper, we present LiRaNet, a novel end-to-end trajectory prediction method which utilizes radar sensor information along with widely used lidar and high definition (HD) maps. Automotive radar provides rich, complementary information, allowing for longer range vehicle detection as well as instantaneous radial velocity measurements. However, there are factors that make the fusion of lidar and radar information challenging, such as the relatively low angular resolution of radar measurements, their sparsity and the lack of exact time synchronization with lidar. To overcome these challenges, we propose an efficient spatio-temporal radar feature extraction scheme which achieves state-of-the-art performance on multiple large-scale datasets.Further, by incorporating radar information, we show a 52% reduction in prediction error for objects with high acceleration and a 16% reduction in prediction error for objects at longer range.
