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.

本文提出的LiRaNet是一种新颖的端到端轨迹预测方法，利用了雷达传感器信息和广泛使用的激光雷达和高清晰度地图，通过高效的时空雷达特征提取方案，实现了多个大规模数据集上最先进的性能表现，并获得了高加速度物体预测误差的52％降低和远距离物体预测误差的16％降低。

LiRaNet: 基于时空雷达融合的端到端轨迹预测