LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion
LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion
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LiRaNet：使用时空雷达融合进行端到端轨迹预测
DOI：
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发表时间：
2020
期刊：
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通讯作者：
R. Urtasun
中科院分区：
文献类型：
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作者：
Meet Shah;Zhi;A. Laddha;Matthew Langford;B. Barber;Sidney Zhang;Carlos Vallespi;R. Urtasun
在本文中，我们提出了 LiRaNet，这是一种新颖的端到端轨迹预测方法，该方法利用雷达传感器信息以及广泛使用的激光雷达和高清 (HD) 地图。汽车雷达提供丰富的补充信息，可实现更远距离的车辆检测以及瞬时径向速度测量。然而，有些因素使得激光雷达和雷达信息的融合具有挑战性，例如雷达测量的角分辨率相对较低、稀疏性以及缺乏与激光雷达的精确时间同步。为了克服这些挑战，我们提出了一种高效的时空雷达特征提取方案，该方案在多个大规模数据集上实现了最先进的性能。此外，通过合并雷达信息，我们发现高加速度物体的预测误差降低了 52%，而远距离物体的预测误差降低了 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.