LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion
This work addresses trajectory prediction for autonomous vehicles, showing incremental improvements by incorporating radar data.
The paper tackles the problem of trajectory prediction in autonomous driving by fusing lidar and radar sensor data, achieving a 52% reduction in prediction error for high-acceleration objects and a 16% reduction for long-range objects.
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.