  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.
