  Fusing Radar and Lidar sensor data can fully utilize their complementary
advantages and provide more accurate reconstruction of the surrounding for
autonomous driving systems. Surround Radar/Lidar can provide 360-degree view
sampling with the minimal cost, which are promising sensing hardware solutions
for autonomous driving systems. However, due to the intrinsic physical
constraints, the rotating speed of surround Radar, and thus the frequency to
generate Radar data frames, is much lower than surround Lidar. Existing
Radar/Lidar fusion methods have to work at the low frequency of surround Radar,
which cannot meet the high responsiveness requirement of autonomous driving
systems.This paper develops techniques to fuse surround Radar/Lidar with
working frequency only limited by the faster surround Lidar instead of the
slower surround Radar, based on the state-of-the-art object detection model
MVDNet. The basic idea of our approach is simple: we let MVDNet work with
temporally unaligned data from Radar/Lidar, so that fusion can take place at
any time when a new Lidar data frame arrives, instead of waiting for the slow
Radar data frame. However, directly applying MVDNet to temporally unaligned
Radar/Lidar data greatly degrades its object detection accuracy. The key
information revealed in this paper is that we can achieve high output frequency
with little accuracy loss by enhancing the training procedure to explore the
temporal redundancy in MVDNet so that it can tolerate the temporal unalignment
of input data. We explore several different ways of training enhancement and
compare them quantitatively with experiments.
