  Moving object detection is a critical task for autonomous vehicles. As
dynamic objects represent higher collision risk than static ones, our own
ego-trajectories have to be planned attending to the future states of the
moving elements of the scene. Motion can be perceived using temporal
information such as optical flow. Conventional optical flow computation is
based on camera sensors only, which makes it prone to failure in conditions
with low illumination. On the other hand, LiDAR sensors are independent of
illumination, as they measure the time-of-flight of their own emitted lasers.
In this work, we propose a robust and real-time CNN architecture for Moving
Object Detection (MOD) under low-light conditions by capturing motion
information from both camera and LiDAR sensors. We demonstrate the impact of
our algorithm on KITTI dataset where we simulate a low-light environment
creating a novel dataset "Dark KITTI". We obtain a 10.1% relative improvement
on Dark-KITTI, and a 4.25% improvement on standard KITTI relative to our
baselines. The proposed algorithm runs at 18 fps on a standard desktop GPU
using $256\times1224$ resolution images.
