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Moving object detection is a critical task for autonomous vehicles. Asdynamic objects represent higher collision risk than static ones, our ownego-trajectories have to be planned attending to the future states of themoving elements of the scene. Motion can be perceived using temporalinformation such as optical flow. Conventional optical flow computation isbased on camera sensors only, which makes it prone to failure in conditionswith low illumination. On the other hand, LiDAR sensors are independent ofillumination, 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 MovingObject Detection (MOD) under low-light conditions by capturing motioninformation from both camera and LiDAR sensors. We demonstrate the impact ofour algorithm on KITTI dataset where we simulate a low-light environmentcreating a novel dataset "Dark KITTI". We obtain a 10.1% relative improvementon Dark-KITTI, and a 4.25% improvement on standard KITTI relative to ourbaselines. The proposed algorithm runs at 18 fps on a standard desktop GPUusing $256\times1224$ resolution images.

FuseMODNet: Real-Time Camera and LiDAR based Moving Object Detection for robust low-light Autonomous Driving