LiDAR Cluster First and Camera Inference Later: A New Perspective Towards Autonomous Driving

Understand

Object detection in state-of-the-art Autonomous Vehicles (AV) framework relies heavily on deep neural networks.

• Typically, these networks perform object detection uniformly on the entire camera LiDAR frames.
• However, this uniformity jeopardizes the safety of the AV by giving the same priority to all objects in the scenes regardless of their risk of collision to the AV.
• In this paper, we present a new end-to-end pipeline for AV that introduces the concept of LiDAR cluster first and camera inference later to detect and classify objects.