  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. The benefits of our proposed framework are twofold. First, our
pipeline prioritizes detecting objects that pose a higher risk of collision to
the AV, giving more time for the AV to react to unsafe conditions. Second, it
also provides, on average, faster inference speeds compared to popular deep
neural network pipelines. We design our framework using the real-world
datasets, the Waymo Open Dataset, solving challenges arising from the
limitations of LiDAR sensors and object detection algorithms. We show that our
novel object detection pipeline prioritizes the detection of higher risk
objects while simultaneously achieving comparable accuracy and a 25% higher
average speed compared to camera inference only.

Computer Vision and Pattern Recognition