Convolution neural network application for road asset detection and classification in LiDAR point cloud

Abstract

Self-driving cars (or autonomous cars) can sense and navigate through an environment without any driver intervention. To achieve this task, they rely on vision sensors working in tandem with accurate algorithms to detect movable and non-movable objects around them. These vision sensors typically include cameras to identify static and non-static objects, Radio Detection and Ranging (RADAR) to detect the speed of the moving objects using Doppler effect and Light Detection and Ranging (LiDAR) to detect the distance to objects. In this paper, we explore a new usage of LiDAR data to classify static objects on the road. We present a pipeline to classify point cloud data grouped in volumetric pixels (voxels). We introduce a novel approach to point cloud data representation for processing within Convolution Neural Networks (CNN). Results show an accuracy exceeding 90% in the detection and classification of road edges, solid and broken lane markings, bike lanes, and lane center lines. Our data pipeline is capable of processing up to 20,000 points per 900ms on a server equipped with 2 Intel Xeon processors 8-core CPU with HyperThreading for a total of 32 threads and 2 NVIDIA Tesla K40 GPUs. Our model outperforms by 2% ResNet applied to camera images for the same road.