  This paper presents the FPGA design of a convolutional neural network (CNN)
based road segmentation algorithm for real-time processing of LiDAR data. For
autonomous vehicles, it is important to perform road segmentation and obstacle
detection such that the drivable region can be identified for path planning.
Traditional road segmentation algorithms are mainly based on image data from
cameras, which is subjected to the light condition as well as the quality of
road markings. LiDAR sensor can obtain the 3D geometry information of the
vehicle surroundings with very high accuracy. However, it is a computational
challenge to process a large amount of LiDAR data at real-time. In this work, a
convolutional neural network model is proposed and trained to perform semantic
segmentation using the LiDAR sensor data. Furthermore, an efficient hardware
design is implemented on the FPGA that can process each LiDAR scan in 16.9ms,
which is much faster than the previous works. Evaluated using KITTI road
benchmarks, the proposed solution achieves high accuracy of road segmentation.
