Design of a Drivable Area Segmentation Network Using a Field Programmable Gate Array Based on Light Detection and Ranging

With the continuing development of autonomous driving systems, the drivable area segmentation problem has become an indispensable part of self-driving cars. The drivable area segmentation technology introduces many features to self-driving car technology, such as providing information about the surrounding environment, assisting decision-making mechanisms, selecting appropriate driving paths, and avoiding obstacles. In addition, accurate segmentation of drivable areas is crucial for improving self-driving navigation and avoiding obstacles. To enable effective identification of drivable areas on the basis of environmental information, this study designed a drivable area segmentation network named DASNet. The proposed DASNet utilizes depthwise separable convolution as a basis/platform for feature extraction to enable features to be efficiently extracted to reduce both the computational load and required network parameters. Additionally, the proposed DASNet enhances the inference speed of the network while maintaining high accuracy. In order to reduce point cloud density without compromising essential information, we perform sampling and fusion on the point clouds in both Cartesian and spherical coordinate spaces during data preprocessing. The fused point cloud serves as the input to DASNet, while the output is the drivable area map. Finally, the proposed DASNet is ported to a field programmable gate array to achieve real-time drivable area detection. This study employs the publicly available KITTI dataset and proposed wooded environment dataset for experimental evaluation. By evaluating the model on two distinct datasets, we aim to demonstrate the capabilities across three types of urban road scenes and more densely wooded environments. The experimental results indicate that DASNet achieved an F1-score of 0.9449 and an inference speed of 9.32 ms on the KITTI dataset. Furthermore, the proposed DASNet was applied to autonomous vehicles operating in a wooded environment, achieving an F1-score of 98.64%.