LiDAR Multi-Task BeV model for Semantic Segmentation and Lane Detection

Engineering notes

Key topics: autonomous driving system, autonomous driving, bird's eye view, bev, lane detection, semantic segmentation, lidar, point cloud, perception. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Autonomous driving systems depend on various perception tasks to ensure a clear understanding of the road, requiring accuracy and speed. Traditionally, real-time performance was achieved by optimizing each task independently, resulting in inefficiencies when handling multiple tasks due to high memory and computation costs. Multi-Task Learning (MTL) has emerged as a solution to these issues by using generalized deep learning models that simultaneously infer multiple tasks, reducing Giga Floating Point Operations (GFLOPs), neural network size, and enhancing inference speeds. Additionally, when tasks share information, individual task performance can improve. This paper introduces a Multi-task Deep Learning model that performs both Semantic Segmentation and Lane Detection using LiDAR point clouds. By applying a Bird's Eye View transformation, the model predicts point-wise segmentation labels while detecting and clustering lane lines and curbsides into related instances. Experimental results demonstrate MTL's memory and time efficiency, halving resource usage and proving the feasibility of lane line detection solely from LiDAR data.

Links and sources

• Official / arXiv page

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