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Lightweight LiDAR-Based Cone Detection Framework Using Random Forest for Formula Student Driverless

arXiv · AI, language, vision and robotics · article · Sep 10, 2026 · UTC

Reliable, low-latency perception is crucial for Formula Student Driverless vehicles, yet many existing pipelines rely on deep learning and multi-sensor fusion, often requiring GPU acceleration. This paper presents a lightweight LiDAR-only perception pipeline tailored for CPU execution, combining ground removal, IMU-based motion compensation, DBSCAN clustering, and geometric feature-based Random Forest classification. Feature importance analysis reduced the model input from 12 to 7 features while preserving performance. Evaluated on 2,371 labeled clusters collected from real FSD events, the pip

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Evidence & attribution

arXiv · AI, language, vision and robotics · 2026-09-10T13:28:53.000Z

First collected: 2026-09-20T19:02:05.452Z. This is not the publication date.