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

• URL: http://arxiv.org/abs/2609.11527v1
• Date: Thu, 10 Sep 2026 13:28:53 GMT
• Title: Lightweight LiDAR-Based Cone Detection Framework Using Random Forest for Formula Student Driverless
• Abstract summary: This paper presents a lightweight LiDAR-only perception pipeline tailored for CPU execution.<n>It combines ground removal, IMU-based motion compensation, DBSCAN clustering, and geometric feature-based Random Forest classification.<n>The pipeline achieves an F1-score of 98.33% and an end-to-end runtime of 3.13 ms on CPU-only hardware.
• Score: 2.675957073325146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 pipeline achieves an F1-score of 98.33% and an end-to-end runtime of 3.13 ms on CPU-only hardware. The released dataset, labeling tool, and trained models provide a practical and reproducible baseline for other resource-constrained autonomous racing teams.

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