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

arXiv: https://arxiv.org/abs/2609.11527 · HTML: https://mlsanity.com/paper/2609.11527/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2026.

## Abstract (arXiv)

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.