# 面向 Formula Student Driverless 的基于 Random Forest 的轻量级 LiDAR 锥桶检测框架

> 原文链接：https://robotics.hellobike.com/papers/2026-09-10-2609-11527
> 频道：papers | 更新日期：2026-09-10 | 标签：arxiv, cs.RO
> 核心摘要：提出面向CPU的LiDAR-only轻量感知流水线，并在真实FSD赛事采集的标注簇上验证低延迟锥桶检测。

# 面向 Formula Student Driverless 的基于 Random Forest 的轻量级 LiDAR 锥桶检测框架

::: v-pre
> 原文链接：[Lightweight LiDAR-Based Cone Detection Framework Using Random Forest for Formula Student Driverless](https://arxiv.org/abs/2609.11527)（arXiv 2609.11527 · 发布日期：2026-09-10）

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

提出面向CPU的LiDAR-only轻量感知流水线，并在真实FSD赛事采集的标注簇上验证低延迟锥桶检测。

## 核心要点

- 面向FSD车辆对可靠、低延迟感知的需求，避免依赖深度学习、多传感器融合和GPU加速。
- 流水线结合地面移除、IMU运动补偿、DBSCAN聚类与几何特征Random Forest分类。
- 特征重要性分析将输入从12项降至7项，同时保持性能。
- 在2,371个真实FSD赛事标注簇上达到98.33% F1，CPU端到端运行3.13 ms，并发布数据集、标注工具和模型。

**验证范围与局限**：验证范围为真实FSD赛事采集的2,371个标注簇，运行环境为CPU-only硬件。

## 英文摘要 (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.

作者：Márk Mező-Kerekes, Péter Praksz, Chang Liu
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## 原始参考来源 (Sources)

- [arXiv 2609.11527：Lightweight LiDAR-Based Cone Detection Framework Using Random Forest for Formula Student Driverless](https://arxiv.org/abs/2609.11527)