发表机构
Budapest University of Technology and Economics; HUN-REN Institute for Computer Science and Control (SZTAKI)（布达佩斯技术与经济大学; 匈牙利国家研究网络计算机科学与控制研究所（SZTAKI））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对大学生方程式无人驾驶，提出仅用激光雷达的轻量级CPU感知流程，采用地面去除、IMU补偿、DBSCAN聚类及随机森林分类，特征缩减至7个，F1达98.33%，耗时3.13毫秒，并开源数据集与工具。
AI 中文摘要
可靠、低延迟的感知对于大学生方程式无人驾驶车辆至关重要，然而许多现有流程依赖深度学习和多传感器融合，通常需要GPU加速。本文提出了一种专为CPU执行设计的仅使用激光雷达的轻量级感知流程，结合地面去除、基于IMU的运动补偿、DBSCAN聚类和基于几何特征的随机森林分类。特征重要性分析将模型输入从12个特征减少到7个特征，同时保持性能。在从真实FSD赛事中收集的2,371个标记聚类上进行评估，该流程在仅使用CPU的硬件上实现了98.33%的F1分数和3.13毫秒的端到端运行时间。发布的数据集、标注工具和训练模型为其他资源受限的自动驾驶赛车队提供了实用且可复现的基线。
英文摘要
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.
Comments9 pages, 2 figures, 3 tables. Accepted at the 5th International Conference on Cognitive Mobility (CogMob 2026)
