轻量级激光雷达模型实现赛车端到端控制，实测性能优异且可低算力运行。
TinyLidarNet: 2D LiDAR-based End-to-End Deep Learning Model for F1TENTH Autonomous Racing
- 基于一维卷积网络，直接从激光雷达数据输出控制指令。
- 在未训练赛道上表现稳定，竞赛中获第3名。
- 可在低端微控制器上实时运行，适合资源受限场景。
先前研究已证明端到端深度学习在机器人导航中的有效性，即直接从原始传感数据生成控制信号。然而，现有大多数端到端导航方案以摄像头为主。本文提出TinyLidarNet，一种轻量级2D激光雷达驱动的端到端深度学习模型，用于自动驾驶赛车。使用TinyLidarNet的F1TENTH车辆在第12届F1TENTH自动驾驶大奖赛中获得第3名，验证了其竞争力。我们系统分析了其在未训练赛道上的表现及实时处理所需的计算资源。结果表明，基于一维卷积神经网络（1D CNN）的架构显著优于广泛使用的多层感知机（MLP）架构。此外，该模型可在低端微控制器单元（MCUs）上实现实时处理。
原文摘要 · Abstract (English)
Prior research has demonstrated the effectiveness of end-to-end deep learning for robotic navigation, where the control signals are directly derived from raw sensory data. However, the majority of existing end-to-end navigation solutions are predominantly camera-based. In this paper, we introduce TinyLidarNet, a lightweight 2D LiDAR-based end-to-end deep learning model for autonomous racing. An F1TENTH vehicle using TinyLidarNet won 3rd place in the 12th F1TENTH Autonomous Grand Prix competition, demonstrating its competitive performance. We systematically analyze its performance on untrained tracks and computing requirements for real-time processing. We find that TinyLidarNet's 1D Convolutional Neural Network (CNN) based architecture significantly outperforms widely used Multi-Layer Perceptron (MLP) based architecture. In addition, we show that it can be processed in real-time on low-end micro-controller units (MCUs).
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