用单摄像头低成本搭建自动驾驶模型评测系统
DriveNetBench: An Affordable and Configurable Single-Camera Benchmarking System for Autonomous Driving Networks
- 基于市售硬件和开源软件，实现单摄像头评测
- 支持目标检测、车道跟随等模型，测试精度与速度一致
- 适合研究者和教育场景，开箱即用易复现
验证自动驾驶神经网络通常需要昂贵设备和复杂配置，限制了研究人员和教育者的使用。我们提出DriveNetBench，一个低成本、可配置的单摄像头基准测试系统，用于评估自动驾驶网络。该系统采用廉价通用硬件和灵活软件栈，可轻松集成多种驾驶模型（如目标检测、车道跟随），并在真实场景中实现标准化评估。系统模拟常见驾驶条件，提供一致、可重复的性能指标。初步实验表明，该系统能在受控环境中有效测量模型推理速度与准确率。主要贡献包括低成本、开源可复现性，以及与现有工作流无缝集成，使自动驾驶研究更易获取。
原文摘要 · Abstract (English)
Validating autonomous driving neural networks often demands expensive equipment and complex setups, limiting accessibility for researchers and educators. We introduce DriveNetBench, an affordable and configurable benchmarking system designed to evaluate autonomous driving networks using a single-camera setup. Leveraging low-cost, off-the-shelf hardware, and a flexible software stack, DriveNetBench enables easy integration of various driving models, such as object detection and lane following, while ensuring standardized evaluation in real-world scenarios. Our system replicates common driving conditions and provides consistent, repeatable metrics for comparing network performance. Through preliminary experiments with representative vision models, we illustrate how DriveNetBench effectively measures inference speed and accuracy within a controlled test environment. The key contributions of this work include its affordability, its replicability through open-source software, and its seamless integration into existing workflows, making autonomous vehicle research more accessible.
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