DriveNetBench: An Affordable and Configurable Single-Camera Benchmarking System for Autonomous Driving Networks

Abstract

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.