DriveNetBench: An Affordable and Configurable Single-Camera Benchmarking System for Autonomous Driving Networks (2505.01893v1)

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.

Overview of DriveNetBench: An Autonomous Driving Benchmarking System

The paper "DriveNetBench: An Affordable and Configurable Single-Camera Benchmarking System for Autonomous Driving Networks" addresses a critical gap in autonomous vehicle research by introducing a simplified yet effective benchmarking system. DriveNetBench is engineered to facilitate the evaluation of autonomous driving networks using cost-efficient hardware and software configurations, specifically leveraging single-camera setups. This approach mitigates the barriers posed by expensive multi-sensor systems, thereby democratizing access to autonomous driving research and development.

Key Contributions

The notable contributions of DriveNetBench include:

1. Affordability and Replicability: The system is designed with low-cost, readily available components, allowing researchers from diverse backgrounds to replicate the setup without significant financial outlays. The compact hardware kit is engineered from consumer-grade parts, streamlining its adoption for educators and hobbyists alike.
2. Modular Benchmarking Pipeline: DriveNetBench employs an open-source software framework that provides standardized data capturing, network integration, and evaluation metrics calculation. This framework enhances accessibility and usability of various driving models, ensuring compatibility with existing research workflows.
3. Configurable Experimentation: The system offers adjustable parameters for benchmarking, such as track layout and threshold definitions via configuration files. This user-friendly approach empowers researchers to tailor the system to specific experimental needs without necessitating code modifications.
4. Comprehensive Metric Analysis: DriveNetBench evaluates driving performance using metrics that account for both accuracy (e.g., path similarity) and efficiency (e.g., completion time). This dual-focus metric analysis facilitates a holistic understanding of network performance under real-world conditions.

Methodology and Evaluation

The benchmarking system employs a single-camera setup to record footage of autonomous vehicles navigating predefined tracks. DriveNetBench then analyzes this footage using various configurable settings to assess how well different models perform fundamental tasks like lane following and obstacle avoidance.

• Path Similarity: Dynamic Time Warping (DTW) and Fréchet Distance metrics are used to quantify the alignment of the driven path with a reference route. This measurement is crucial as it reflects the autonomous vehicle's ability to maintain a consistent trajectory.
• Completion Time: The time taken to complete a route is recorded, with penalties applied for any deviations from the track. This metric complements path similarity by assessing the speed and efficiency of network performance.
• Transformation Error Analysis: The paper discusses calibration error metrics to ensure accurate data projection onto digital tracks, highlighting how miscalibrations could impact evaluation results.

Implications

DriveNetBench presents a promising alternative to high-cost, multi-sensor systems and complex simulation environments commonly employed in autonomous driving research. By focusing on a single-camera setup, it opens up avenues for cost-effective research and educational applications, aligning with open science principles. The accessibility and modularity offered by DriveNetBench encourage wider participation in autonomous driving research, potentially accelerating innovation and collaboration in this field.

The development of DriveNetBench could lead to standardized testing protocols for vision-only autonomous systems, fostering comparative analysis among various models and methodologies. Its open-source nature further encourages community-driven enhancements and iterative development, maintaining transparency in research findings.

Future Directions

Future research may focus on expanding DriveNetBench to accommodate stereo cameras and additional sensor integrations, potentially offering enhanced capabilities for depth perception and distance estimation. Such developments could strengthen the system's applicability in more complex real-world scenarios. Moreover, extending its application to outdoor tracks would provide invaluable insights into the robustness of autonomous networks across diverse testing environments.

In conclusion, DriveNetBench represents a significant step forward in the evaluation of autonomous driving networks, providing a practical, low-cost, and standardized framework for both research and educational purposes. Its contributions to the field suggest meaningful advancements in the accessibility and rigor of autonomous vehicle testing methodologies.