An Optimized Density-Based Lane Keeping System for A Cost-Efficient Autonomous Vehicle Platform: AurigaBot V1

2025-04-05

AI Summary

To address the challenge of developing cost-effective autonomous driving platforms under computational resource constraints, this paper introduces AurigaBot V1—a lightweight, four-wheeled autonomous vehicle platform designed for algorithm validation and research. Methodologically, it integrates a histogram-driven density-based clustering approach for robust landmark tracking, a lightweight hybrid lateral controller combining PID and Pure Pursuit, and a modular, transferable system architecture. An embedded perception–control stack is implemented using OpenCV and ROS, augmented by a systematic data enhancement strategy. Experimental results demonstrate robust lane tracking under varying illumination conditions, with trajectory smoothness improved by 32%; object detection achieves an mAP of 86.4%, and automated parking success rate reaches 94.7%. These outcomes significantly enhance functional completeness and engineering scalability of resource-constrained autonomous platforms.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Vision for Robotics & Autonomous Driving
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

The development of self-driving cars has garnered significant attention from researchers, universities, and industries worldwide. Autonomous vehicles integrate numerous subsystems, including lane tracking, object detection, and vehicle control, which require thorough testing and validation. Scaled-down vehicles offer a cost-effective and accessible platform for experimentation, providing researchers with opportunities to optimize algorithms under constraints of limited computational power. This paper presents a four-wheeled autonomous vehicle platform designed to facilitate research and prototyping in autonomous driving. Key contributions include (1) a novel density-based clustering approach utilizing histogram statistics for landmark tracking, (2) a lateral controller, and (3) the integration of these innovations into a cohesive platform. Additionally, the paper explores object detection through systematic dataset augmentation and introduces an autonomous parking procedure. The results demonstrate the platform's effectiveness in achieving reliable lane tracking under varying lighting conditions, smooth trajectory following, and consistent object detection performance. Though developed for small-scale vehicles, these modular solutions are adaptable for full-scale autonomous systems, offering a versatile and cost-efficient framework for advancing research and industry applications.

Problem

• Optimizing lane tracking using density-based clustering for autonomous vehicles
• Developing cost-effective small-scale platforms for autonomous driving research
• Integrating modular solutions adaptable to full-scale autonomous systems

Innovation

• Density-based clustering for landmark tracking
• Lateral controller for vehicle control
• Modular solutions adaptable for full-scale systems