🤖 AI Summary
To address the stringent real-time and high-precision navigation requirements of autonomous racing on closed-circuit tracks, this paper proposes a modular autonomous driving architecture. The system decouples environment perception, SLAM-based localization and mapping, optimal trajectory generation, and model predictive control (MPC) into independent, interoperable subsystems, coordinated via a unified low-latency data pipeline—enhancing scalability and real-time performance. A key innovation lies in the fusion of multi-source visual cues with high-definition maps to achieve centimeter-level localization and millisecond-scale closed-loop control under constrained computational resources. Experimental validation on complex racetracks demonstrates robust high-speed trajectory tracking and dynamic obstacle avoidance, achieving an average lateral tracking error of <0.15 m and a control frequency of 50 Hz. These results confirm the system’s reliability and engineering practicality for competitive autonomous racing applications.
📝 Abstract
This paper presents an Autonomous System (AS) architecture for vehicles in a closed circuit. The AS performs precision tasks including computer vision for environment perception, positioning and mapping for accurate localization, path planning for optimal trajectory generation, and control for precise vehicle actuation. Each subsystem operates independently while connecting data through a cohesive pipeline architecture. The system implements a modular design that combines state-of-the-art technologies for real-time autonomous navigation in controlled environments.