Simulating an Autonomous System in CARLA using ROS 2

TL;DR

This paper develops and tests a ROS 2-based autonomous racing system in CARLA, integrating multiple sensors and optimized trajectory planning to enhance high-speed autonomous driving performance for competitive racing.

Contribution

It presents a comprehensive ROS 2 software stack for autonomous racing in CARLA, including sensor integration, trajectory optimization, and validation for competitive driving scenarios.

Findings

Reliable cone detection up to 35 m using LiDAR and cameras

Effective trajectory planning considering vehicle dynamics and environmental factors

Successful validation of the system in CARLA before hardware deployment

Abstract

Autonomous racing offers a rigorous setting to stress test perception, planning, and control under high speed and uncertainty. This paper proposes an approach to design and evaluate a software stack for an autonomous race car in CARLA: Car Learning to Act simulator, targeting competitive driving performance in the Formula Student UK Driverless (FS-AI) 2025 competition. By utilizing a 360{\deg} light detection and ranging (LiDAR), stereo camera, global navigation satellite system (GNSS), and inertial measurement unit (IMU) sensor via ROS 2 (Robot Operating System), the system reliably detects the cones marking the track boundaries at distances of up to 35 m. Optimized trajectories are computed considering vehicle dynamics and simulated environmental factors such as visibility and lighting to navigate the track efficiently. The complete autonomous stack is implemented in ROS 2 and validated extensively in CARLA on a dedicated vehicle (ADS-DV) before being ported to the actual hardware, which includes the Jetson AGX Orin 64GB, ZED2i Stereo Camera, Robosense Helios 16P LiDAR, and CHCNAV Inertial Navigation System (INS).