Simulating an Autonomous System in CARLA using ROS 2

📅 2025-11-14
📈 Citations: 0
Influential: 0
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🤖 AI Summary
To address challenges in high-speed, high-dynamics racing environments—namely, low perception robustness, difficulty in trajectory optimization, and poor simulation-to-reality transfer—this work designs and implements a ROS 2-based autonomous driving software stack for the Formula Student UK Driverless 2025 competition. The method integrates multi-modal sensing (360° LiDAR, ZED2i stereo vision, and GNSS/IMU), incorporates vehicle dynamics modeling, and enforces environmental constraints during trajectory generation. Developed in the CARLA simulator and deployed in real time on a Jetson AGX Orin edge platform, the system achieves stable cone detection within 35 meters and robust closed-loop control. Key contributions include a lightweight perception-planning co-design architecture tailored to racing scenarios, enabling seamless transfer from simulation to physical hardware—including chassis and actuators—and validated via real-vehicle closed-loop testing. This significantly enhances autonomous performance on complex tracks and improves engineering deployability.

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📝 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).
Problem

Research questions and friction points this paper is trying to address.

Develops autonomous racing software stack for FS-AI competition using CARLA simulator
Detects track boundary cones up to 35m using LiDAR and stereo camera sensors
Computes optimized trajectories considering vehicle dynamics and environmental conditions
Innovation

Methods, ideas, or system contributions that make the work stand out.

Uses ROS 2 with LiDAR and cameras for cone detection
Computes optimized trajectories considering vehicle dynamics
Implements full autonomous stack in ROS 2 for simulation
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