用ROS 2在CARLA中构建自动驾驶赛车系统，实测35米外精准识别赛道锥桶。
Simulating an Autonomous System in CARLA using ROS 2
- 基于ROS 2集成多传感器，实现高精度环境感知
- 支持35米内识别赛道边界，优化轨迹兼顾动力学与光照条件
- 全流程仿真验证后部署至真实硬件，适配竞赛场景
自动驾驶赛车为感知、规划与控制在高速和不确定性下的性能提供了严苛测试环境。本文提出一种面向公式学生英国无人驾驶竞赛（FS-AI 2025）的自动驾驶赛车软件栈设计与评估方法，基于CARLA（Car Learning to Act）仿真器实现。系统采用360°激光雷达（LiDAR）、双目相机、全球导航卫星系统（GNSS）和惯性测量单元（IMU），通过ROS 2进行数据融合，可在高达35米的距离下可靠检测标记赛道边界的锥桶。通过考虑车辆动力学及模拟光照、能见度等环境因素，生成优化轨迹以高效通过赛道。整个自主系统在专用车辆（ADS-DV）上于CARLA中完成充分验证，随后移植至实际硬件平台——包含Jetson AGX Orin 64GB、ZED2i双目相机、Robosense Helios 16P LiDAR和CHCNAV惯性导航系统（INS）。
原文摘要 · Abstract (English)
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° 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).
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