Autonomous driving paper index
A Realistic Radar Simulator for End-to-End Autonomous Driving in CARLA
One-line summary
To address these limitations, we present CShenron, a radar simulation framework integrated into CARLA, which generates realistic radar measurements by fusing LiDAR and camera data.
Engineering notes
CARLA, a widely adopted open-source simulator, provides a simplistic radar model that fails to capture the complex physical and material-dependent behavior of real-world radar.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
The advancement of self-driving technology is driven by the need for robust perception and navigation systems. Simulators for autonomous driving facilitate the rapid development and testing of navigation algorithms; however, a key issue for most is their inaccurate modeling of the radar sensor. This is a significant drawback as radars offer robust sensing capabilities in adverse weather conditions and occlusions. CARLA, a widely adopted open-source simulator, provides a simplistic radar model that fails to capture the complex physical and material-dependent behavior of real-world radar. To address these limitations, we present CShenron, a radar simulation framework integrated into CARLA, which generates realistic radar measurements by fusing LiDAR and camera data. C-Shenron also supports configurable radar parameters, multiple sensor placements, and scalable dataset generation. Our evaluations demonstrate that radar-camera fusion models, trained with C-Shenron’s generated data, achieve performance equivalent to traditional LiDAR-camera baselines on key metrics from the CARLA leaderboard.
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