Autonomous driving paper index
Autonomous Lane-Keeping Using LaneNet and ROS in CARLA Simulation
One-line summary
This study develops an autonomous lane-keeping and steering control system using the CARLA simulator, ROS, and LaneNet for deep learning-based lane detection.
Engineering notes
Key topics: autonomous driving, lane detection, carla, adas, real-world driving, control. See the paper for implementation details and experimental results.
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
This study develops an autonomous lane-keeping and steering control system using the CARLA simulator, ROS, and LaneNet for deep learning-based lane detection. The system integrates carla_ros_bridge to publish real-time camera images and vehicle data as ROS topics, enabling external nodes to process them. LaneNet performs pixel-wise segmentation to detect multiple lane markings, with results output in a parameterized format. Control algorithms (e.g., PID or LQR) compute the vehicle’s deviation from the lane center and provide steering commands to simulate real-world driving. Experimental results show robust performance under varying weather and traffic conditions, even with challenging lighting and lane markings. Future improvements with additional sensors and advanced models will enhance real-world applicability for autonomous driving and ADAS.
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