Autonomous driving paper index
Integrated Detection of Traffic Signs and Lane Changes Using Deep Learning for Autonomous Driving Systems
One-line summary
Abstract: This research presents a modular deep learning- based pipeline designed to integrate traffic sign detection, lane segmentation, and lane change prediction for autonomous driv- ing.
Engineering notes
Key topics: autonomous driving system, autonomous driving, lane change, adas, prediction. 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
Abstract: This research presents a modular deep learning- based pipeline designed to integrate traffic sign detection, lane segmentation, and lane change prediction for autonomous driv- ing. ThesystemutilizesYOLOv8 forobjectdetection (trafficsigns andspeedbreakers),andYOLOv8-segforlanesegmentation. A custom logic module processes lane masks for accurate lane changeprediction,whileGoogleText-to-Speech(gTTS)generates audio alerts. The pipeline supports real-time performance with GPU acceleration and processes videos offline with visual and verbalfeedback.Resultssuggesthighprecisionindetection and practical application for advanced driver assistance systems (ADAS).
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