• Research on Algorithm Optimization of Object‑Detection Image Recognition for Autonomous Driving Under Complex Illumination Conditions (复杂光照条件下自动驾驶目标检测图像识别算法优化研究)
  
• Research on On‑vehicle Image Semantic Segmentation and Road Condition Perception Under Adverse Weather (Rain, Fog, Ice and Snow) (恶劣天气（雨雾冰雪）车载图像语义分割技术与路况感知研究)
• Transformer‑based Behavior Recognition and Prediction of Pedestrians and Non‑motor‑Vehicles for Autonomous Driving (基于 Transformer 的自动驾驶行人与非机动车行为识别与预判)
• Research on Obstacle Perception Technology of Autonomous Driving Based on Image Recognition Under Camera‑Radar Fusion (摄像头‑雷达融合下图像识别自动驾驶障碍物感知技术研究)
• Research on Image‑recognition Algorithm for Worn and Occluded Lane Lines and Its Application in Lane Departure Warning (磨损遮挡车道线图像识别算法及车道偏离预警应用研究)
• Small‑sample Training and Lightweight‑Model Design for Traffic‑Sign Image Recognition in Autonomous Driving (自动驾驶交通标志图像识别小样本训练与轻量化模型设计)
• Research on Adversarial‑Sample Attack Risks and Security Protection Strategies for Image Recognition in Autonomous Driving (自动驾驶图像识别对抗样本攻击风险与安全防护策略研究)
• Research on Obstacle Detection of Surround‑view Image‑recognition Technology in Automatic Parking Scenarios (环视图像识别技术在自动泊车场景中的障碍物检测研究)

This topic focuses on the core image‑recognition technologies applied in autonomous driving. As a vital component of environment perception for intelligent vehicles, such technologies are critical to the safe deployment of autonomous driving and the development of intelligent transportation. Targeting autonomous‑driving learners, entry‑level industry practitioners and students of relevant majors, this session aims to clarify the working principles, key application scenarios, industry pain points and development trends of these technologies.