将视觉感知与自适应控制结合，提升自动驾驶在恶劣天气下的安全性。
Integration of Computer Vision with Adaptive Control for Autonomous Driving Using ADORE
- 用上下文感知的视觉模型+ADORE自适应控制框架实现感知-决策-控制协同
- 在雨天等扰动条件下仍保持低延迟响应，成功识别限速与障碍物
- 适合关注自动驾驶安全系统设计的研究者和工程师
自动驾驶的安全性依赖于在不确定环境下感知与决策的无缝融合。尽管基于计算机视觉（CV）的YOLO模型在检测交通标志和障碍物上表现优异，但在天气变化或出现未知物体导致的漂移场景下性能下降。本文构建了一个基于CARLA模拟器的自动驾驶系统，通过ROS桥接将感知、决策与控制模块实时联动，并采用ADORE框架实现自适应控制。在晴天与扰动天气条件下进行测试，结果表明感知模型仍具鲁棒性，ADORE能快速调整车辆行为以应对限速与障碍物，响应延迟低。研究验证了深度学习感知与规则驱动自适应决策相结合，在提升车载安全关键系统方面的潜力。
原文摘要 · Abstract (English)
Ensuring safety in autonomous driving requires a seamless integration of perception and decision making under uncertain conditions. Although computer vision (CV) models such as YOLO achieve high accuracy in detecting traffic signs and obstacles, their performance degrades in drift scenarios caused by weather variations or unseen objects. This work presents a simulated autonomous driving system that combines a context aware CV model with adaptive control using the ADORE framework. The CARLA simulator was integrated with ADORE via the ROS bridge, allowing real-time communication between perception, decision, and control modules. A simulated test case was designed in both clear and drift weather conditions to demonstrate the robust detection performance of the perception model while ADORE successfully adapted vehicle behavior to speed limits and obstacles with low response latency. The findings highlight the potential of coupling deep learning-based perception with rule-based adaptive decision making to improve automotive safety critical system.
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