用常识推理提升自动驾驶系统判断力
Commonsense Reasoning-Aided Autonomous Vehicle Systems
- 融合图像数据与常识推理模型增强决策能力
- 让自动驾驶更准确、可解释且符合伦理
- 适合关注AI安全与可解释性的研究者
自动驾驶系统依赖机器学习技术，尽管深度学习在感知与分类任务中表现优异，但在道路情境的高层次推理方面仍显不足。本研究引入基于图像数据的常识推理模型，以提升自动驾驶系统的推理准确性，同时增强其可调节性、可解释性与伦理合规性。论文总结当前成果，并展望未来发展方向。
原文摘要 · Abstract (English)
Autonomous Vehicle (AV) systems have been developed with a strong reliance on machine learning techniques. While machine learning approaches, such as deep learning, are extremely effective at tasks that involve observation and classification, they struggle when it comes to performing higher level reasoning about situations on the road. This research involves incorporating commonsense reasoning models that use image data to improve AV systems. This will allow AV systems to perform more accurate reasoning while also making them more adjustable, explainable, and ethical. This paper will discuss the findings so far and motivate its direction going forward.
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