边缘AI让自动驾驶在恶劣天气下决策更快更准。
Edge AI-Powered Real-Time Decision-Making for Autonomous Vehicles in Adverse Weather Conditions
- 在设备端融合CNN、RNN与强化学习，实现感知与控制协同优化。
- 处理时间减少40%，感知准确率提升25%，显著降低延迟。
- 适合对实时性要求高的自动驾驶系统，尤其适用于复杂路况。
自动驾驶汽车正重塑现代交通，但在暴雨、浓雾和大雪等恶劣天气下，其可靠性与安全性受到严重挑战。这些环境因素会削弱摄像头、激光雷达和雷达的性能，导致情境感知能力下降，事故风险上升。传统基于云端的AI系统存在通信延迟，难以满足实时导航所需的快速决策。本文提出一种新型边缘AI驱动的实时决策框架，通过集成卷积神经网络（CNN）与循环神经网络（RNN）提升感知能力，并采用强化学习（RL）策略优化不确定环境中的车辆控制。该系统在边缘节点处理数据，显著降低决策延迟并增强自适应性。在CARLA仿真驾驶场景及Waymo Open Dataset真实数据集上进行评估，涵盖多种天气条件。实验结果表明，相比传统云系统，本模型处理时间减少40%，感知准确率提升25%。这些成果凸显了边缘AI在提升自动驾驶自主性、安全性和效率方面的潜力，为复杂现实环境下的可靠自动驾驶技术铺平道路。
原文摘要 · Abstract (English)
Autonomous vehicles (AVs) are transforming modern transportation, but their reliability and safety are significantly challenged by harsh weather conditions such as heavy rain, fog, and snow. These environmental factors impair the performance of cameras, LiDAR, and radar, leading to reduced situational awareness and increased accident risks. Conventional cloud-based AI systems introduce communication delays, making them unsuitable for the rapid decision-making required in real-time autonomous navigation. This paper presents a novel Edge AI-driven real-time decision-making framework designed to enhance AV responsiveness under adverse weather conditions. The proposed approach integrates convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for improved perception, alongside reinforcement learning (RL)-based strategies to optimize vehicle control in uncertain environments. By processing data at the network edge, this system significantly reduces decision latency while improving AV adaptability. The framework is evaluated using simulated driving scenarios in CARLA and real-world data from the Waymo Open Dataset, covering diverse weather conditions. Experimental results indicate that the proposed model achieves a 40% reduction in processing time and a 25% enhancement in perception accuracy compared to conventional cloud-based systems. These findings highlight the potential of Edge AI in improving AV autonomy, safety, and efficiency, paving the way for more reliable self-driving technology in challenging real-world environments.
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