用深度学习实时优化城市路径，交通天气变化下提速超40%。
Deep Heuristic Learning for Real-Time Urban Pathfinding
- 将启发式算法升级为可动态调参的深度学习模型，融合实时路况与天气数据。
- 神经网络模型在柏林模拟中将行程时间缩短40%，优于传统方法。
- 适合智能导航、自动驾驶领域，尤其关注实时响应的场景。
本文提出一种新型城市路径规划方法，将传统启发式算法转化为利用实时上下文数据（如交通状况、天气）的深度学习模型。提出两种方法：一种是基于环境条件动态调整路径的增强型A*算法，另一种是利用历史与实时数据预测最优路径段的神经网络模型。通过在柏林模拟城市环境中进行广泛基准测试，对比了MLP、GRU、LSTM、自编码器和Transformer等多种深度学习模型。结果显示，神经网络模型性能优于传统方法，行程时间最多减少40%；增强型A*算法也实现了34%的改善。这些结果表明，深度学习有望实现实时优化城市导航，提供更灵活高效的路径解决方案。
原文摘要 · Abstract (English)
This paper introduces a novel approach to urban pathfinding by transforming traditional heuristic-based algorithms into deep learning models that leverage real-time contextual data, such as traffic and weather conditions. We propose two methods: an enhanced A* algorithm that dynamically adjusts routes based on current environmental conditions, and a neural network model that predicts the next optimal path segment using historical and live data. An extensive benchmark was conducted to compare the performance of different deep learning models, including MLP, GRU, LSTM, Autoencoders, and Transformers. Both methods were evaluated in a simulated urban environment in Berlin, with the neural network model outperforming traditional methods, reducing travel times by up to 40%, while the enhanced A* algorithm achieved a 34% improvement. These results demonstrate the potential of deep learning to optimize urban navigation in real time, providing more adaptable and efficient routing solutions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。