Spatiotemporal Modeling and AI Integration for Traffic Signal Countdown Prediction

Engineering notes

Key topics: autonomous driving, end-to-end, prediction, control. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Urban transport relies on efficient distribution of fuel and minimization of greenhouse gas emissions. The incorporation of accurate Signal Phase and Timing (SPaT) predictions into navigation systems along with Green Light Optimized Speed Advisory (GLOSA) technology will improve the predictive mobility aspect of urban transport. This paper provides a comprehensive study of the evolution of such technologies from statistical and shallow machine learning to RNNs and GNNs as deep learning technologies, and offers a comparison of the various alternatives of deep learning techniques, in particular, EVs of the Transformers such as Informer and FE-Transformer, for the prediction of long-range spatiotemporal dependencies. Despite the reductions in prediction accuracy provided by deep learning, for commercial applications, the limitations of the latency in the end-to-end prediction and the adaptive control logic remain a barrier. This paper reviews the most recent models and examples of industry applications from Google and Baidu, and provides a comprehensive guide for the use of predictive SPaT in the design of Green Light Optimized Speed Advisory (GLOSA) technology. The tools and techniques describe the elements of a carbon neutral Intelligent Transportation System and provide a valuable foundation for developing predictive, dynamically adjusting, and integrated technologies for next-generation traffic control systems.

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