Lane Level Traffic Prediction

Lane-level traffic prediction aims to accurately forecast traffic flow and vehicle behavior at the granularity of individual lanes, improving upon previous city- or road-level approaches. Current research emphasizes the use of deep learning models, particularly those incorporating graph neural networks and transformers, to leverage spatial relationships between lanes and temporal dependencies in traffic patterns. This detailed level of prediction is crucial for optimizing traffic management, enhancing autonomous driving safety, and enabling efficient infrastructure maintenance, as demonstrated by its application in real-time monitoring systems and automated map generation. The field is actively developing standardized benchmarks and datasets to facilitate more robust model comparisons and accelerate progress.