Geo-Spatial and Temporal Relation Driven Vehicle Motion Prediction: A Geographic Perspective

The constraints imposed by traffic regulations and road geometries lead to a certain degree of geo-spatial similarity in vehicular behaviors, a factor overlooked in existing studies on vehicle motion prediction. To leverage the similarity, this paper introduces three novel vehicle motion prediction algorithms driven by geo-spatial and temporal relations within a classical Kalman filter (KF) framework, integrating Tobler's first law of geography and Newton's second law of motion. These algorithms utilize three specialized geo-spatial and temporal search mechanisms to extract geo-spatial dependencies from a spatial trajectory database and concurrently propel the KF prediction processes. Particularly, spatial interpolation and Dempster-Shafer reasoning modules are introduced to boost the algorithms' performance. We validate these methods using real driving data in public environments, demonstrating their state-of-the-art performance; and further examine their performance evolution in space domain and investigate their robustness in missing-measurement situations, which are unexplored in existing literature.