Smart-City Spatiotemporal Data-Driven Trajectory Prediction for Autonomous Vehicles via Attention Mechanisms and Self-Supervised Learning

In smart-city deployments, vast spatiotemporal trajectory data are generated by consumer-electronics sensors, such as in-vehicle devices. Predicting the future movements of traffic participants in such complex environments is a fundamental task in autonomous driving. In this study, we proposed a trajectory prediction strategy based on attention mechanism and self-supervised learning. First, the agent and map features are extracted, and a set of searched candidate paths for the target agent is constructed based on prior knowledge. Leveraging these candidate paths, a feature selection mechanism is introduced to enable the model to adaptively select relevant feature vectors. This enhances the attention mechanism’s ability to model agent interactions. Furthermore, self-supervised learning is utilized to design auxiliary tasks, including path classification and agent interaction strength estimation, which further improve trajectory prediction performance. Extensive experiments on the nuScenes dataset demonstrate that the prediction model is competitive with the state-of-the-art models, reducing minADE by up to 32.6% and minFDE by 40.8% compared to the latest DICE model, while maintaining real-time inference with an average runtime of 14.9 ms per frame for 8 agents. Ablation studies indicate that each component is essential to ensure prediction performance. Finally, closed-loop Vehicle-in-the-Loop experiments validate the model’s ability to generate meaningful multimodal predictions and effectively integrate them into downstream planning tasks, the prediction-aware planner yields enhanced safety and comfort over existing baselines, and its low computational cost highlights strong potential for deployment on consumer electronics platforms, laying the groundwork for intelligent spatiotemporal analytics in smart-city traffic management.