SSP: Single Shot Future Trajectory Prediction
This addresses trajectory forecasting for autonomous systems in crowded settings, representing an incremental improvement with practical functionality.
The paper tackles future trajectory prediction for autonomous agents in crowded environments by proposing a single-shot method with constant time complexity, achieving robust performance on ETH, UCY, and SDD datasets compared to state-of-the-art methods.
We propose a robust solution to future trajectory forecast, which can be practically applicable to autonomous agents in highly crowded environments. For this, three aspects are particularly addressed in this paper. First, we use composite fields to predict future locations of all road agents in a single-shot, which results in a constant time complexity, regardless of the number of agents in the scene. Second, interactions between agents are modeled as a non-local response, enabling spatial relationships between different locations to be captured temporally as well (i.e., in spatio-temporal interactions). Third, the semantic context of the scene are modeled and take into account the environmental constraints that potentially influence the future motion. To this end, we validate the robustness of the proposed approach using the ETH, UCY, and SDD datasets and highlight its practical functionality compared to the current state-of-the-art methods.