Map-Free Trajectory Prediction via Deformable Attention in Bird's-Eye View Space

In autonomous driving, trajectory prediction is crucial for safe navigation. While many recent methods rely on pre-built high definition (HD) maps, these are limited to specific regions and cannot reflect real-time changes. We propose a novel framework that constructs bird's-eye view representations from real-time sensor data and selectively extracts critical features using deformable attention, eliminating the need for HD maps. We also introduces a sparse goal candidate proposal module for fully end-to-end prediction without post-processing. Experiments demonstrate that our model achieves competitive performance compared to HD map-based methods.