Collision Risk Assessment of Lane-Changing Vehicles Based on Spatio-Temporal Feature Fusion Trajectory Prediction

Accurate forecasting of potential collision risk in dense traffic is addressed by a framework grounded in multi-vehicle trajectory prediction. A spatio-temporal fusion architecture, STGAT-EDGRU, is proposed. A Transformer encoder learns temporal motion patterns from each vehicle’s history; a boundary-aware graph (GAT) attention network models inter-vehicle interactions; and a Gated Multimodal Unit (GMU) adaptively fuses the temporal and spatial streams. Future positions are parameterized as bivariate Gaussians and decoded by a two-layer GRU. Using probabilistic trajectory forecasts for the main vehicle and its surrounding vehicles, collision probability and collision intensity are computed at each prediction instant and integrated via a weighted scheme into a Collision Risk Index (CRI) that characterizes risk over the entire horizon. On HighD, for 3–5 s horizons, average RMSE reductions of 0.02 m, 0.12 m, and 0.26 m over a GAT-Transformer baseline are achieved. In high-risk lane-change scenarios, CRI issues warnings 0.4–0.6 s earlier and maintains a stable response across the high-risk interval. These findings substantiate improved long-horizon accuracy together with earlier and more reliable risk perception, and indicate practical utility for lane-change assistance, where CRI can trigger early deceleration or abort decisions, and for risk-aware motion planning in intelligent driving.