HGCA-Net: long-horizon lane-change trajectory prediction with intention-guided fusion

Abstract

Accurate trajectory prediction is critical for autonomous driving. To address extended-horizon error accumulation during nonlinear highway lane changes, the HGCA-Net framework is proposed. It employs a dynamic spatio-temporal graph attention network for interaction modeling and an intention recognition branch for maneuver preferences. A cross-attention mechanism integrates these priors to alleviate intention ambiguity, while a time-weighted loss penalizes far-end drift. Experiments on the NGSIM dataset demonstrate that HGCA-Net reduces ADE and FDE to 1.9632 m and 4.7359 m, outperforming baselines at the 5-second horizon.