Aug 2026· International Conference on Electromechanical Control Technology and Transportation· Vol 14324, pp. 1432421 - 1432421-6· 0 citations· 17 references
Engineering
TL;DR
The HGCA-Net framework employs a dynamic spatio-temporal graph attention network for interaction modeling and an intention recognition branch for maneuver preferences to address extended-horizon error accumulation during nonlinear highway lane changes.
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.
This study isolates the effect of local lane graph conditioning by proposing a waterflow method that extracts an ego-centric lane topology via breadth-first traversal of the HD map, suggesting feasibility for resource-constrained deployment, pending validation on production automotive hardware.
Xing-Nan Zhou, C. Alecsandru· Sustainability· 0 citations
The prediction of lane-change intent in mixed traffic environments, characterized by the coexistence of human-driven vehicles and autonomous vehicles, presents a formidable challenge due to the stochastic nature of human driving behaviors and complex vehicular interactions. Conventional trajectory prediction and intent...
Anele Mthembu, Thabo Pretorius· Journal of innovative resear...· 0 citations
A vision-based end-to-end autonomous parking framework trained through imitation learning that introduces a historical context fusion encoder to capture temporal dependencies from past vehicle motions, a dual-stream attention decoder to enhance interaction between scene features and trajectory representations, and kine...
Da Zheng, Bing-Li Zhang, Xinyu Wang et al.· Engineering Research Express· 0 citations
A non-local multi-head spatiotemporal attention based long short-term memory model (NL-MHA-LSTM) is introduced which employs an attention mechanism to assign context weights to relevant neighbor vehicles and extends beyond pairwise effects to model long-range dependencies.
S. Rashid, M. A. Khan, Usman Akram et al.· PLoS ONE· 0 citations
A joint intention-and-trajectory prediction framework that combines mutual-information-guided feature selection with a hybrid convolutional neural network (CNN)–Transformer architecture is proposed to improve the accuracy, efficiency, and interpretability of intention-aware vehicle trajectory prediction.
A framework that fuses the output of Trajectron++, a neural network-based trajectory predictor, with extended Kalman filter (EKF)-based multiple trajectory candidates at a late stage indicates that EKF-based trajectory candidates can effectively complement neural trajectory prediction through learned fusion.
Seong-Jun Kim, Seung-Hyun Kong· Journal of Institute of Cont...· 0 citations
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