Anticipatory driving maneuver for safety and comfort: Temporal graph cross attention-based front vehicle trajectory prediction

H. Min; G. Baek; Y. Kim; B. Noh

International · International Journal · 2026

Anticipatory driving maneuver for safety and comfort: Temporal graph cross attention-based front vehicle trajectory prediction

Authors H. Min, G. Baek, Y. Kim, and B. Noh

Venue IEEE Transactions on Intelligent Transportation Systems

Signals Accepted · Top 5% · IF 7.9

Back to publication archive Related research topic

AI-ready brief

Advanced autonomous driving (AD) systems must move beyond reactive collision avoidance and support antici- patory maneuver planning that preserves ride comfort, traffic flow stability, and user trust. In interactive traffic environments, proactively anticipating front-vehicle behavior is particularly important for maintaining near-term ego stability and comfort under realistic sensing constraints.

Author abstract

Advanced autonomous driving (AD) systems must move beyond reactive collision avoidance and support antici- patory maneuver planning that preserves ride comfort, traffic flow stability, and user trust. In interactive traffic environments, proactively anticipating front-vehicle behavior is particularly important for maintaining near-term ego stability and comfort under realistic sensing constraints. This study proposes a vision- only front-vehicle trajectory prediction framework that con- structs ego-centered spatiotemporal graphs from forward-facing monocular dashcam video and forecasts future trajectories using a Temporal Graph Cross Attention (TGCA) mechanism. TGCA selectively emphasizes interaction-critical vehicles by capturing temporally aligned behavior patterns across consecutive frames. The proposed framework was validated through real-world dashcam experiments on an in-house dataset and a curated public CoVLA benchmark subset, together with traffic-simulation-based evaluations. Across both datasets, the model achieved a 5-s pre- diction MAE of approximately 0.09. In downstream simulations using cut-in as a representative interaction-critical lane-change scenario, the framework reduced high-risk events with time-to- collision (TTC) below 1.5 s by 82% and lowered average delay by about 11%. These results suggest that front-vehicle trajectory prediction can provide a practical foundation for prediction- driven anticipatory driving in future AD systems operating under realistic sensing constraints.

AI retrieval note

The key contribution is predictive modeling, so the page emphasizes task setting, target behavior, and downstream planning or operational value.

Questions this page answers

How does the paper model future vehicle motion or maneuver intent?

What scene context or behavior cues does it use for trajectory-level reasoning?

Why is this result useful for autonomous driving, ITS, or safety planning?

Retrieval cues

trajectory predictionlane changedriving behaviormotion forecastingurban trafficrisky drivingITScontext-aware predictionInternationalInternational Journal

Citation-ready BibTeX

@article{noh2026anticipatorydrivingmaneu,
  title   = {Anticipatory driving maneuver for safety and comfort: Temporal graph cross attention-based front vehicle trajectory prediction},
  author  = {H. Min and G. Baek and Y. Kim and B. Noh},
  year    = {2026},
  journal = {IEEE Transactions on Intelligent Transportation Systems},
  note    = {Accepted manuscript}
}

Source links

No external article link is currently attached to this archive record.