arXiv:2608. 07480v1 Announce Type: new Abstract: Active inference has emerged as a principled framework for modeling adaptive behavior by balancing goal-directed action with uncertainty reduction.
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arXiv:2610.33713v1 Announce Type: new Abstract: Driver emotion provides an important indication of how drivers perceive and evaluate ongoing traffic situations, making reliable emotion recognition an...
arXiv:2610.33726v1 Announce Type: new Abstract: Modeling human driving behavior is fundamental to understanding driver decision-making and supporting autonomous vehicles (AVs) in mixed traffic. Rewar...
arXiv:2609.06654v1 Announce Type: new Abstract: Computational modeling of emotion has long faced a tension between descriptive, "snapshot-based" appraisal models and granular, signal-driven architect...
The paper presents a lane‑change prediction model using knowledge‑graph embeddings and Bayesian inference to anticipate surrounding vehicles’ intentions 3–4 seconds before they occur. Three driving strategies—reactive, prediction‑based longitudinal, and prediction‑based lateral—are compared in CARLA simulations of risky merging scenarios. Prediction‑based approaches avoid collisions, reduce peak deceleration by up to 91 %, and are evaluated for ride comfort using ISO 2631‑1 vibration estimates.
arXiv:2506. 12283v2 Announce Type: replace Abstract: Modeling vehicle interactions at unsignalized intersections is a challenging task due to the complexity of the underlying game-theoretic processes.
arXiv:2605. 05092v2 Announce Type: replace-cross Abstract: Safe L2/L3 driving automation requires anticipating human-in-the-loop reactions during shared-control transitions.