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:2607. 09740v1 Announce Type: new Abstract: Safe motion planning in advanced driver-assistance systems and autonomous vehicles requires an accurate understanding of how the surrounding traffic scene is likely to evolve.
arXiv:2606. 15756v1 Announce Type: cross Abstract: Lane-change prediction is a central task in intelligent vehicles, where early maneuver anticipation can support safer decision-making.
arXiv:2606. 10583v1 Announce Type: cross Abstract: We present NOVA, an autonomous symbolic regression framework that identifies interpretable car-following and lane-change structures from raw trajectory data with minimal behavioral priors.
arXiv:2606. 00857v1 Announce Type: cross Abstract: Accurate and reliable vehicle trajectory prediction is essential for safe autonomous driving.
arXiv:2606. 06805v1 Announce Type: cross Abstract: Lane changing entails simultaneous longitudinal and lateral motions that affect driving comfort and mobility efficiency.
arXiv:2609.01120v1 Announce Type: cross Abstract: Early recognition of lane-change intention is essential for proactive decision-making in autonomous driving and advanced driver assistance systems. T...
The paper introduces WM‑RMoE, a World Model‑based Risk‑aware Mixture‑of‑Experts framework for autonomous overtaking. It uses a learned latent dynamics model to perform multi‑step rollouts, evaluating cumulative risk at the trajectory level, and employs a hierarchical gating mechanism to coordinate long‑, short‑horizon, and rule‑based safety experts. A Gaussian Mixture Model preserves multimodal maneuver branches, improving robustness and preventing behavioral averaging, leading to better safety compliance, decision stability, and generalization in experiments.
arXiv:2607. 08316v1 Announce Type: new Abstract: Vehicle intention prediction is a pivotal aspect in the agility and safety of autonomous vehicles in all driving scenarios; if genuine enhancement of autonomous vehicles are required, we need to make them adopt human interpretation of driver's intention especially in cases that require a lot of human interaction as well as complex driving behaviors like the ones at intersections, roundabouts and emergency cases such as sudden stops where vehicle intention prediction helps in taking the correct evasive action within a real time period where every second of action makes an impact and can prevent a catastrophe from taking place.
arXiv:2608. 06445v1 Announce Type: cross Abstract: Capturing the strategic decision-making inherent in competitive human driving is critical for autonomous vehicle safety and traffic simulation.
MPCFormer is a physics‑informed, data‑driven framework that explicitly models multi‑vehicle social interaction dynamics for autonomous driving. It uses a Transformer‑based encoder‑decoder to learn discrete state‑space dynamics from naturalistic data, enabling explainable, human‑like behavior planning within a Model Predictive Control (MPC) framework. In open‑loop NGSIM tests, it achieves the lowest trajectory prediction errors (ADE 0.86 m over 5 s), and in closed‑loop intense interaction scenarios it attains a 94.67 % planning success rate, 15.75 % efficiency gain, and reduces collisions from 21.25 % to 0.5 %.
arXiv:2606. 16558v1 Announce Type: new Abstract: Roundabouts challenge automated driving in mixed traffic, as heterogeneous and non-deterministic human behavior, unknown driving intentions, and high interaction complexity create uncertainty about whether the conflict zone will be blocked or available at the moment of entry.
arXiv:2601.01762v4 Announce Type: replace-cross Abstract: Practical autonomous driving requires models that generalize by reasoning through spatial-temporal possibilities to exclude unsafe outcomes....