arXiv:2607. 00027v1 Announce Type: cross Abstract: Urban deceleration is one of the most empirically studied yet least taxonomically organized behaviors in car-following research.
Traffic microsimulators rely on hand-crafted behavior models that reproduce aggregate flow but miss the heterogeneous interactions between vehicles at signalized intersections. Learned trajectory predictors capture richer interactions but are short-horizon and tend to be unstable when run in closed loop.
arXiv:2609.16058v1 Announce Type: cross Abstract: Red-light violations and harsh braking at signalized intersections are major contributors to traffic accidents. This paper analyzes and predicts huma...
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:2607. 20549v1 Announce Type: new Abstract: Trajectory datasets used in ADAS evaluation are heavily biased toward routine driving; genuine vehicle-to-vehicle conflict events are rare, and the rarer the event, the higher the cost when an ADAS system fails to handle it.
arXiv:2606. 18824v1 Announce Type: cross Abstract: Pedestrian trajectory prediction from an ego-centric camera is challenging since it depends on complex interactions with vehicles and scene context, as well as the intention of the pedestrian.
arXiv:2607. 23822v1 Announce Type: new Abstract: Driving style captures stable, driver-specific patterns in how a vehicle is driven.
arXiv:2608. 01049v1 Announce Type: cross Abstract: World models have attracted significant attention for their ability to capture and predict the structure and dynamics of the physical world.
The paper introduces Context-driven Personalized ACC (CoP-ACC), a data‑driven framework that learns from drivers’ throttle overrides to tailor Adaptive Cruise Control behavior. It uses unsupervised clustering to identify representative acceleration profiles, a context classifier to select the appropriate profile based on pre‑maneuver conditions, and a residual regressor to smooth the final profile. Evaluations on real‑world public‑road data show that CoP-ACC reconstructs driver‑expected acceleration patterns more accurately than a standard forced‑ACC baseline, suggesting it can reduce manual interventions and improve ride comfort.
arXiv:2607. 13319v1 Announce Type: cross Abstract: High-speed off-road autonomy requires precise closed-loop control for a target vehicle while remaining robust across changing terrains.
arXiv:2608.28656v1 Announce Type: cross Abstract: Behavior-cloned Vision-Language-Action (VLA) driving policies struggle with rare rule-governed maneuvers at signalized intersections. Braking and lau...
The paper proposes a proactive approach to road safety in Greater Sydney by using connected vehicle telemetry to predict risky driving events before crashes occur. It quantifies risky driving with g‑force thresholds and builds spatio‑temporal heatmaps to locate high‑risk zones. Eight predictive models were compared, with ARIMA achieving the lowest error and showing that simple time‑series methods can rival deep learning when data are limited, highlighting the value of IoT data for targeted safety interventions.
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 %.