arXiv:2606. 02762v1 Announce Type: new Abstract: Knowledge of real-time road slipperiness, or even better, a refined estimate of peak grip potential, is a critical input for vehicle warning and intervention control systems.
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.
High-quality driving data are essential for autonomous-driving systems and generative world models. However, rare and safety-critical scenarios involving adverse weather, braking under low tire--road friction, and uneven road geometry are costly and risky to collect at scale.
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.
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...
The paper introduces MotoTimePressure (MTPS), a deep learning model that predicts the time pressure experienced by powered two‑wheeler riders using 63 vehicle and environmental features. MTPS achieves 91.53% accuracy and 98.93% ROC AUC on a dataset of 129,209 feature windows from 51 experienced male riders across no, low, and high time‑pressure conditions, and its predictions improve collision‑risk models such as Informer and TimesNet. The authors argue that detecting high time pressure can inform proactive ITS interventions—adaptive alerts, haptic feedback, V2I signaling, and speed guidance—to enhance rider safety under the Safe System Approach.
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.
arXiv:2607. 02124v1 Announce Type: new Abstract: Conventional traction control architectures intervene only after the adhesion limit of a tire has already been breached.
arXiv:2603. 13343v3 Announce Type: replace-cross Abstract: Predictive maintenance for connected vehicles offers the potential to reduce unexpected breakdowns and improve fleet reliability, but most existing systems rely exclusively on internal diagnostic signals and are validated on simulated or industrial benchmark data.
Conventional traction control architectures intervene only after the adhesion limit of a tire has already been breached. This paper investigates whether Rolling Split Conformal Prediction , monitoring the volatility of non-conformity residuals from a per-driver Random Forest model of expected slip behavior , can serve as a statistically grounded pre-incident warning signal, ahead of gross traction loss.
arXiv:2607. 09402v1 Announce Type: new Abstract: Deep learning models dependency on large-scale inertial datasets presents a significant bottleneck in inertial sensor-based classification tasks, such as human activity recognition and smartphone location recognition.
MLPerf Automotive is the first standardized public performance benchmark for evaluating machine learning systems used in automotive AI acceleration. Developed by MLCommons, it addresses the unique constraints of automotive workloads—such as sensor suites, safety, and real‑time processing—that existing benchmarks cannot handle. The benchmark covers perception tasks (2D/3D object detection, semantic segmentation), end‑to‑end driving, and infotainment, and includes curated models, methodology, and reference implementations available on GitHub.
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.