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.
By Rwik Rana, Jesse Quattrociocchi, Christian Ellis, Nathan Tsoi, Garrett Warnell, Joydeep Biswas
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.
By Adriana-Simona Mih\u{a}i\c{t}\u{a}, Clarence Cheung, Artur Grigorev, Tuo Mao, David Lillo-Trynes
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...
By Mohammad Khoshkdahan, Pavel Laskov, Alexey Vinel
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.
By Sumit S. Shevtekar, Chandresh K. Maurya, Gourab Sil
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.
By Eni Solomon Laughter