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. 02722v1 Announce Type: new Abstract: Predicting thermal volatility in high-performance EV powertrains is difficult as internal temperatures are rarely observable outside the lab, and models calibrated on lab drive cycles fail when deployed against real-world loads.
By Varshith Roy Kotla
arXiv:2606. 11949v1 Announce Type: new Abstract: We present an online monitoring system for distributional shift in deployed safety classifiers, using calibrated sequential statistics to detect when a classifier has moved out of distribution.
By Jun Wen Leong
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
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.
By Ishak Abassi, Nassim Ali Bouazzouni, Farah Ibelaiden, Nadir Farhi
arXiv:2606. 14238v1 Announce Type: cross Abstract: Safety certification of Vision-Language-Action (VLA) driving planners under ISO 21448 (SOTIF) rests on an Operational Design Domain (ODD) specification that answers two complementary questions: when does the planner start to fail, and how severely does it fail once it does?
By Abhinaw Priyadershi, Jelena Frtunikj
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.
By Vishal Hariharan, Salar Basiri, Kanwar Bharat Singh
arXiv:2608. 04896v1 Announce Type: new Abstract: Defensive driving scores are useful only when they preserve distinctions between policies that observe surrounding actors and those that do not.
By Ziang Wei, Minjun Yu, Zheyuan Lai, Mingjie Pang, Wei Li
arXiv:2606. 21875v2 Announce Type: replace-cross Abstract: Modern data analysis usually gives a prediction without showing whether the evidence behind it is clear, conflicting, or stable.
By Jeffery Opoku, David Banahene
arXiv:2608. 05199v1 Announce Type: cross Abstract: Autonomous security agents operate as staged pipelines, such as classifying network traffic and then attributing attacks to a specific technique.
By Zhenpeng Li
arXiv:2606. 15153v1 Announce Type: new Abstract: Selective prediction with distribution-free risk control promises that, with confidence 1-delta over the calibration draw, the error rate of accepted inputs stays below a user budget alpha.
By Jingwen Zhou, Mingzhe Wang
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.
By Eni Solomon Laughter