Predictive Conformal Slip Monitoring: An Empirical Evaluation of Rolling Split Conformal Prediction for Pre-Incident Traction Loss Detection

• URL: http://arxiv.org/abs/2607.02124v1
• Date: Thu, 02 Jul 2026 13:02:34 GMT
• Title: Predictive Conformal Slip Monitoring: An Empirical Evaluation of Rolling Split Conformal Prediction for Pre-Incident Traction Loss Detection
• Abstract summary: This paper investigates whether Rolling Split Conformal Prediction can serve as a statistically grounded pre-incident warning signal.<n>Across 19 drivers and 55,563 test-phase telemetry samples, the rolling-volatility detector achieves a mean precision of essentially 0.0.<n>We report this as a methodologically rigorous negative finding, diagnose its likely causes, and outline what a genuinely predictive version of this approach would require.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Unlike an earlier internal draft of this work, the evaluation reported here corrects a confound in the slip proxy (vehicle speed is included as an explicit model feature, not left implicit in the target's denominator), uses every racing lap for each driver rather than only the fastest lap, and is scored against real, timestamped incident labels extracted from FIA Race Control Messages and track-limits lap deletions rather than narrated post-hoc. The result is negative: across 19 drivers and 55,563 test-phase telemetry samples, the rolling-volatility detector achieves a mean precision of essentially 0.0 and mean recall of 0.0 against 14 ground-truth incidents, while flagging on average 15.3% of all samples as anomalous , too high a false-alarm rate for any early-warning use. A static 95th-percentile threshold baseline performs no better in any way that would justify the added complexity of the conformal-volatility formulation. Residual autocorrelation diagnostics show the split-conformal exchangeability assumption is violated for every driver (Ljung-Box p < 0.001, n = 19/19), which is one plausible driver of the high false-alarm rate. We report this as a methodologically rigorous negative finding, diagnose its likely causes, and outline what a genuinely predictive version of this approach would require.

Related papers

• Excitation-Supervised Closed-Loop Self-Calibration and Target Seeking for an Unknown-Pose Range-Bearing Relay [0.0]
  This paper supplies the closed-loop layer that governs identifiability.<n>An excitation-supervised algorithm is used to supervise an excitation-reset controller.<n>A decay-rate sweep shows that supervision matters when a fixed schedule's decay outruns the unknown time-to-adequate-excitation.
  arXiv  Detail & Related papers  (2026-08-12T19:00:07Z)
• Evaluation Awareness Is Not One Capability: Evidence from Open Language Models [1.758143872501506]
  Safety benchmarks assume that test-condition behavior predicts deployment behavior, an assumption that fails if models detect evaluation cues and adapt.<n>This opens a gap between benchmark performance and deployment behavior.<n>We characterize this evaluation awareness through eight experiments across 37 open-weight models and seven families.
  arXiv  Detail & Related papers  (2026-06-22T16:48:43Z)
• Taming Perception Jitter: Uncertainty-Aware LiDAR Object Detection for Reliable Motion Classification [8.046180274721541]
  Unstable bounding box predictions can lead to spurious velocity estimates in tracking and falsely predicted trajectories.<n>We present a deployment-friendly mitigation strategy that augments a 3D object detector with aleatoric uncertainty estimates and applies a two-sample z-test over short observation windows to separate true motion from jitter.
  arXiv  Detail & Related papers  (2026-06-08T11:24:57Z)
• Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference [48.78219918881965]
  We propose a principled framework for deriving task-specific strictly proper scoring rules by matching the local curvature of the downstream error metric.<n>We apply this to the Average Treatment Effect (ATE) estimation, deriving a closed-form loss and its corresponding canonical probability mapping.
  arXiv  Detail & Related papers  (2026-06-02T08:41:25Z)
• Testing the Test: Score-Direction Instability in Class-Split Anomaly Detection [56.362776482614976]
  Within-dataset class-split evaluation is widely used as a proxy for fully unconditional out-of-distribution anomaly detection.<n>We show that this protocol can become ill-posed when the held-out anomaly class overlaps the normal mixture in representation space.<n>We introduce a simple training-free diagnostic, neighborhood class leakage, and show that it predicts score-direction instability across Fashion-MNIST, CIFAR-10, and Imagenette.
  arXiv  Detail & Related papers  (2026-05-23T06:27:42Z)
• FASE : A Fairness-Aware Spatiotemporal Event Graph Framework for Predictive Policing [0.0]
  Predictive policing systems that allocate patrol resources based solely on predicted crime risk can unintentionally amplify racial disparities through feedback driven data.<n>We present FASE, Stemporal Event Graph framework, which integrates crime prediction with fairness constrained patrol allocation and a closed loop deployment feedback simulator.<n>We show that allocation level fairness alone do not eliminate feedback induced bias in retraining data, highlighting the need for fairness interventions across the full pipeline.
  arXiv  Detail & Related papers  (2026-04-19T21:22:45Z)
• Beyond Completion: Probing Cumulative State Tracking to Predict LLM Agent Performance [9.771590610969918]
  We introduce Working Memory Fidelity-Active Manipulation (WMF-AM)<n>We evaluate it on 20 open-weight models (0.5B-35B, 13 families) against a released deterministic 10-task agent battery.
  arXiv  Detail & Related papers  (2026-03-28T17:25:11Z)
• Towards Anytime-Valid Statistical Watermarking [63.02116925616554]
  We develop the first e-value-based watermarking framework, Anchored E-Watermarking, that unifies optimal sampling with anytime-valid inference.<n>Our framework can significantly enhance sample efficiency, reducing the average token budget required for detection by 13-15% relative to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-02-19T18:32:26Z)
• Unsupervised Anomaly Detection in Multi-Agent Trajectory Prediction via Transformer-Based Models [45.08545174556591]
  We propose an unsupervised anomaly detection framework based on a multi-agent Transformer.<n>A dual evaluation scheme has been proposed to assess both detection stability and physical alignment.<n>Our framework identifies 388 unique anomalies missed by Time-to-Collision and statistical baselines.
  arXiv  Detail & Related papers  (2026-01-28T08:33:10Z)
• Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting [65.21599711087538]
  Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and test data by adapting a given model w.r.t. any test sample.<n>Prior methods perform backpropagation for each test sample, resulting in unbearable optimization costs to many applications.<n>We propose an Efficient Anti-Forgetting Test-Time Adaptation (EATA) method which develops an active sample selection criterion to identify reliable and non-redundant samples.
  arXiv  Detail & Related papers  (2024-03-18T05:49:45Z)
• Near-Optimal Non-Parametric Sequential Tests and Confidence Sequences with Possibly Dependent Observations [44.71254888821376]
  We provide the first type-I-error and expected-rejection-time guarantees under general non-data generating processes. We show how to apply our results to inference on parameters defined by estimating equations, such as average treatment effects.
  arXiv  Detail & Related papers  (2022-12-29T18:37:08Z)
• AutoCP: Automated Pipelines for Accurate Prediction Intervals [84.16181066107984]
  This paper proposes an AutoML framework called Automatic Machine Learning for Conformal Prediction (AutoCP) Unlike the familiar AutoML frameworks that attempt to select the best prediction model, AutoCP constructs prediction intervals that achieve the user-specified target coverage rate. We tested AutoCP on a variety of datasets and found that it significantly outperforms benchmark algorithms.
  arXiv  Detail & Related papers  (2020-06-24T23:13:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.