Safe Urban Traffic Control via Uncertainty-Aware Conformal Prediction and World-Model Reinforcement Learning

• URL: http://arxiv.org/abs/2602.04821v1
• Date: Wed, 04 Feb 2026 18:10:59 GMT
• Title: Safe Urban Traffic Control via Uncertainty-Aware Conformal Prediction and World-Model Reinforcement Learning
• Authors: Joydeep Chandra, Satyam Kumar Navneet, Aleksandr Algazinov, Yong Zhang,
• Abstract summary: STREAM-RL is an Uncertainty-Guided Adaptive Conformal Forecaster, a Conformal Residual Flow Network, and an Uncertainty-Guided Safe World-Model RL agent.<n>Experiments on multiple real-world traffic trajectory data demonstrate that STREAM-RL achieves 91.4% coverage efficiency, controls FDR at 4.1% under verified dependence, and improves safety rate to 95.2% compared to 69% for standard PPO.
• Score: 43.06827300023392
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Urban traffic management demands systems that simultaneously predict future conditions, detect anomalies, and take safe corrective actions -- all while providing reliability guarantees. We present STREAM-RL, a unified framework that introduces three novel algorithmic contributions: (1) PU-GAT+, an Uncertainty-Guided Adaptive Conformal Forecaster that uses prediction uncertainty to dynamically reweight graph attention via confidence-monotonic attention, achieving distribution-free coverage guarantees; (2) CRFN-BY, a Conformal Residual Flow Network that models uncertainty-normalized residuals via normalizing flows with Benjamini-Yekutieli FDR control under arbitrary dependence; and (3) LyCon-WRL+, an Uncertainty-Guided Safe World-Model RL agent with Lyapunov stability certificates, certified Lipschitz bounds, and uncertainty-propagated imagination rollouts. To our knowledge, this is the first framework to propagate calibrated uncertainty from forecasting through anomaly detection to safe policy learning with end-to-end theoretical guarantees. Experiments on multiple real-world traffic trajectory data demonstrate that STREAM-RL achieves 91.4\% coverage efficiency, controls FDR at 4.1\% under verified dependence, and improves safety rate to 95.2\% compared to 69\% for standard PPO while achieving higher reward, with 23ms end-to-end inference latency.

Related papers

• Uncertainty-aware Generative Recommendation [52.0751022792023]
  Uncertainty-aware Generative Recommendation (UGR) is a unified framework that leverages uncertainty as a critical signal for adaptive optimization.<n>UGR not only yields superior recommendation performance but also fundamentally stabilizes training, preventing the performance degradation often observed in standard methods.
  arXiv  Detail & Related papers  (2026-02-12T08:48:51Z)
• LEC: Linear Expectation Constraints for False-Discovery Control in Selective Prediction and Routing Systems [95.35293543918762]
  Large language models (LLMs) often generate unreliable answers, while uncertainty methods fail to fully distinguish correct from incorrect predictions.<n>We address this issue through the lens of false discovery rate (FDR) control, ensuring that among all accepted predictions, the proportion of errors does not exceed a target risk level.<n>We propose LEC, which reinterprets selective prediction as a constrained decision problem by enforcing a Linear Expectation Constraint.
  arXiv  Detail & Related papers  (2025-12-01T11:27:09Z)
• Learning to Navigate Under Imperfect Perception: Conformalised Segmentation for Safe Reinforcement Learning [6.255435016547602]
  COPPOL is a conformal-driven perception-to-policy learning approach.<n>It integrates distribution-free, finite-sample safety guarantees into semantic segmentation.<n>It achieves near-complete detection of unsafe regions while reducing hazardous violations during navigation.
  arXiv  Detail & Related papers  (2025-10-21T10:07:04Z)
• Confidence-Based Response Abstinence: Improving LLM Trustworthiness via Activation-Based Uncertainty Estimation [7.3923284353934875]
  We propose a method for confidence estimation in retrieval-augmented generation (RAG) systems that aligns closely with the correctness of large language model (LLM) outputs.<n>Our approach extends prior uncertainty quantification methods by leveraging raw feed-forward network (FFN) activations as auto-regressive signals.<n>Our results demonstrate that activation-based confidence modeling offers a scalable, architecture-aware path toward trustworthy RAG deployment.
  arXiv  Detail & Related papers  (2025-10-15T16:55:56Z)
• Adaptive Federated Learning Defences via Trust-Aware Deep Q-Networks [1.5374297736981706]
  Federated learning is vulnerable to poisoning and backdoor attacks under partial observability.<n>We introduce a trust-aware Deep Q-Network that integrates multi-signal evidence into client trust updates.
  arXiv  Detail & Related papers  (2025-09-25T13:30:09Z)
• Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality [53.525547349715595]
  We propose a novel primal-only algorithm called Rectified Robust Policy Optimization (RRPO)<n>RRPO operates directly on the primal problem without relying on dual formulations.<n>We show convergence to an approximately optimal feasible policy with complexity matching the best-known lower bound.
  arXiv  Detail & Related papers  (2025-08-24T16:59:38Z)
• COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees [51.5976496056012]
  COIN is an uncertainty-guarding selection framework that calibrates statistically valid thresholds to filter a single generated answer per question.<n>COIN estimates the empirical error rate on a calibration set and applies confidence interval methods to establish a high-probability upper bound on the true error rate.<n>We demonstrate COIN's robustness in risk control, strong test-time power in retaining admissible answers, and predictive efficiency under limited calibration data.
  arXiv  Detail & Related papers  (2025-06-25T07:04:49Z)
• Data-Driven Calibration of Prediction Sets in Large Vision-Language Models Based on Inductive Conformal Prediction [0.0]
  We propose a model-agnostic uncertainty quantification method that integrates dynamic threshold calibration and cross-modal consistency verification.<n>We show that the framework achieves stable performance across varying calibration-to-test split ratios, underscoring its robustness for real-world deployment in healthcare, autonomous systems, and other safety-sensitive domains.<n>This work bridges the gap between theoretical reliability and practical applicability in multi-modal AI systems, offering a scalable solution for hallucination detection and uncertainty-aware decision-making.
  arXiv  Detail & Related papers  (2025-04-24T15:39:46Z)
• Beyond Confidence: Adaptive Abstention in Dual-Threshold Conformal Prediction for Autonomous System Perception [0.4124847249415279]
  Safety-critical perception systems require reliable uncertainty quantification and principled abstention mechanisms to maintain safety.<n>We present a novel dual-threshold conformalization framework that provides statistically-guaranteed uncertainty estimates while enabling selective prediction in high-risk scenarios.
  arXiv  Detail & Related papers  (2025-02-11T04:45:31Z)
• ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees [68.33498595506941]
  We introduce a novel uncertainty measure based on self-consistency theory. We then develop a conformal uncertainty criterion by integrating the uncertainty condition aligned with correctness into the CP algorithm. Empirical evaluations indicate that our uncertainty measure outperforms prior state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-06-29T17:33:07Z)

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.