Risk-Aware World Model Predictive Control for Generalizable End-to-End Autonomous Driving

• URL: http://arxiv.org/abs/2602.23259v1
• Date: Thu, 26 Feb 2026 17:32:30 GMT
• Title: Risk-Aware World Model Predictive Control for Generalizable End-to-End Autonomous Driving
• Authors: Jiangxin Sun, Feng Xue, Teng Long, Chang Liu, Jian-Fang Hu, Wei-Shi Zheng, Nicu Sebe,
• Abstract summary: "Only driving like the expert" suffers from limited generalization.<n>Can an E2E-AD system make reliable decisions without any expert action supervision?<n>We propose a unified framework named Risk-aware World Model Predictive Control.
• Score: 82.69496624372944
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: With advances in imitation learning (IL) and large-scale driving datasets, end-to-end autonomous driving (E2E-AD) has made great progress recently. Currently, IL-based methods have become a mainstream paradigm: models rely on standard driving behaviors given by experts, and learn to minimize the discrepancy between their actions and expert actions. However, this objective of "only driving like the expert" suffers from limited generalization: when encountering rare or unseen long-tail scenarios outside the distribution of expert demonstrations, models tend to produce unsafe decisions in the absence of prior experience. This raises a fundamental question: Can an E2E-AD system make reliable decisions without any expert action supervision? Motivated by this, we propose a unified framework named Risk-aware World Model Predictive Control (RaWMPC) to address this generalization dilemma through robust control, without reliance on expert demonstrations. Practically, RaWMPC leverages a world model to predict the consequences of multiple candidate actions and selects low-risk actions through explicit risk evaluation. To endow the world model with the ability to predict the outcomes of risky driving behaviors, we design a risk-aware interaction strategy that systematically exposes the world model to hazardous behaviors, making catastrophic outcomes predictable and thus avoidable. Furthermore, to generate low-risk candidate actions at test time, we introduce a self-evaluation distillation method to distill riskavoidance capabilities from the well-trained world model into a generative action proposal network without any expert demonstration. Extensive experiments show that RaWMPC outperforms state-of-the-art methods in both in-distribution and out-of-distribution scenarios, while providing superior decision interpretability.

Related papers

• Steering Externalities: Benign Activation Steering Unintentionally Increases Jailbreak Risk for Large Language Models [62.16655896700062]
  Activation steering is a technique to enhance the utility of Large Language Models (LLMs)<n>We show that it unintentionally introduces critical and under-explored safety risks.<n>Experiments reveal that these interventions act as a force multiplier, creating new vulnerabilities to jailbreaks and increasing attack success rates to over 80% on standard benchmarks.
  arXiv  Detail & Related papers  (2026-02-03T12:32:35Z)
• AD-R1: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving with Impartial World Models [75.214287449744]
  We introduce a framework for post-training policy refinement built around an Impartial World Model.<n>Our primary contribution is to teach this model to be honest about danger.<n>We demonstrate through extensive experiments, that our model significantly outperforms baselines in predicting failures.
  arXiv  Detail & Related papers  (2025-11-25T13:57:24Z)
• Enhancing End-to-End Autonomous Driving with Risk Semantic Distillaion from VLM [14.016225216093643]
  We introduce Risk Semantic Distillation (RSD), a novel framework that leverages Vision-Language Models (VLMs) to enhance the training of End-to-End (E2E) autonomous driving backbones.<n>Specifically, we introduce RiskHead, a plug-in module that distills causal risk estimates from Vision-Language Models into Bird's-Eye-View (BEV) features.<n>Our experiments on the Bench2Drive benchmark demonstrate the effectiveness of RSD in managing complex and unpredictable driving conditions.
  arXiv  Detail & Related papers  (2025-11-18T13:46:18Z)
• INSIGHT: Enhancing Autonomous Driving Safety through Vision-Language Models on Context-Aware Hazard Detection and Edge Case Evaluation [7.362380225654904]
  INSIGHT is a hierarchical vision-language model (VLM) framework designed to enhance hazard detection and edge-case evaluation.<n>By using multimodal data fusion, our approach integrates semantic and visual representations, enabling precise interpretation of driving scenarios.<n> Experimental results on the BDD100K dataset demonstrate a substantial improvement in hazard prediction straightforwardness and accuracy over existing models.
  arXiv  Detail & Related papers  (2025-02-01T01:43:53Z)
• Learning Multiple Probabilistic Decisions from Latent World Model in Autonomous Driving [40.4491758280365]
  Autoregressive world model exhibits robust generalization capabilities but encounters difficulties in deriving actions due to insufficient uncertainty modeling and self-delusion. We propose LatentDriver, a framework models the environment's next states and the ego vehicle's possible actions as a mixture distribution. LatentDriver surpasses state-of-the-art reinforcement learning and imitation learning methods, achieving expert-level performance.
  arXiv  Detail & Related papers  (2024-09-24T04:26:24Z)
• When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning [62.00672284480755]
  This paper aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent. Accurate models of expertise in executing a task has applications in safety-sensitive applications such as clinical decision making and autonomous driving.
  arXiv  Detail & Related papers  (2023-02-15T04:14:20Z)
• Selecting Models based on the Risk of Damage Caused by Adversarial Attacks [2.969705152497174]
  Regulation, legal liabilities, and societal concerns challenge the adoption of AI in safety and security-critical applications. One of the key concerns is that adversaries can cause harm by manipulating model predictions without being detected. We propose a method to model and statistically estimate the probability of damage arising from adversarial attacks.
  arXiv  Detail & Related papers  (2023-01-28T10:24:38Z)
• Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction [55.569050872780224]
  We present an online framework for safe crowd-robot interaction based on risk-sensitive optimal control, wherein the risk is modeled by the entropic risk measure. Our modular approach decouples the crowd-robot interaction into learning-based prediction and model-based control. A simulation study and a real-world experiment show that the proposed framework can accomplish safe and efficient navigation while avoiding collisions with more than 50 humans in the scene.
  arXiv  Detail & Related papers  (2020-09-12T02:02:52Z)
• Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? [104.04999499189402]
  Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment. We propose an uncertainty-aware planning method, called emphrobust imitative planning (RIP) Our method can detect and recover from some distribution shifts, reducing the overconfident and catastrophic extrapolations in OOD scenes. We introduce an autonomous car novel-scene benchmark, textttCARNOVEL, to evaluate the robustness of driving agents to a suite of tasks with distribution shifts.
  arXiv  Detail & Related papers  (2020-06-26T11:07:32Z)

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.