# Argus: Resilience-Oriented Safety Assurance Framework for End-to-End ADSs

arXiv: https://arxiv.org/abs/2511.09032 · HTML: https://mlsanity.com/paper/2511.09032/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial scenarios and safety testing, one of 9 directions in End-to-end autonomous driving.
- Builds on 33 of its 85 references that are in the map.
- First posted on arXiv 12 Nov 2025.

## Placed in

- End-to-end autonomous driving / [Adversarial scenarios and safety testing](https://mlsanity.com/driving/adversarial-scenarios-and-safety-testing/)

## Builds on (most cited first, 30 of 33)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [VAD: Vectorized Scene Representation for Efficient Autonomous Driving](https://mlsanity.com/paper/2303.12077/) (2023)
- [DriveLM: Driving with Graph Visual Question Answering](https://mlsanity.com/paper/2312.14150/) (2023)
- [End-to-end Autonomous Driving: Challenges and Frontiers](https://mlsanity.com/paper/2306.16927/) (2023)
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016)
- [Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving](https://mlsanity.com/paper/2406.03877/) (2024)
- [Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline](https://mlsanity.com/paper/2206.08129/) (2022)
- [Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer](https://mlsanity.com/paper/2207.14024/) (2022)
- [DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving](https://mlsanity.com/paper/2503.07656/) (2025)
- [Learning to drive from a world on rails](https://mlsanity.com/paper/2105.00636/) (2021)
- [End-to-end Lane Shape Prediction with Transformers](https://mlsanity.com/paper/2011.04233/) (2020)
- [Rethinking Efficient Lane Detection via Curve Modeling](https://mlsanity.com/paper/2203.02431/) (2022)
- [Anomaly Detection in Autonomous Driving: A Survey](https://mlsanity.com/paper/2204.07974/) (2022)
- [Misbehaviour Prediction for Autonomous Driving Systems](https://mlsanity.com/paper/1910.04443/) (2019)
- [Think2Drive: Efficient Reinforcement Learning by Thinking in Latent World Model for Quasi-Realistic Autonomous Driving (in CARLA-v2)](https://mlsanity.com/paper/2402.16720/) (2024)
- [Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model](https://mlsanity.com/paper/2210.04017/) (2022)
- [SOTER: A Runtime Assurance Framework for Programming Safe Robotics Systems](https://mlsanity.com/paper/1808.07921/) (2018)
- [AdaWM: Adaptive World Model based Planning for Autonomous Driving](https://mlsanity.com/paper/2501.13072/) (2025)
- [DeepGuard: A Framework for Safeguarding Autonomous Driving Systems from Inconsistent Behavior](https://mlsanity.com/paper/2111.09533/) (2021)
- [LawBreaker: An Approach for Specifying Traffic Laws and Fuzzing Autonomous Vehicles](https://mlsanity.com/paper/2208.14656/) (2022)
- [BehAVExplor: Behavior Diversity Guided Testing for Autonomous Driving Systems](https://mlsanity.com/paper/2307.07493/) (2023)
- [Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification](https://mlsanity.com/paper/2404.18573/) (2024)
- [No driver, No Regulation? --Online Legal Driving Behavior Monitoring for Self-driving Vehicles](https://mlsanity.com/paper/2212.04156/) (2022)
- [Runtime Safety Assurance for Learning-enabled Control of Autonomous Driving Vehicles](https://mlsanity.com/paper/2109.13446/) (2021)
- [REDriver: Runtime Enforcement for Autonomous Vehicles](https://mlsanity.com/paper/2401.02253/) (2024)
- [Robust Long-Range Perception Against Sensor Misalignment in Autonomous Vehicles](https://mlsanity.com/paper/2408.11196/) (2024)
- [Dual-AEB: Synergizing Rule-Based and Multimodal Large Language Models for Effective Emergency Braking](https://mlsanity.com/paper/2410.08616/) (2024)
- [Enabling Software Resilience in GPGPU Applications via Partial Thread Protection](https://mlsanity.com/paper/2103.02825/) (2021)
- [FixDrive: Automatically Repairing Autonomous Vehicle Driving Behaviour for 0.08 per Violation](https://mlsanity.com/paper/2502.08260/) (2025)

## Built on by (most cited first, 1 of 1)

- [Auditing Latent-Space Monitors for Autonomous Driving](https://mlsanity.com/paper/2609.30557/) (2026)

## Abstract (arXiv)

End-to-end autonomous driving systems (ADSs), with their strong capabilities in environmental perception and generalizable driving decisions, are attracting growing attention from both academia and industry. However, once deployed on public roads, ADSs are inevitably exposed to diverse driving hazards that may compromise safety and degrade system performance. This raises a strong demand for resilience of ADSs, particularly the capability to continuously monitor driving hazards and adaptively respond to potential safety violations, which is crucial for maintaining robust driving behaviors in complex driving scenarios. To bridge this gap, we propose a runtime resilience-oriented framework, Argus, to mitigate the driving hazards, thus preventing potential safety violations and improving the driving performance of an ADS. Argus continuously monitors the trajectories generated by the ADS for potential hazards and, whenever the EGO vehicle is deemed unsafe, seamlessly takes control through a hazard mitigator. We integrate Argus with three state-of-the-art end-to-end ADSs, i.e., TCP, UniAD and VAD. Our evaluation has demonstrated that Argus effectively and efficiently enhances the resilience of ADSs, improving the driving score of the ADS by up to 150.30% on average, and preventing up to 64.38% of the violations, with little additional time overhead.