# Runtime Safety Assurance for Learning-enabled Control of Autonomous Driving Vehicles

arXiv: https://arxiv.org/abs/2109.13446 · HTML: https://mlsanity.com/paper/2109.13446/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 28 Sep 2021.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Continuous Deep Q-Learning with Model-based Acceleration](https://mlsanity.com/paper/1603.00748/) (2016)
- [End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances](https://mlsanity.com/paper/1911.10868/) (2019)
- [A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers](https://mlsanity.com/paper/1804.07871/) (2018)
- [End-to-End Race Driving with Deep Reinforcement Learning](https://mlsanity.com/paper/1807.02371/) (2018)
- [Neural Simplex Architecture](https://mlsanity.com/paper/1908.00528/) (2019)
- [SOTER: A Runtime Assurance Framework for Programming Safe Robotics Systems](https://mlsanity.com/paper/1808.07921/) (2018)
- [Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning](https://mlsanity.com/paper/1904.10171/) (2019)
- [Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States](https://mlsanity.com/paper/1809.03214/) (2018)
- [Hierarchical Reinforcement Learning for Self-Driving Decision-Making without Reliance on Labeled Driving Data](https://mlsanity.com/paper/2001.09816/) (2020)

## Built on by (most cited first, 9 of 9)

- [Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking](https://mlsanity.com/paper/2205.06750/) (2022)
- [Embodied Foundation Models at the Edge: A Survey of Deployment Constraints and Mitigation Strategies](https://mlsanity.com/paper/2603.16952/) (2026)
- [Physics-Driven ML-Based Modelling for Correcting Inverse Estimation](https://mlsanity.com/paper/2309.13985/) (2023)
- [A Safe Self-evolution Algorithm for Autonomous Driving Based on Data-Driven Risk Quantification Model](https://mlsanity.com/paper/2408.12805/) (2024)
- [Argus: Resilience-Oriented Safety Assurance Framework for End-to-End ADSs](https://mlsanity.com/paper/2511.09032/) (2025)
- [Real-DRL: Teach and Learn in Reality](https://mlsanity.com/paper/2511.00112/) (2025)
- [Engineering Trustworthy Agentic AI for Critical Systems](https://mlsanity.com/paper/2607.18548/) (2026)
- [Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment](https://mlsanity.com/paper/2608.12198/) (2026)
- [Cognitively-Grounded On-Device Runtime Learning for Ground Robots in Unknown Physical Environments](https://mlsanity.com/paper/2608.28677/) (2026)

## Abstract (arXiv)

Providing safety guarantees for Autonomous Vehicle (AV) systems with machine-learning-based controllers remains a challenging issue. In this work, we propose Simplex-Drive, a framework that can achieve runtime safety assurance for machine-learning enabled controllers of AVs. The proposed Simplex-Drive consists of an unverified Deep Reinforcement Learning (DRL)-based advanced controller (AC) that achieves desirable performance in complex scenarios, a Velocity-Obstacle (VO) based baseline safe controller (BC) with provably safety guarantees, and a verified mode management unit that monitors the operation status and switches the control authority between AC and BC based on safety-related conditions. We provide a formal correctness proof of Simplex-Drive and conduct a lane-changing case study in dense traffic scenarios. The simulation experiment results demonstrate that Simplex-Drive can always ensure operation safety without sacrificing control performance, even if the DRL policy may lead to deviations from the safe status.