# Uncertainty-Aware Safety-Critical Decision and Control for Autonomous Vehicles at Unsignalized Intersections

arXiv: https://arxiv.org/abs/2505.19939 · HTML: https://mlsanity.com/paper/2505.19939/

## Key facts

- Cited by 1 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 15 of its 40 references that are in the map.
- First posted on arXiv 26 May 2025.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)
- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 15 of 15)

- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [Deep Exploration via Bootstrapped DQN](https://mlsanity.com/paper/1602.04621/) (2016)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Responsive Safety in Reinforcement Learning by PID Lagrangian Methods](https://mlsanity.com/paper/2007.03964/) (2020)
- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [Randomized Prior Functions for Deep Reinforcement Learning](https://mlsanity.com/paper/1806.03335/) (2018)
- [DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation](https://mlsanity.com/paper/2101.05544/) (2021)
- [Ensemble Quantile Networks: Uncertainty-Aware Reinforcement Learning with Applications in Autonomous Driving](https://mlsanity.com/paper/2105.10266/) (2021)
- [DSAC: Distributional Soft Actor-Critic for Risk-Sensitive Reinforcement Learning](https://mlsanity.com/paper/2004.14547/) (2020)
- [Identify, Estimate and Bound the Uncertainty of Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2305.07487/) (2023)
- [Meta SAC-Lag: Towards Deployable Safe Reinforcement Learning via MetaGradient-based Hyperparameter Tuning](https://mlsanity.com/paper/2408.07962/) (2024)
- [High-Order Control Barrier Functions: Insights and a Truncated Taylor-Based Formulation](https://mlsanity.com/paper/2503.15014/) (2025)
- [Synthesize Efficient Safety Certificates for Learning-Based Safe Control using Magnitude Regularization](https://mlsanity.com/paper/2209.11787/) (2022)
- [Risk-Aware Reinforcement Learning for Autonomous Driving: Improving Safety When Driving through Intersection](https://mlsanity.com/paper/2503.19690/) (2025)

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

- [A Risk-Sensitive and Uncertainty-Aware Decision-Making and Control Framework for Safe and Robust Autonomous Driving](https://mlsanity.com/paper/2609.09650/) (2026)

## Abstract (arXiv)

Reinforcement learning (RL) has demonstrated potential in autonomous driving (AD) decision tasks. However, applying RL to urban AD, particularly in intersection scenarios, still faces significant challenges. The lack of safety constraints makes RL vulnerable to risks. Additionally, cognitive limitations and environmental randomness can lead to unreliable decisions in safety-critical scenarios. Therefore, it is essential to quantify confidence in RL decisions to improve safety. This paper proposes an Uncertainty-aware Safety-Critical Decision and Control (USDC) framework, which generates a risk-averse policy by constructing a risk-aware ensemble distributional RL, while estimating uncertainty to quantify the policy's reliability. Subsequently, a high-order control barrier function (HOCBF) is employed as a safety filter to minimize intervention policy while dynamically enhancing constraints based on uncertainty. The ensemble critics evaluate both HOCBF and RL policies, embedding uncertainty to achieve dynamic switching between safe and flexible strategies, thereby balancing safety and efficiency. Simulation tests on unsignalized intersections in multiple tasks indicate that USDC can improve safety while maintaining traffic efficiency compared to baselines.