# Does Knowledge About Perceptual Uncertainty Help an Agent in Automated Driving?

arXiv: https://arxiv.org/abs/2502.11864 · HTML: https://mlsanity.com/paper/2502.11864/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 6 of its 48 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Placed in

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

## Builds on (most cited first, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Maps](https://mlsanity.com/paper/2010.01884/) (2020)
- [Safety Guaranteed Robust Multi-Agent Reinforcement Learning with Hierarchical Control for Connected and Automated Vehicles](https://mlsanity.com/paper/2309.11057/) (2023)
- [Safe Reinforcement Learning with Contrastive Risk Prediction](https://mlsanity.com/paper/2209.09648/) (2022)
- [Trajectory Planning for Autonomous Vehicle Using Iterative Reward Prediction in Reinforcement Learning](https://mlsanity.com/paper/2404.12079/) (2024)

## Abstract (arXiv)

Agents in real-world scenarios like automated driving deal with uncertainty in their environment, in particular due to perceptual uncertainty. Although, reinforcement learning is dedicated to autonomous decision-making under uncertainty these algorithms are typically not informed about the uncertainty currently contained in their environment. On the other hand, uncertainty estimation for perception itself is typically directly evaluated in the perception domain, e.g., in terms of false positive detection rates or calibration errors based on camera images. Its use for deciding on goal-oriented actions remains largely unstudied. In this paper, we investigate how an agent's behavior is influenced by an uncertain perception and how this behavior changes if information about this uncertainty is available. Therefore, we consider a proxy task, where the agent is rewarded for driving a route as fast as possible without colliding with other road users. For controlled experiments, we introduce uncertainty in the observation space by perturbing the perception of the given agent while informing the latter. Our experiments show that an unreliable observation space modeled by a perturbed perception leads to a defensive driving behavior of the agent. Furthermore, when adding the information about the current uncertainty directly to the observation space, the agent adapts to the specific situation and in general accomplishes its task faster while, at the same time, accounting for risks.