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

TL;DR

This paper explores how providing agents with perceptual uncertainty information affects their decision-making in automated driving, showing that it leads to more adaptive and safer driving behaviors.

Contribution

It demonstrates that incorporating perceptual uncertainty into the agent's observations improves decision-making and task efficiency in autonomous driving scenarios.

Findings

Unreliable perception causes defensive driving behavior.

Informing the agent about uncertainty improves task speed.

Uncertainty-aware agents better account for risks.

Abstract

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.