Uncertainty-Aware Prediction and Application in Planning for Autonomous Driving: Definitions, Methods, and Comparison

TL;DR

This work tackles the challenge of uncertainty in autonomous driving by proposing a unified prediction and planning framework that simultaneously models short-term aleatoric uncertainty, long-term aleatoric uncertainty, and epistemic uncertainty. It utilizes Gaussian mixtures for multimodal predictions and deep ensembles to quantify epistemic uncertainty, integrating these into an uncertainty-aware planning pipeline with a hierarchical risk model. The approach is evaluated on the CommonRoad benchmark under normal and limited-perception conditions, demonstrating that jointly modeling SAU, LAU, and EU improves planning safety, robustness, and efficiency compared to baselines. The findings highlight the value of incorporating multiple uncertainty sources into prediction and planning, with comprehensive risk modeling offering significant gains in real-world, dynamic driving scenarios.

Abstract

Autonomous driving systems face the formidable challenge of navigating intricate and dynamic environments with uncertainty. This study presents a unified prediction and planning framework that concurrently models short-term aleatoric uncertainty (SAU), long-term aleatoric uncertainty (LAU), and epistemic uncertainty (EU) to predict and establish a robust foundation for planning in dynamic contexts. The framework uses Gaussian mixture models and deep ensemble methods, to concurrently capture and assess SAU, LAU, and EU, where traditional methods do not integrate these uncertainties simultaneously. Additionally, uncertainty-aware planning is introduced, considering various uncertainties. The study's contributions include comparisons of uncertainty estimations, risk modeling, and planning methods in comparison to existing approaches. The proposed methods were rigorously evaluated using the CommonRoad benchmark and settings with limited perception. These experiments illuminated the advantages and roles of different uncertainty factors in autonomous driving processes. In addition, comparative assessments of various uncertainty modeling strategies underscore the benefits of modeling multiple types of uncertainties, thus enhancing planning accuracy and reliability. The proposed framework facilitates the development of methods for UAP and surpasses existing uncertainty-aware risk models, particularly when considering diverse traffic scenarios. Project page: https://swb19.github.io/UAP/.

Figures (7)

• Figure 1: Uncertainty in prediction and risk-aware planning.
• Figure 2: Overview of current UAP approaches: a classification based on the type of modeled uncertainty. [*] indicates the absence of deep learning models, whereas [*] denotes a lack of distinction among the various types of uncertainties modeled or their effects.
• Figure 3: Proposed unified prediction and planning framework that considers different types of uncertainties.
• Figure 4: The modeled uncertainties and their combinations, as well as various uncertainty-aware risk models.
• Figure 5: The process of uncertainty-aware planning.