Uncertainty Quantification for Safe and Reliable Autonomous Vehicles: A Review of Methods and Applications

Abstract

In the past decade, deep learning has been widely applied across various fields. However, its applicability in open-world scenarios is often limited due to the lack of quantifying uncertainty in both data and models. In recent years, a multitude of uncertainty quantification (UQ) approaches for neural networks have emerged and found applications in safety-critical domains such as autonomous vehicles and medical analysis. This paper aims to review the latest advancements in UQ methods and investigate their application specifically in the field of computer vision and autonomous vehicles. Initially, we identify several key qualifications, namely practicability, robustness, accuracy, scalability, and efficiency (referred to as PRASE), and employ them as evaluation criteria throughout this study. By considering these criteria as uniform measurements, we meticulously evaluate and compare the performance of different types of UQ methods, including Bayesian methods, ensemble methods, and single deterministic methods. Furthermore, we delve into the discussion of their application in diverse tasks within the autonomous vehicle domain, such as semantic segmentation, object detection, depth estimation, and end-to-end control. Through comprehensive analysis and comparison, we identify a range of challenges and propose future research directions in this field. Our findings shed light on the importance of addressing uncertainty quantification in deep learning models and provide insights into enhancing the reliability and performance of autonomous vehicles in real-world scenarios.