面向自动驾驶规划的不确定性感知预测与应用：定义、方法与比较
Uncertainty-Aware Prediction and Application in Planning for Autonomous Driving: Definitions, Methods, and Comparison
AI总结：
本文提出统一预测与规划框架，利用高斯混合模型和深度集成同时建模SAU、LAU与EU，并在CommonRoad及感知受限场景中验证其规划可靠性优势。
摘要：
自动驾驶系统面临着在充满不确定性的复杂动态环境中导航的艰巨挑战。本研究提出了一个统一的预测与规划框架，同时对短期偶然不确定性（SAU）、长期偶然不确定性（LAU）和认知不确定性（EU）进行建模，以在动态情境中进行预测并为规划建立稳健基础。该框架采用高斯混合模型和深度集成方法，同时捕获并评估SAU、LAU和EU，而传统方法无法同时整合这些不确定性。此外，研究引入了考虑多种不确定性的不确定性感知规划。本研究的贡献包括：与现有方法相比，对不确定性估计、风险建模和规划方法进行比较。所提出的方法在CommonRoad基准以及感知受限的设置下得到了严格评估。这些实验阐明了不同不确定性因素在自动驾驶过程中的优势和作用。此外，对各种不确定性建模策略的比较评估凸显了对多种类型不确定性进行建模的益处，从而提高规划的准确性和可靠性。所提出的框架促进了UAP方法的发展，并超越了现有的不确定性感知风险模型，尤其是在考虑多样化交通场景时。项目页面：https://swb19.github.io/UAP/。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
