Autonomous vehicles (AVs) are poised to revolutionize global transportation systems. However, its widespread acceptance and market penetration remain significantly below expectations. This gap is primarily driven by persistent challenges in safety, comfort, commuting efficiency and energy economy when compared to the performance of experienced human drivers. We hypothesize that these challenges can be addressed through the development of a driver foundation model (DFM). Accordingly, we propose a framework for establishing DFMs to comprehensively benchmark AVs. Specifically, we describe a large-scale dataset collection strategy for training a DFM, discuss the core functionalities such a model should possess, and explore potential technical solutions to realize these functionalities. We further present the utility of the DFM across the operational spectrum, from defining human-centric safety envelopes to establishing benchmarks for energy economy. Overall, We aim to formalize the DFM concept and introduce a new paradigm for the systematic specification, verification and validation of AVs.

翻译：自动驾驶车辆（AVs）有望彻底变革全球交通系统。然而，其广泛接受度和市场渗透率仍远低于预期。这一差距主要源于与经验丰富的人类驾驶员相比，自动驾驶车辆在安全性、舒适性、通勤效率和能源经济性方面持续存在的挑战。我们假设，这些挑战可以通过开发驾驶员基础模型（DFM）来解决。为此，我们提出了一个建立DFM以全面评估自动驾驶车辆性能的框架。具体而言，我们描述了用于训练DFM的大规模数据集收集策略，讨论了此类模型应具备的核心功能，并探讨了实现这些功能的潜在技术方案。我们进一步展示了DFM在操作全谱系中的实用性，从定义以人为本的安全包络线到建立能源经济性基准。总体而言，我们旨在形式化DFM概念，并为自动驾驶车辆的系统化规范、验证与确认引入一种新范式。