用驾驶员基础模型统一评估自动驾驶安全与效率。
Benchmarking Autonomous Vehicles: A Driver Foundation Model Framework
- 构建大规模数据集训练驾驶员基础模型，模拟人类驾驶行为。
- 提出完整评估框架，涵盖安全、舒适、能耗等核心指标。
- 适合自动驾驶研发与评测团队参考，推动行业标准落地。
自动驾驶汽车有望重塑全球交通系统，但其普及程度远低于预期，主要受限于安全、舒适性、通勤效率和能源经济性等方面与经验丰富的驾驶员相比仍有差距。我们假设这些问题可通过开发驾驶员基础模型（DFM）来解决。为此，我们提出一个建立DFM的框架，包括大规模数据集采集策略、模型应具备的核心功能，以及实现这些功能的技术方案。此外，我们展示了DFM在全运行周期中的应用价值，从定义以人为中心的安全边界到建立能源经济性的基准。总体目标是正式确立DFM概念，引入一种系统化规范、验证与评估自动驾驶的新范式。
原文摘要 · Abstract (English)
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.
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