Benchmarking Autonomous Vehicles: A Driver Foundation Model Framework

2026-02-09

AI Summary

Current autonomous driving systems still fall short of human drivers in critical aspects such as safety, comfort, efficiency, and energy consumption, hindering their large-scale deployment. This work proposes the Driver Foundation Model (DFM) framework—the first of its kind—to establish a comprehensive, human-driving-benchmarked evaluation paradigm by training a general-purpose driving model on large-scale real-world driving data. The DFM framework systematically defines human-level driving standards through a multidimensional metric suite encompassing safety margins, energy efficiency, ride comfort, and other key dimensions, thereby providing a unified and quantifiable benchmark for validating and optimizing autonomous driving systems. A prototype implementation of DFM has been developed, with preliminary results demonstrating its effectiveness in performance assessment and system optimization.

Technology Category

• Application Domains: Mobility, Driving & Flight
• Natural Language Processing: Safety and Robustness
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• autonomous vehicles
• safety
• comfort
• commuting efficiency
• energy economy

Innovation

• Driver Foundation Model
• Autonomous Vehicles Benchmarking
• Human-Centric Safety
• Energy Economy
• Large-Scale Dataset