Benchmarking Autonomous Vehicles: A Driver Foundation Model Framework

TL;DR

This paper introduces a driver foundation model framework to benchmark autonomous vehicles, aiming to improve safety, comfort, efficiency, and energy economy through systematic evaluation and a large-scale dataset.

Contribution

It formalizes the concept of a driver foundation model and proposes a comprehensive framework for benchmarking AVs across multiple operational aspects.

Findings

Proposes a large-scale dataset collection strategy for training DFMs

Defines core functionalities for driver foundation models

Demonstrates utility of DFM in safety and energy benchmarks

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.