Scenario Understanding of Traffic Scenes Through Large Visual Language Models

TL;DR

This paper evaluates large visual language models like GPT-4 and LLaVA for understanding and classifying urban traffic scenes, aiming to improve autonomous driving systems' generalization and reduce manual annotation efforts.

Contribution

It introduces a scalable captioning pipeline using LVLMs for urban traffic scene understanding and demonstrates their effectiveness on multiple datasets.

Findings

LVLMs effectively classify traffic scenes with high accuracy.

The proposed pipeline enables flexible deployment on new datasets.

LVLMs reduce the need for manual annotation in scene categorization.

Abstract

Deep learning models for autonomous driving, encompassing perception, planning, and control, depend on vast datasets to achieve their high performance. However, their generalization often suffers due to domain-specific data distributions, making an effective scene-based categorization of samples necessary to improve their reliability across diverse domains. Manual captioning, though valuable, is both labor-intensive and time-consuming, creating a bottleneck in the data annotation process. Large Visual Language Models (LVLMs) present a compelling solution by automating image analysis and categorization through contextual queries, often without requiring retraining for new categories. In this study, we evaluate the capabilities of LVLMs, including GPT-4 and LLaVA, to understand and classify urban traffic scenes on both an in-house dataset and the BDD100K. We propose a scalable captioning pipeline that integrates state-of-the-art models, enabling a flexible deployment on new datasets. Our analysis, combining quantitative metrics with qualitative insights, demonstrates the effectiveness of LVLMs to understand urban traffic scenarios and highlights their potential as an efficient tool for data-driven advancements in autonomous driving.