InterHub: A Naturalistic Trajectory Dataset with Dense Interaction for Autonomous Driving

TL;DR

This work presents InterHub, a dense interaction dataset derived by mining interaction events from extensive naturalistic driving records, and employs formal methods to describe and extract multi-agent interaction events, exposing the limitations of existing autonomous driving solutions.

Abstract

The driving interaction-a critical yet complex aspect of daily driving-lies at the core of autonomous driving research. However, real-world driving scenarios sparsely capture rich interaction events, limiting the availability of comprehensive trajectory datasets for this purpose. To address this challenge, we present InterHub, a dense interaction dataset derived by mining interaction events from extensive naturalistic driving records. We employ formal methods to describe and extract multi-agent interaction events, exposing the limitations of existing autonomous driving solutions. Additionally, we introduce a user-friendly toolkit enabling the expansion of InterHub with both public and private data. By unifying, categorizing, and analyzing diverse interaction events, InterHub facilitates cross-comparative studies and large-scale research, thereby advancing the evaluation and development of autonomous driving technologies.

Figures (9)

• Figure 1: Road map of dense driving interaction dataset construction and application.
• Figure 2: Accumulated usage of widely adopted naturalistic driving datasets.
• Figure 3: Overall data structure of InterHub
• Figure 4: Interaction defined by min-effort conflict resolution.
• Figure 5: Formal description of interaction.