ROAD: The Road Event Awareness Dataset for Autonomous Driving

The development of autonomous vehicles (AVs) requires a thorough comprehension of dynamic roadconditions in addition to object identification. By advancing from static object recognition to dynamicevent understanding, the Road Event Awareness Dataset (ROAD) presents a fresh standard for AVsituational awareness. The rich semantic triplets (Agent, Action, Location) that ROAD is labeled withacross more than 122,000 video frames allow models to understand how traffic players interact with theiractions in different spatial situations. This study provides a thorough review of the ROAD dataset, talksabout how useful it is for improving road event recognition, and investigates how it may be used for AVdecision-making, event prediction, and continuous learning. ROAD establishes the foundation for saferand more intelligent autonomous driving solutions by promoting context-aware technologies.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-08
DOI
https://doi.org/10.5281/zenodo.15622148
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
0.00
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article

ROAD: The Road Event Awareness Dataset for Autonomous Driving

Zenodo (CERN European Organization for Nuclear Research)
Autonomous Vehicle Technology and Safety
article

ROAD: The Road Event Awareness Dataset for Autonomous Driving

article en

Abstract

The development of autonomous vehicles (AVs) requires a thorough comprehension of dynamic roadconditions in addition to object identification. By advancing from static object recognition to dynamicevent understanding, the Road Event Awareness Dataset (ROAD) presents a fresh standard for AVsituational awareness. The rich semantic triplets (Agent, Action, Location) that ROAD is labeled withacross more than 122,000 video frames allow models to understand how traffic players interact with theiractions in different spatial situations. This study provides a thorough review of the ROAD dataset, talksabout how useful it is for improving road event recognition, and investigates how it may be used for AVdecision-making, event prediction, and continuous learning. ROAD establishes the foundation for saferand more intelligent autonomous driving solutions by promoting context-aware technologies.

Zenodo (CERN European Organization for Nuclear Research)
Sustainable cities and communities
Openalex Percentile: Top 53%
Autonomous Vehicle Technology and Safety
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