Journal Article


ROAD: The ROad event Awareness Dataset for autonomous driving

Abstract

Humans approach driving in a holistic fashion which entails, in particular, understanding road events and their evolution. Injecting these capabilities in an autonomous vehicle has thus the potential to take situational awareness and decision making closer to human-level performance. To this purpose, we introduce the ROad event Awareness Dataset (ROAD) for Autonomous Driving, to our knowledge the first of its kind. ROAD is designed to test an autonomous vehicle’s ability to detect road events, defined as triplets composed by a moving agent, the action(s) it performs and the corresponding scene locations. ROAD comprises 22 videos, originally from the Oxford RobotCar Dataset, annotated with bounding boxes showing the location in the image plane of each road event. We propose a number of relevant detection tasks and provide as a baseline a new incremental algorithm for online road event awareness, based on inflating RetinaNet along time. We also report the performance on the ROAD tasks of Slowfast and YOLOv5 detectors, as well as that of the top participants in the ROAD challenge co-located with ICCV 2021. Our baseline results highlight the challenges faced by situation awareness in autonomous driving. Finally, ROAD allows scholars to investigate exciting tasks such as complex (road) activity detection, future road event anticipation and the modelling of sentient road agents in terms of mental states. The dataset is available at https://github.com/gurkirt/road-dataset; the baseline code can be found at https://github.com/gurkirt/3D-RetinaNet.

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Authors

Singh, Gurkit
Akrigg, Stephen
Di Maio, Manuele
Fontana, Valentina
Javanmard Alitappeh, Reza
Saha, Suman
Jeddisaravi, Kossar
Yousefi, Farzad
Culley, Jacob
Nicholson, Tom
Omokeowa, Jordan
Khan, Salman
Grazioso, Stanislao
Bradley, Andrew
Di Gironimo, Giuseppe
Cuzzolin, Fabio

Oxford Brookes departments

School of Engineering, Computing and Mathematics

Dates

Year of publication: 2022
Date of RADAR deposit: 2022-02-04


Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License


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