A3CarScene: An audio-visual dataset for driving scene understanding.

Data Brief

Groupe Signal Image et Instrumentation (GSII), École Supérieure d'Électronique de l'Ouest (ESEO), 10 Bd Jeanneteau, 49107 Angers, France.

Published: June 2023

AI Article Synopsis

  • - The article discusses a dataset created for automotive research, which includes audio and video recordings from a research vehicle driving 1500 km on various roads in the Marche Region, Italy, during October and November 2022.
  • - The sensor setup featured eight microphones and two dashcams, collecting around 31 hours of data in different weather and lighting conditions, with detailed annotations on road types, urbanization, weather, lighting levels, road conditions, and window states.
  • - This extensive dataset aids in developing and improving driving assistance technologies, supports deep learning engineers in creating benchmarks, and evaluates sensor placement for optimal performance in autonomous driving systems.

Article Abstract

Accurate perception and awareness of the environment surrounding the automobile is a challenge in automotive research. This article presents , a dataset recorded while driving a research vehicle equipped with audio and video sensors on public roads in the Marche Region, Italy. The sensor suite includes eight microphones installed inside and outside the passenger compartment and two dashcams mounted on the front and rear windows. Approximately 31 h of data for each device were collected during October and November 2022 by driving about 1500 km along diverse roads and landscapes, in variable weather conditions, in daytime and nighttime hours. All key information for the scene understanding process of automated vehicles has been accurately annotated. For each route, annotations with beginning and end timestamps report the type of road traveled (y, , and roads), the degree of urbanization of the area ( and ), the weather conditions (, and ), the level of lighting (, and ), the type ( or ) and moisture status ( or ) of the road pavement, and the state of the windows ( or ). This large-scale dataset is valuable for developing new driving assistance technologies based on audio or video data alone or in a multimodal manner and for improving the performance of systems currently in use. The data acquisition process with sensors in multiple locations allows for the assessment of the best installation placement concerning the task. Deep learning engineers can use this dataset to build new baselines, as a comparative benchmark, and to extend existing databases for autonomous driving.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10148019PMC
http://dx.doi.org/10.1016/j.dib.2023.109146DOI Listing

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