Urban Intersection Classification: A Comparative Analysis
Author:
Ballardini Augusto LuisORCID,
Hernández Saz ÁlvaroORCID,
Carrasco Limeros SandraORCID,
Lorenzo JavierORCID,
Parra Alonso IgnacioORCID,
Hernández Parra NoeliaORCID,
García Daza IvánORCID,
Sotelo Miguel ÁngelORCID
Abstract
Understanding the scene in front of a vehicle is crucial for self-driving vehicles and Advanced Driver Assistance Systems, and in urban scenarios, intersection areas are one of the most critical, concentrating between 20% to 25% of road fatalities. This research presents a thorough investigation on the detection and classification of urban intersections as seen from onboard front-facing cameras. Different methodologies aimed at classifying intersection geometries have been assessed to provide a comprehensive evaluation of state-of-the-art techniques based on Deep Neural Network (DNN) approaches, including single-frame approaches and temporal integration schemes. A detailed analysis of most popular datasets previously used for the application together with a comparison with ad hoc recorded sequences revealed that the performances strongly depend on the field of view of the camera rather than other characteristics or temporal-integrating techniques. Due to the scarcity of training data, a new dataset is created by performing data augmentation from real-world data through a Generative Adversarial Network (GAN) to increase generalizability as well as to test the influence of data quality. Despite being in the relatively early stages, mainly due to the lack of intersection datasets oriented to the problem, an extensive experimental activity has been performed to analyze the individual performance of each proposed systems.
Funder
H2020 Marie Skłodowska-Curie Actions
Spanish Ministry of Science, Innovation and Universities
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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