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Vehicle Recognition in an Autonomous Driving System for Road and Vehicle Cooperation

  • Infrastructure-cooperated autonomous driving systems are attracting attention as a method for promoting the practical application of highly functional autonomous driving. We focused on the part where recognition processing on the infrastructure side can be advanced, which is not possible with in-vehicle processing. Using a fixed-point camera and recognition of the situation behind a vehicle or object in which multiple cameras are linked are such examples. In this paper, we selected a difficult situation such as a curved road, focused on the scene where the vehicle is running while deforming its shape, and examined a method of accurately recognizing the vehicle using a fixed-point camera. It is a study of the criteria for dividing the vehicle shape class. Recognition of the general vehicle class of autonomous driving also needs to identify unknown objects and non-vehicles. In his article, we have excluded the identification of unknown objects and focused on recognizing known vehicles using deep learning. Consider six different vehicleInfrastructure-cooperated autonomous driving systems are attracting attention as a method for promoting the practical application of highly functional autonomous driving. We focused on the part where recognition processing on the infrastructure side can be advanced, which is not possible with in-vehicle processing. Using a fixed-point camera and recognition of the situation behind a vehicle or object in which multiple cameras are linked are such examples. In this paper, we selected a difficult situation such as a curved road, focused on the scene where the vehicle is running while deforming its shape, and examined a method of accurately recognizing the vehicle using a fixed-point camera. It is a study of the criteria for dividing the vehicle shape class. Recognition of the general vehicle class of autonomous driving also needs to identify unknown objects and non-vehicles. In his article, we have excluded the identification of unknown objects and focused on recognizing known vehicles using deep learning. Consider six different vehicle shapes on curved roads. We investigated the impact of vehicle shape class integration and performance, and found that the integration of the two classes reduced the number of vehicle shape classes and increased recognition accuracy.show moreshow less

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Metadaten
Author:Stefan-Alexander SchneiderORCiDGND, Kazuo Ohzeki, Koichi Kamijo
Parent Title (English):Fast-zero ´21, September 28-30, 2021, Full Online Conference: Program & Proceedings
Publisher:Society of Automotive Engineers of Japan, Inc. (JSAE)
Document Type:conference proceeding (article)
Conference:Fast-zero ´21, September 28-30, 2021, Full Online Conference, 6th International Symposium on Future Active Safety Technology Toward zero traffic accidents
Language:English
Year of first Publication:2021
Article Number:20219083
Number of pages:6 Seiten
First Page:1
Last Page:6
Institutes:Fakultät Elektrotechnik
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften
Research focus:FSP2: Mobilität
Publication Lists:Schneider, Stefan-Alexander
Publication reviewed:begutachtet
Release Date:2022/01/24
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