Road Terrain Detection: Avoiding Common Obstacle Detection Assumptions Using Sensor Fusion

Autor(es)

Shinzato, Patrick Y. ; Wolf, Denis E. ; Stiller, Christoph ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2014 IEEE INTELLIGENT VEHICLES SYMPOSIUM PROCEEDINGS; v. N/A, p. 6-pg., 2014-01-01.

Resumo

Obstacle detection is a fundamental task for Advanced Driver Assistance Systems (ADAS) and Self-driving cars. Several commercial systems like Adaptive Cruise Controls and Collision Warning Systems depend on them to notify the driver about a risky situation. Several approaches have been presented in the literature in the last years. However, most of them are limited to specific scenarios and restricted conditions. In this paper we propose a robust sensor fusion-based method capable of detecting obstacles in a wide variety of scenarios using a minimum number of parameters. Our approach is based on the spatial-relationship on perspective images provided by a single camera and a 3D LIDAR. Experimental tests have been carried out in different conditions using the standard ROAD-KITTI benchmark, obtaining positive results. (AU)

• Processo FAPESP: 10/01305-1 - Sistema de direção assistida para veículos baseado em fusão de sensores.
• Beneficiário: Patrick Yuri Shinzato
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 11/21956-0 - Desenvolvimento de veículos autônomos: um método híbrido para identificação de sinalização horizontal de trânsito em ambientes urbanos
• Beneficiário: Patrick Yuri Shinzato
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Doutorado