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

Author(s)

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

• Document type: Journal article
• Source: 2014 IEEE INTELLIGENT VEHICLES SYMPOSIUM PROCEEDINGS; v. N/A, p. 6-pg., 2014-01-01.

Abstract

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)

• FAPESP's process: 10/01305-1 - A vehicle driving assistance system based on sensor fusion.
• Grantee: Patrick Yuri Shinzato
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 11/21956-0 - Development of autonomous vehicles: a hybrid method for identifying markings in urban traffic
• Grantee: Patrick Yuri Shinzato
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate