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Conference papers

Object Perception for Intelligent Vehicle Applications: A Multi-Sensor Fusion Approach

Trung-Dung Vu 1 Olivier Aycard 2 Fabio Tango 3
1 E-MOTION - Geometry and Probability for Motion and Action
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : The paper addresses the problem of object perception for intelligent vehicle applications with main tasks of detection, tracking and classification of obstacles where multiple sensors (i.e.: lidar, camera and radar) are used. New algorithms for raw sensor data processing and sensor data fusion are introduced making the most information from all sensors in order to provide a more reliable and accurate information about objects in the vehicle environment. The proposed object perception module is implemented and tested on a demonstrator car in real-life traffics and evaluation results are presented.
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Submitted on : Monday, July 7, 2014 - 11:31:58 AM
Last modification on : Wednesday, February 2, 2022 - 3:56:46 PM
Long-term archiving on: : Tuesday, October 7, 2014 - 12:12:52 PM


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  • HAL Id : hal-01019527, version 1


Trung-Dung Vu, Olivier Aycard, Fabio Tango. Object Perception for Intelligent Vehicle Applications: A Multi-Sensor Fusion Approach. Intelligent Vehicles Symposium, 2014 IEEE, Jun 2014, Dearborn, MI, United States. pp.100-106. ⟨hal-01019527⟩



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