Abstract
Autonomous vehicle encounters numerous difficulties which need to be solved. Precise detection and perception of surrounding obstacles are the first step required to overcome the problems. Therefore, it is important to properly fuse the sensor data. In this paper, one front camera and four corner radars are used to percept the surrounding obstacles. Two algorithms are introduced; a sensor fusion algorithm and a compensating algorithm which improve the performance of the sensor fusion algorithm. Nearest neighbor (NN) algorithm and linear Kalman filter are used for data association and tracking respectively. Moreover, the obstacles' boundary information which is given by camera and radars is used to estimate the free space. By applying Occupancy Grid Map (OGM) with boundary information, previous fused data can be compensated.
Original language | English |
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Title of host publication | ICCAS 2019 - 2019 19th International Conference on Control, Automation and Systems, Proceedings |
Publisher | IEEE Computer Society |
Pages | 575-578 |
Number of pages | 4 |
ISBN (Electronic) | 9788993215182 |
DOIs | |
Publication status | Published - 2019 Oct |
Event | 19th International Conference on Control, Automation and Systems, ICCAS 2019 - Jeju, Korea, Republic of Duration: 2019 Oct 15 → 2019 Oct 18 |
Publication series
Name | International Conference on Control, Automation and Systems |
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Volume | 2019-October |
ISSN (Print) | 1598-7833 |
Conference
Conference | 19th International Conference on Control, Automation and Systems, ICCAS 2019 |
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Country/Territory | Korea, Republic of |
City | Jeju |
Period | 19/10/15 → 19/10/18 |
Bibliographical note
Publisher Copyright:© 2019 Institute of Control, Robotics and Systems - ICROS.
Keywords
- Autonomous Vehicle
- Occupancy Grid Map
- Sensor Fusion
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Control and Systems Engineering
- Electrical and Electronic Engineering