Abstract
Over a million fatal accidents occur every year with road vehicles. Road user detection for Advanced Driver Assistance Systems and Autonomous Vehicles could significantly reduce the number of accidents. Despite the research focus on road user detection and such systems, there is a surprising lack of research in real-world applications. In this work, radar and camera data are combined on an autonomous shuttle called `WEpod', driving on the public road in Wageningen, The Netherlands. With experiments we show that our method reduces the candidate region margin to 0.2m and reduces the miss rate significantly. Furthermore, our specifically trained Convolutional Neural Network improves the performance by 1.4% over vision-based road user detection, and combined with radars we improve by 7.6%. Finally, with our approach we show a performance of 95.1% on the WEpod while driving on the public road.
Original language | English |
---|---|
Title of host publication | Proceedings of the 15th IAPR International Conference on Machine Vision Applications |
Subtitle of host publication | - MVA2017 |
Editors | Hiroshi Ishikawa, Norimichi Ukita, Hitoshi Habe |
Place of Publication | Piscataway, NJ, USA |
Publisher | IEEE |
Pages | 101-104 |
ISBN (Print) | 978-4-9011-2216-0 |
DOIs | |
Publication status | Published - 2017 |
Event | MVA2017: 15th IAPR International Conference on Machine Vision Applications - Nagoya, Japan Duration: 8 May 2017 → 12 May 2017 |
Conference
Conference | MVA2017: 15th IAPR International Conference on Machine Vision Applications |
---|---|
Country/Territory | Japan |
City | Nagoya |
Period | 8/05/17 → 12/05/17 |
Keywords
- Roads
- Radar Detection
- Vehicle dynamics
- Cameras
- Sensors
- Visualization