Leveraging Thermal Imaging for Autonomous Driving
| Autor: | Martin von Mohrenschildt, Ash Liu, Saeid Habibi, Ben Miethig |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
020901 industrial engineering & automation Lidar Computer science Radar imaging Real-time computing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 02 engineering and technology Overall performance Pedestrian Sensor fusion Radar detection |
| Zdroj: | 2019 IEEE Transportation Electrification Conference and Expo (ITEC). |
| DOI: | 10.1109/itec.2019.8790493 |
| Popis: | In 2018, vehicles operating in an autonomous mode have been involved in at least five major accidents. Of these, one involved a pedestrian death due to a lack of timely information available to the detection system. Thermal imaging (TI) could have potentially helped with the time of detection of the pedestrian. This paper will argue how TI can provide supplementary information to existing autonomous detection systems to improve their overall performance. It will outline detection considerations for classifying objects in a thermal image which can later be used in sensor fusion applications. A new labelled dataset of thermal images will also be introduced under snowy, overcast, misty, and nighttime driving conditions. A labeled dataset of a golden retriever and a Doberman will be also be introduced. These datasets are publicly available for download. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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