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Volume 7 Issue 2
Mar.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Chen Sun, Jean M. Uwabeza Vianney, Ying Li, Long Chen, Li Li, Fei-Yue Wang, Amir Khajepour and Dongpu Cao, "Proximity Based Automatic Data Annotation for Autonomous Driving," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 395-404, Mar. 2020. doi: 10.1109/JAS.2020.1003033
Citation: Chen Sun, Jean M. Uwabeza Vianney, Ying Li, Long Chen, Li Li, Fei-Yue Wang, Amir Khajepour and Dongpu Cao, "Proximity Based Automatic Data Annotation for Autonomous Driving," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 395-404, Mar. 2020. doi: 10.1109/JAS.2020.1003033

Proximity Based Automatic Data Annotation for Autonomous Driving

doi: 10.1109/JAS.2020.1003033
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  • The recent development in autonomous driving involves high-level computer vision and detailed road scene understanding. Today, most autonomous vehicles employ expensive high quality sensor-set such as light detection and ranging (LIDAR) and HD maps with high level annotations. In this paper, we propose a scalable and affordable data collection and annotation framework, image-to-map annotation proximity (I2MAP), for affordance learning in autonomous driving applications. We provide a new driving dataset using our proposed framework for driving scene affordance learning by calibrating the data samples with available tags from online database such as open street map (OSM). Our benchmark consists of 40 000 images with more than 40 affordance labels under various day time and weather even with very challenging heavy snow. We implemented sample advanced driver-assistance systems (ADAS) functions by training our data with neural networks (NN) and cross-validate the results on benchmarks like KITTI and BDD100K, which indicate the effectiveness of our framework and training models.


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