ParallelEye-CS: A New Dataset of Synthetic Images for Testing the Visual Intelligence of Intelligent Vehicles

Virtual simulation testing is becoming indispensable for the intelligence testing of intelligent vehicles. However, even the most advanced simulation software provides rather limited test conditions. In the long run, intelligent vehicles are expected to work at SAE (Society of Automotive Engineers)...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2019-10, Vol.68 (10), p.9619-9631
Main Authors: Li, Xuan, Wang, Yutong, Yan, Lan, Wang, Kunfeng, Deng, Fang, Wang, Fei-Yue
Format: Article
Language:English
Subjects:
Algorithms
Automation
Automobiles
Automotive engineering
Computer simulation
Datasets
Intelligence
intelligence testing
Intelligent vehicles
Labels
Object detection
Object recognition
Roads
Simulation
Synthetic data
synthetic images
Task analysis
Testing
Virtual reality
virtual simulation
visual intelligence
Visual perception
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Summary:Virtual simulation testing is becoming indispensable for the intelligence testing of intelligent vehicles. However, even the most advanced simulation software provides rather limited test conditions. In the long run, intelligent vehicles are expected to work at SAE (Society of Automotive Engineers) level 4 or level 5. Researchers should make full use of virtual simulation scenarios to test the visual intelligence algorithms of intelligent vehicles under various imaging conditions. In this paper, we create realistic artificial scenes to simulate the self-driving scenarios, and collect a dataset of synthetic images from the virtual driving scenes, named "ParallelEye-CS". In the artificial scenes, we can flexibly change environmental conditions and automatically acquire accurate and diverse ground-truth labels. As a result, ParallelEye-CS has six ground-truth labels and includes twenty types of tests, which are divided into normal, environmental, and difficult tasks. Furthermore, we utilize ParallelEye-CS in combination with other publicly available datasets to conduct experiments for visual object detection. The experimental results indicate that: 1) object detection algorithms of intelligent vehicles can be tested under various scenario challenges; 2) mixed dataset can improve the accuracy of object detection algorithms, but domain shift is a serious issue worthy of attention.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2019.2936227