IPS300+: a Challenging multi-modal data sets for Intersection Perception System

Due to high complexity and occlusion, insufficient perception in the crowded urban intersection can be a serious safety risk for both human drivers and autonomous algorithms, whereas CVIS (Cooperative Vehicle Infrastructure System) is a proposed solution for full-participants perception under this scenario. However, the research on roadside multi-modal perception is still in its infancy, and there is no open-source data sets for such scene. Accordingly, this paper fills the gap. Through an IPS (Intersection Perception System) installed at the diagonal of the intersection, this paper proposes a high-quality multi-modal data sets for the intersection perception task. The center of the experimental intersection covers an area of 3000m 2, and the extended distance reaches 300m, which is typical for CVIS. The first batch of open-source data includes 14198 frames, and each frame has an average of 319. 84 labels, which is 9. 6 times larger than the most crowded data sets (H3D data sets in 2019) by now. Our data sets is available at: http://www.openmpd.com/column/IPS300.

Authors

• Huanan Wang
• Xinyu Zhang 0001
• Zhiwei Li
• Jun Li 0082
• Kun Wang
• Zhu Lei
• Haibing Ren

Keywords

• Automation
• Safety
• Complexity theory
• IP networks
• Task analysis
• Open source software
• Vehicles
• Perceptual System
• Challenging Dataset
• Multimodal Dataset
• Open Data
• Perceptual Task
• Pedestrian
• Point Cloud
• Bounding Box
• Detection Task
• Autonomous Vehicles
• Traffic Flow
• Labeled Data
• Roll Angle
• Yaw Angle
• External Parameters
• Tracking Task
• Time Synchronization
• Advanced Driver Assistance Systems
• 3D Detection
• Roadside Units
• Original Equipment Manufacturers
• 3D Bounding Box
• Point Cloud Features
• Raw Point Cloud
• Target Detection Task
• Single Frame
• Sensor Parameters
• Traffic Accidents