Yuan Sun. Advisor: Dr. Hao Xu. University of Nevada, Reno

Transcription

1 Yuan Sun Advisor: Dr. Hao Xu Center of Advanced Transportation Education and Research Intelligent Transportation System Lab Nov 11, 2016

2 1. Background. Outlines 2. Introduction of Lidar Sensor. 3. Current researches of pedestrian and vehicle detection. 4. Segmentation and Tracking algorithm. 5. Speed estimation results. 6. Conclusions. 2

3 Current Sensors Inductive loop detection Video vehicle detection Bluetooth detection Cellphone detection Figure 1 Traditional Traffic Data Collecting Sensors 3

4 Lidar Sensor Application Figure 2 Applications of Lidar Sensor 4

5 Lidar Sensor Data Clouds Visualization Figure 3 Visualization of real-time lidar scanning on all-terrain vehicle(atv) 5

6 Current researches on Pedestrian and Vehicle Detection and Tracking Figure 4 HOG features(left) and Lidar Features(Right) 6

7 Lidar Data Clouds Q: How can we estimate vehicle speed from Lidar Data? Figure 5 Raw Lidar Data at the intersection of Talus and Virginia Street. 7

8 Cloud Data transformation Figure 6. Cloud Data Transformation From Excel To PCD type 8

9 Set Region Of Interest(ROI) Figure 7. ROI setting Before(Left) and After (Right) 9

10 Statistical Outlier Removal Figure 8. SOR performed Before(Left) and After (Right) 10

11 Planar Model Segmentation Figure 9. RANSAC method performed Before(Left) and After (Right) 11

12 Euclidean Cluster Segmentation Step1 KD Tree definition Figure 10. KD-tree real object(left) and abstract (Right) 12

13 Euclidean Cluster Segmentation Step2 Clustering Figure 11. clustering results 13

14 Tracking Through Vehicle and Turn Vehicle To test the tracking algorithm, Frame 1621-Frame 1630 was selected as the test set. Through Speed Turn Speed Through Speed 2 Turn Speed 2 Through Speed 3 Turn Speed

15 Tracking Through Vehicle and Turn Vehicle 1. The 2 and 3 in the name column indicate the sampling second difference as 0.2 seconds, 0.3 seconds respectively. 2. The Speed fluctuation at 0.1 second is high, so it was regarded as no stable. 3. Further examining the clouds indicate that there are some unscreened ground reflection, thus the Z value in ROI step need to be limited in a range. 15

16 Tracking Through Vehicle and Turn Vehicle The speed now is stabilized, and it is estimated the through speed is 39.4 mph, and the turn speed is decreased from 21.1 mph to 17.4 mph in 1 second. Through Speed Turn Speed Through Speed 2 Turn Speed 2 Through Speed 3 Turn Speed

17 Conclusions 1. Using Lidar Sensor alone to track vehicle and estimate the speed is possible. 2. The segmentation and clustering is the key steps in algorithm. More approaches need to be tested. 3. Data logger equipped vehicle is needed if the precise speed is desired. 4. This research enables the possibilities of automatically broadcasting information to the connected vehicles systems. 17

18 References [1] Premebida, C., G. Monteiro, U. Nunes, and P. Peixoto. A lidar and vision-based approach for pedestrian and vehicle detection and tracking.in 2007 IEEE Intelligent Transportation Systems Conference, IEEE, pp [2] Premebida, C., O. Ludwig, and U. Nunes. LIDAR and vision based pedestrian detection system. Journal of Field Robotics, Vol. 26, No. 9, 2009, pp [3] Szarvas, M., U. Sakai, and J. Ogata. Real-time pedestrian detection using LIDAR and convolutional neural networks.in 2006 IEEE Intelligent Vehicles Symposium, IEEE, pp [4] Douillard, B., J. Underwood, N. Kuntz, V. Vlaskine, A. Quadros, P. Morton, and A. Frenkel. On the segmentation of 3D LIDAR point clouds.in Robotics and Automation (ICRA), 2011 IEEE International Conference on, IEEE, pp [5] Moosmann, F., O. Pink, and C. Stiller. Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion.in Intelligent Vehicles Symposium, 2009 IEEE, IEEE, pp [6] Himmelsbach, M., A. Müller, T. Luettel, and H.-J. Wünsche. LIDAR-based 3D object perception.in Proceedings of 1st international workshop on cognition for technical systems, No. 1,