Pedestrian Detection
Description: the detection module is for detecting pedestrians within 20-meter range of the infrastructure. The difference between detected pedestrian coordinate and the ground truth should be within 0.3 m based on the FVE performance requirement.
Current status: the module is able to detect the pedestrians using information of LiDAR point cloud data. By subtracting the raw point data with pre-stored dense background, we are able to get all the foreground points and divide them into different groups using euclidean clustering method. Centroids of each point group are then calculated as the centroid for each pedestrian.
Analysis testing: the unit test for detection module is conducted by assigning human agent to 16 of the pre-measured points. The errors are calculated as the euclidean distance between the groundtruth and detected coordinate. All the testing data are shown below:
| Point No. | Point Coordinate (m) | Detected Coordinate (m) | Error (m) |
| 1 | 2,1 | 2.00,1.00 | <0.0001 |
| 2 | 4,1 | 4.00,1.02 | 0.0200 |
| 3 | 6,1 | 5.99,1.02 | 0.0224 |
| 4 | 10,1 | 10.02,0.99 | 0.0224 |
| 5 | 15,1 | 15,0.98 | 0.0200 |
| 6 | 21,1 | 20.92,1.00 | 0.0800 |
| 7 | 21,3.3 | 20.85,3.34 | 0.1552 |
| 8 | 9,3.8 | 8.98,3.57 | 0.2309 |
| 9 | 8,3.8 | 8.04,3.72 | 0.0894 |
| 10 | 3,8 | 3.08,7.93 | 0.1063 |
| 11 | 3,6 | 3.05,5.94 | 0.0781 |
| 12 | 3,4 | 3.06,3.95 | 0.0781 |
| 13 | 3,2 | 3.03,1.98 | 0.0361 |
| 14 | 0,1 | 0.03,1 | 0.0300 |
| 15 | -7,4 | -6.86,4.09 | 0.1664 |
| 16 | 4,8 | 4.05,7.90 | 0.1118 |
Table 1: Detection Performance Test
The mean of euclidean distance error is 0.0779m which proves that the detection module achieves a decent result and meets the FVE requirement.