1. Ring Road Experiment For Driving Safety Analysis Beijing, 2011 Spring
POSS , Peking University
Ring Road Experiment For Driving Safety Analysis Beijing, 2011 Spring

2. Experiment Objective Objectives
Collect multi-modal sensing data in ring-road/free-way driving scenario
Lasers sensing other traffic participants in vicinity and moving obstacle detection and tracking
GPS data for host vehicle positioning and trajectory recording
Panoramic camera for ground truth
Driving behavior analysis
The driving behavior analysis includes several meaning: To learn from human driver’s driving behavior in order to refine the trajectories or motion planning strategies; to investigate the effect of other traffic participants on our host vehicle so that to give risk assessment that is proper to describe the potential danger around our robot. In addition we can study the relation between human driving strategy and the vicinity traffic to learn some decision making progress in behavior layer of the motion planner, such as the switching between different strategies in different situation.(Lane changing/overtaking/car following etc.)

3. Sensor Setting LadyBug GPS/IMU LMS URG
This photo is not taken when we did the experiment, so the sensor setting is illustrated with virtual sensors. In our experiment we only use HAYUKO URG laser(small)*3, a LMS291, GPS/IMU and the ladybug.
URG

4. Sensing Range Coverage
SICK LMS
LadyBug Panoramic camera RM FM FL FR
HAKUYO URG Lasers
Laser Coverage
Front Middle
SICK LMS
Front Left(Right)
HAKUYO URG
Rear Middle
The gray circle represents the ladybug is 360°, the blue area is covered with urg and the red one is LMS. In fact the LMS at front middle is used only to obtain a further sensing range and in case the URG is not available. The URGs have already covered the proposed sensing area.

5. Experiment Design Experiment environment Experiment time
3rd ring road in Beijing
Whole length: 96.53km
Design speed: 80km/h
Experiment time
2011/05/30 20:38—22:50(UTC+8)
The experiment is carried at night which is not the rush hour.

6. Experiment Design: Beijing 3rd Ring Road

7. Data Example: Lasers & Panoramic Camera
4 Lasers + GPS
Panoramic Camera

8. Data Example: GPS Data
GPS failure
GPS points
GPS failure

9. Data Example: GPS Data
Large GPS error

10. Data Processing Steps BM Laser Calibration Laser Synchronization
Laser calibration Schematic
Steps
Laser Calibration
Laser Synchronization
Laser-GPS interpolation BM FM FL FR
Box
This slide shows the laser calibration procedure. By looking at the same box at different position around the car using multi lasers, we can calibrate the relationship between these lasers.

11. Data Analysis Generating Occupancy Grid Map Using GPS + laser data
Including host vehicle trajectory
Including static obstacles

12. Data Analysis: Occupancy Grid Map Generation
This is a 1:12 long video to replay part of our exp on OGM, the “Lane changing” behavior is distinguished from the “overtaking” behavior since the trajectory didn’t return to it’s former line.

13. Data Analysis SLAM with Moving Obstacle Detection and Tracking
Yellow: host vehicle trajectory
Orange: moving objects and their trajectories
Green: static obstacles
Cyan: undecided

14. Data Analysis GPS Data Interpolation Why need interpolation?
Yaw data from GPS is not always available
To get pos\yaw\curvature between GPS data points
Interpolation using cubic spline
Guarantee C2 continuity trajectory (with continuous yaw and curvature) to satisfy nonholonomic constraints

15. Raw GPS Data with yaw error
The two videos show the necessity of interpolation. There are two key frames in the upper video, while no key frame in the bottom one which just show it has improved the yaw data at the same position with the key frames in the upper video by interpolation.
After interpolation

16. Future Work
Generating trajectory set from real GPS trajectory of host vehicle
Extract some features(curvature, length etc) from GPS trajectory
Learning human driving behavior from real data with these features and comparing the difference between motion planning results and the real trajectory of human driving vehicle
Try to find some driving safety indices by learning from the state of moving obstacles around the host vehicle

17. Thank You!