Duk-Geun Yun

Test of Global Positioning System-Inertial Measurement Unit Performance for Surveying Road Alignment

In an effort to shift road alignment surveying from manual to automatic methods, moving objects—such as a vehicle on which a Global Positioning System–inertial measurement unit (GPS-IMU) is mounted—are being used to perform surveys and obtain more accurate results. Two road alignment survey methods are compared: an affordably priced microelectromechanical system (MEMS) GPS-IMU and a high-priced fiber-optic–type GPS-IMU. Cross-section angle values from a stationary laser scanner survey and a GPS-IMU survey are compared to determine whether the roll attitude value of GPS-IMU can be used to ensure a more accurate comparison of the cross-section angle. In addition, GPS-IMU performance is compared under various test conditions to survey road alignment, and results indicate that the fiber-optic GPS-IMU is needed to ensure accuracy within the 10% margin of error required for road alignment. In a test conducted to verify whether the roll value of a vehicle can be used as is to survey the cross-section angle of a road, the values from GPS-IMU, drawing, and laser were within a 1% margin of error. Thus, it would be possible to use the roll value obtained from a vehicle as is to survey the road cross-section angle accurately.

A Methodology for Evaluating Vehicle Driving Safety based on the Analysis of Interactions With Roads and Adjacent Vehicles

Traffic accidents can be defined as a physical collision event of vehicles occurred instantaneously when drivers do not perceive the surrounding vehicles and roadway environments properly. Therefore, detecting the high potential events that cause traffic accidents with monitoring the interactions among the surroundings continuously by driver is the prerequisite for prevention the traffic accidents. For the analysis, basic data were collected to analyze interactions using a test vehicle which is equipped the GPS(Global Positioning System)-IMU(Inertial Measurement Unit), camera, radar and RiDAR. From the collected data, highway geometric information and the surrounding traffic situation were analyzed and then safety evaluation algorithm for driving vehicle was developed. In order to detect a dangerous event of interaction with surrounding vehicles, locations and speed data of surrounding vehicles acquired from the radar sensor were used. Using the collected data, the tangent and curve section were divided and the driving safety evaluation algorithm which is considered the highway geometric characteristic were developed. This study also proposed an algorithm that can assess the possibility of collision against surrounding vehicles considering the characteristics of geometric road structure. The methodology proposed in this study is expected to be utilized in the fields of autonomous vehicles in the future since this methodology can assess the driving safety using collectible data from vehicles sensors.