Graduation Year


Document Type




Degree Granting Department

Civil and Environmental Engineering

Major Professor

Jian Lu


GPS, IMU, PID Controller, Simulation, Trajectory Tracking


This research focuses primary on recognition of lane-change behaviors using support vector machines (SVMs). Previous research and statistical results show that the vast majority of motor vehicle accidents are caused by driver behavior and errors. Therefore, the interpretation and evaluation of driver behavior is important for road safety analysis and improvement. The main limit to understanding driver behavior is the data availability. In particular, a full-scale lane-change data set is difficult to collect in a real traffic environment because of the safety and cost issues. Considering the data demands of the recognition model development and the obstacles of field data collection, data were collected from two aspects: simulation data and the field data. To obtain field data, an in-vehicle data recorder (IVDR) that integrates a Global Positioning System (GPS) and Inertial Measurement Unit (IMU) are developed to collect data on speed, position, attitude, acceleration, etc. To obtain simulation data, a lane-change simulation with a speed controller and a trajectory tracking controller with preview ability were developed, and sufficient lane-change data were generated. Proportional-Integral-Derivative (PID) control is applied to the speed controller and trajectory tracking controller.

Simulation data were divided into two classes: dual lane-change data and single lane-change data; field data were further divided as single lane-change and non-lane-change data. Two-class and three-class classification SVM model are trained by simulation data and field data, and the model parameters were optimized by Genetic Algorithm (GA). A radial basis function and polynomial kernel functions were found that suitable for this recognition task. The recognition results indicate that, the SVM model trained by simulation data and non-lane-change data can correctly classify up to 85 percent of single lane-change field data.