Lane-changing detection is necessary for accurate
positioning, to allow vehicle navigation system to generate more
specific path planning. Lane-changing detection method in this
paper is more of a deterministic task, proposed based on curve
analysis obtained from visual odometry. From the visual
odometry trajectory, we have the estimation of vehicle
lateral/longitudinal position, yaw, and speed. We also used the
road lane information from digital map provided by
OpenStreetMap to narrow the lane-changing event possibility.
The analysis is conducted on sequences from KITTI dataset that
contains lane-changing scenarios to study the potential of lanechanging detection by using visual odometry trajectory curve.
Cumulative sum and curve fitting methods were utilized for the
lane-changing detection from visual odometry curve. The
detection was conducted on several visual odometry approaches
for comparison and system feasibility. Our analysis shows that
trajectory generated by visual odometry is highly potential for a
low-cost and effective lane-changing detection with 90.9%
precision and 93.8% recall accuracy to complement more
accurate routing service and safety application in Advanced
Driver Assistance System
