Improving Position-Time Trajectory Accuracy in Vehicle Stop-and-Go Scenarios by Using a Mobile Robot as a Testbed

This study sets an example of how mobile robotic vehicles can be used effectively in research on intelligent transportation systems. Especially the stop-and-go mobility seen heavy traffic conditions was simulated with a robot, and is focused to obtain distance-time trajectories more accurately under these conditions. System identification tests platform, whose kinematic model developed, were also carried out, all solutions regarding robot movement obtained. For congested simulation, various points are designated predetermined straight route segment mimic behavior vehicle traffic. Robot obtained different scenarios by using both GPS data through utilization motor encoders. More accurate consistent achieved fusing Extended Kalman Filter. The main contribution this demonstrating number positions improve accuracy estimating robot/vehicle trajectory. paper shows cumulative error predicting reduced as stops increases. example, trajectory estimated for scenario involving five 94% than that case single stop. DOI: 10.61416/ceai.v25i3.8365