Using Kalman Filtering to Improve a Low-Cost GPS-Based Collision Warning System for Vehicle Convoys

Open Access
Chaves, Stephen Marc
Graduate Program:
Mechanical Engineering
Master of Science
Document Type:
Master Thesis
Date of Defense:
Committee Members:
  • Sean N Brennan, Thesis Advisor
  • Kalman filter
  • collision warning
  • GPS
  • vehicle convoys
This thesis addresses the concern of rear-end collisions in vehicle convoys, specifically in military applications. The goal of this thesis is to provide a framework for a low-cost, easily-incorporated collision warning system to be used in vehicle convoys. Such a system can have immediate and substantial impact on saving lives. Military convoys in particular often operate in dusty conditions or adverse weather, and existing radar-based collision detection systems can have degraded performance in these situations. This thesis proposes a collision warning system that is based on a GPS receiver and wireless network for transferring data between convoy vehicles. In order to meet the accuracy requirements outlined by others for successful collision avoidance, this work focuses on improving the vehicle velocity measurements which are not sufficient by themselves. The GPS receiver measurements are fused with readings from a MEMS accelerometer in a Kalman filter. The Kalman filter greatly increases the performance of the proposed collision warning system with only a slight increase in cost. The results of this thesis show that with this type of data fusion, a low-cost GPS-based collision warning system is both feasible in terms of meeting accuracy requirements of collision avoidance systems, and realizable by demonstration through numerous field experiments conducted as part of this thesis.