In this thesis, we have proposed a Video-Assisted Inter-Vehicle Positioning (VIP) system. The system has been designed to integrate sensed data extracted from driving video logger with image processing and share to information among vehicles to improve the accuracy of cooperative positioning. Simulation results have shown that our approach can achieve 10 and 30 percent of improvement in the position accuracy when only half of vehicles were fully equipped with different sensing ranges, and improve the accuracy by factor of 40 percent (within 3m) if all vehicles were fully equipped in the best condition. In the future works, it is essential to evaluate the performance under different types of driving video logger and image professor (or software), and to consider how to achieve cooperative positioning in the city environments, which could be much more complex compared with highway scenarios. Besides, it is interested to evaluate the performance if the information obtained by a driving video logger, e.g. lane index, distance, license plate number, are not always accuracy.
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