Cumulative intelligence gathering for smart vehicles

Latest advances in sensor and communication technologies and computational intelligence will allow vehicles to observe their surroundings, process the collected information and communicate it with other vehicles, e.g., vehicles can sense adverse road conditions, experience trafic congestion, or detect accidents, and then adjust vehicle control. We investigate the problem of effective intelligence gathering in the perspectives of both communications and data fusion. The target system should have high processing and transmission accuracy for the gathered information as well as low communication load requirement. Assuming a binary source, we determine the theoretical bound for the error probability of gathered information and present a way of communicating the observation from individual vehicles to the destination. Compared to the flooding method, our model reduces the communication load by approximately N/log2 N times, with N being the total number of the vehicles, without decreasing the accuracy of the gathered information.