An event-processing system alerting analytically to networked vehicles

There are demands for alerts that use the results of intensive data analysis, such as relationships among events generated by many vehicles over a large area. These alerts require data analysis on servers. However, when a server receives massive amounts of data from vehicles, the processing latency increases because of the communication delays between the vehicles and the servers and the increased workload for data analysis on the server. Therefore we need to develop latency-tolerant alert-generating systems with scalable performance. In this paper, we report on a high-speed event-processing system architecture that integrates event processing in the in-vehicle systems with event processing in the servers. The in-vehicle system analyzes the vehicle's sensor data, detects events, and sends packets of the event information to the servers. The server has a stream-processing system, a pre-aggregation system, and a full-data-accumulation system. The stream-processing system receives the packets from the in-vehicle system. The pre-aggregation system creates and updates decision tables for the alerts repeatedly. The alerter of the stream-processing system generates alerts from the table. We implemented a prototype system to generate alerts about obstacles. We tested input data from actual vehicles and a traffic simulator and estimated the vehicles can receive the alerts within 1.2 sec even when the server receives massive data from 120,000 vehicles, which meets the performance requirement for our alert scenarios.