Bayesian path planning device and method based on GPS detection

Abstract

The invention relates to a Bayes path planning device and method based on GPS (Global Positioning System) detection. The method provided by the invention is a priori path finding method for simulating a driver by adopting a method for evaluating priori information by utilizing priori knowledge. The device provided by the invention contains a priori deduction method of the driver into the path planning device by utilizing a Bayes classifier, and the device is the path planning method formed after the patter recognition processing is carried out on a road network based on the classifier. In theinvention, the Bayes classifier is constructed to form the prior recognition of the road network by utilizing the priori deduction of the driver on real-time traffic conditions of the road network; the path planning device and method formed based on prior recognition can respectively give different optimal paths according to the priori knowledge in rush hours and in non-rush hours at different traffic time intervals. The device and method provided by the invention have good practicality and can be flexibly integrated into various navigation systems based on road traffic.

Description

Technical field: [0001] The invention relates to a path planning device and method suitable for use based on a priori inference in actual traffic conditions, in particular to a GPS detection-based Bayesian path planning device and method. Background technique: [0002] At present, the Global Positioning System (GPS) has been widely used as a positioning and navigation device on various vehicles, such as vehicle navigation systems, vehicle positioning monitoring systems, floating vehicle systems in transportation departments, and so on. On the other hand, the optimal path obtained by the path optimization methods in our various navigation systems is often the shortest path in terms of absolute time or length measurement in actual use. However, due to the random nature of the real-time traffic conditions of the road, such an optimal path in the metric sense is often not so consistent with the driver's prior knowledge, and such an optimal path is often not the optimal path ...

AI Technical Summary

Problems solved by technology

The method of assigning empirical information to road weights for path planning, in the case of real-time traffic information, has the same problem as the traditional optimal path method, and it cannot avoid the randomness of real-time traffic information

[0006] From the perspective of the existing path planning technical methods analyzed above, these optimal path methods that normalize certain empirical information such as speed, length, and transit time to the measurement of road weights do not really integrate road network A priori inference method for drivers who are very familiar with traffic conditions is incorporated into the route planning system

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