Intersection collision-avoiding method based on dynamic Bayes network

Abstract

The invention discloses an intersection collision-avoiding method based on a dynamic Bayes network, and mainly solves the problems that an existing algorithm is not well adapted to the complex road layout of an intersection, a large amount of data processing is needed, and the calculation complexity is high and the time complexity is high. The method comprises the steps of: 1) determining vehicle states, road conditions and driver behavior information, and adopting the dynamic Bayes network to carry out modeling on vehicle state evolution; 2) determining the safe driving behavior of a target vehicle according to the current environment conditions; and 3) deriving the intention behavior of the driver at the intersection, carrying out risk estimation based on comparison between the safe driving behavior and the intention behavior, and when potential risks are detected, taking different measures according to the practical condition for avoiding a collision. According to the invention, the complex vehicle track prediction process is avoided, the calculation amount is reduced, the vehicle collision in other scenes can be flexibly avoided, and the method can be applied to an intelligent traffic system.

Description

technical field [0001] The invention belongs to the field of traffic information and control, and further relates to a road intersection collision warning and avoidance method in an intelligent traffic system, which can be used in the intelligent traffic system. Background technique [0002] As we all know, traffic accidents will bring huge losses to people's lives, properties and traffic efficiency. In order to reduce traffic accidents, researchers have designed a collision avoidance system based on radar, sensing, video and communication technologies to avoid vehicle collisions. Most of the technical means consider avoiding the overlapping of vehicle positions through trajectory prediction, which puts high demands on the accuracy indicators of various technologies. [0003] Traffic accidents at intersections have a particularly important impact on the performance of the traffic road network, and the movement of vehicles at intersections is complex, which not only involves ...

AI Technical Summary

Problems solved by technology

For example, researchers G.S.Aoude and others use neural networks to learn a large amount of data to predict collision events, (G.S.Aoude, V.R. Desaraju, L.H.Stephens, and J.P. How, "Driver behavior classification at intersections and validation on large naturalistic dataset," IEEE Transactions on Intelligent Transportation Systems, vol.13, no.2, pp.724–736,2012.), but this approach will result in a large computational overhead

[0007] The above-mentioned first two types of existing technologies unilaterally consider the geographic location information of vehicles, and cannot deal with the collision avoidance problem at intersections well.

Although the third type of technology comprehensively considers the impact of vehicle location information and driver status on vehicle collisions at intersections, it requires a complex calculation process and brings a lot of computational overhead.

In addition, the above-mentioned existing technologies are all based on vehicle trajectory prediction, which requires a large amount of data processing, which will lead to high computational complexity and time complexity.

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