Vehicle collision early warning method based on analytic hierarchy process and grey fuzziness

Abstract

The invention discloses a vehicle anti-collision early warning method based on an analytic hierarchy process and grey fuzziness. The problem that the safety distance is not accurate in the prior art is mainly solved. The method comprises the implementation steps that first, an analytic hierarchy process structure of a vehicle safety distance is established, and the weight vectors of all levels of influence factors are calculated; second, a grey fuzzy method is used for calculating the evaluation value of an index level influence factor; third, the accelerated speed of a vehicle and the reaction time of a driver are corrected according to the evaluation value of the index level influence factor; fourth, a kinematic formula of the vehicle is used for calculating the absolute safety distance and the relative safety distance of the vehicle; fifth, early warning levels are divided according to the absolute safety distance, the relative safety distance and the vehicle distance detected by a sensor. According to the vehicle anti-collision early warning method based on the analytic hierarchy process and the grey fuzziness, the influences of human factors, vehicle factors, road factors and environment factors on the vehicle safety distance are comprehensively considered, an anti-collision early warning of the vehicle is given out, and the accuracy of the vehicle safety distance and the safety of vehicle traveling are improved.

Description

technical field [0001] The invention belongs to the field of communication technology, and mainly relates to a multi-level early warning method for ensuring driving safety in vehicle ad hoc networks VANETs, ​​and is applicable to medium and high-speed motion scenes such as expressways and suburbs. Background technique [0002] With the increase of vehicle ownership and the increasingly complex road conditions, traffic accidents occur frequently. How to avoid accidents is an important research direction in the field of VANETs. Therefore, the vehicle collision warning system CWS has attracted more and more attention. Daimler-Benz's research shows that an early warning of 0.5s can reduce the accident rate caused by rear-end collisions and cross collisions by more than 50%. [0003] In the document "A Stochastic Model for Chain Collisions of Vehicles Equipped With Vehicular Communications", Joan Garcia-Haro et al. proposed a minimum inter-vehicle distance algorithm for safe driv...

AI Technical Summary

Problems solved by technology

Although the article divides the warning levels, there is no further explanation, nor does it take into account the impact of the driver's state on vehicle safety, so the calculated safety distance is too large

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