Vehicle collision warning system and method based on iga-bp neural network in the environment of Internet of Vehicles

Abstract

The invention discloses a vehicle collision early warning system and method based on IGA-BP neural network in the Internet of Vehicles environment. In the system: roadside equipment is used to collect vehicle driving information and transmit it to the cloud for neural network training, and the cloud will complete the training The final neural network is transmitted to the vehicle through the roadside equipment; the information interaction module is used to perceive the driving information of the vehicle and other vehicles in real time, and the IGA‑BP neural network information; and publish the driving information of the vehicle and forward the driving information of other vehicles ; The risk monitoring module performs calculation and analysis based on the driving information and the IGA-BP neural network model, and makes a judgment on whether there is a collision risk; the control module is used to perform braking and warning control based on the calculation results of the risk detection module. The invention proposes a more sensitive and accurate vehicle collision early warning method, which can adapt to changes in different external conditions, and has higher computing speed, safety and accuracy.

Description

technical field [0001] The invention relates to the technical field of the Internet of Vehicles and neural network, in particular to a vehicle collision warning system and method based on the IGA-BP neural network in the Internet of Vehicles environment. Background technique [0002] The Internet of Vehicles technology is the application of the Internet of Things in the field of intelligent transportation. It is based on the intra-vehicle network, the inter-vehicle network and the vehicle-mounted ad hoc network. Wireless communication and information interaction, realizing the integrated network of intelligent management and control of traffic. Its characteristics can be summarized as comprehensive perception, reliable transmission and intelligent processing. Such as figure 1 As shown, the vehicle perceives the running status information through the on-board sensor, and transmits the information to other vehicles through the link path between the vehicle and the vehicle, a...

AI Technical Summary

Problems solved by technology

In the calculation, it is necessary to set the driver’s reaction time and safety buffer distance based on experience, but the complex road environment and the driver’s driving characteristics are difficult to express through a few simple parameters and linear relationships. If these parameters are not set properly, it will affect Method security and user experience

At the same time, although the existing collision warning methods based on neural networks overcome the limitations of parameter deterministic methods, these methods cannot consider the influence of external factors such as weather and road conditions, and the neural network has a slow convergence speed and is easy to fall into local extremum Shortcomings

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