Vehicle danger early warning system and method based on vehicle cooperation

Abstract

The invention discloses a vehicle danger early warning system based on vehicle cooperation. A vehicle state acquisition module and a driving intention determination module are arranged in a vehicle, the vehicle state acquisition module and the driving intention determination module output induction signals to a dedicated short range communication module, the short range communication module transmits acquired signals to a vehicle danger early warning module, the vehicle danger early warning module transmits early warning signals to a human-machine interaction interface and transmits control signals to a vehicle control module, and information interaction between the dedicated short range communication module and the vehicle equipped with the vehicle danger early warning system is performed. According to the system, vehicle data is acquired via the dedicated short range communication module, whether there are potential risks is determined according to the vehicle data, compared with the general vehicle danger early warning system, the vehicle danger early warning system and the method are more effective, potential risks can be predicted, drivers are reminded in time, the vehicle can be effectively controlled, and traffic accidents are prevented.

Description

technical field [0001] The invention relates to the technical field of intelligent transportation, in particular to a vehicle danger warning system and method based on vehicle-vehicle coordination. Background technique [0002] According to statistics from the Traffic Management Bureau of the Ministry of Public Security, from January to October 2014, a total of 426,378 road traffic accidents occurred across the country, resulting in 87,218 deaths, 391,752 injuries, and direct property losses of 2.02 billion yuan. Compared with the same period in 2013, the number of accidents decreased by 139,097, a drop of 24.6%. In recent years, the number of traffic accident deaths in my country has shown a slight upward trend year by year, which is closely related to the continuous increase in the number of motor vehicles in my country. According to relevant data, the number of motor vehicle drivers in my country at the end of 2014 exceeded a historic 300 million. The rapid use of motor ...

AI Technical Summary

Problems solved by technology

[0004] The distance measurement accuracy of the radar is high, the data processing requirements are low, and the robustness to the weather is good, but its reliability depends on the distance and reflectivity of the target, and often cannot detect short and transparent obstacles, because the information is relatively simple , it is also difficult to identify obstacles

[0005] The application of laser ranging has limitations. In fact, due to the fact that the laser is susceptible to natural weather factors, the cleanliness of the laser emitting and receiving windows, the surface roughness of the detected vehicle and the obstacle itself, pollution, and the bumps of the car during driving, etc. Influenced by various aspects, the detection distance, detection accuracy and accuracy of the laser anti-collision radar are affected to a certain extent.

[0006] Ultrasonic detection distance is short, greatly affected by weather conditions

The propagation speed in the air is small and changes with temperature and weather factors, and the propagation speed is different under different weather conditions; on the other hand, for long-distance obstacles, the reflected wave is too weak due to a lot of interference and influence , the sensitivity decreases, and it is easy to cause ranging errors

[0007] Infrared ranging is not difficult technically, and the cost of the ranging system is low, but it still cannot meet the requirements of car collision avoidance in bad weather and long-distance detection, especially on highways, during high-speed driving. , there will be a certain degree of ranging error

[0008] Machine vision has the advantages of rich detection information, and has a good ability to analyze the shape and texture features of objects, but it is greatly affected by the weather and the environment, and it is helpless for obstacles with similar colors and background colors.

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