Driving risk section recognition and warning system and method based on networked vehicles

Abstract

The invention discloses a driving risk section recognition and warning system and method based on networked vehicles. The method comprises: acquiring acceleration data of a networked vehicle to perform risk analysis, determining a vehicle motion acceleration threshold capable of representing driving risk, using a space clustering process to identify a risk section based on the locational information of the networked vehicle exceeding the threshold in conjunction with a map, and generating a road risk map cloud terminal; acquiring driving data of a target vehicle, comparing the driving data with a cloud driving risk map, extracting current acceleration data of the vehicle when the target vehicle is driven into the risk section, and if current driving acceleration is greater than a risk acceleration trigger value, generating corresponding warning information and sending to a vehicular display device of the target vehicle or an external intelligent cellphone, and giving a warning to a driver in conjunction with a navigation map. The driving risk sections are recognized and warned on in real time, a driver has better sensing and handling for driving risks, and traffic accident rate isdecreased.

Description

Technical field [0001] The invention relates to the technology of the Internet of Vehicles, and in particular to a system and method for identifying and warning road sections based on connected vehicles. Background technique [0002] With the accelerated pace of economic construction, the number of vehicles and traffic in major cities have risen sharply. With the continuous improvement of the road network, the traffic has become more convenient, but at the same time it also brings huge safety risks. The frequent occurrence of traffic accidents is harmful to the people. The safety of life and property poses a major threat. Taking early warning measures before a traffic accident occurs is an effective measure to reduce the rate of vehicle accidents. [0003] The current research on reducing traffic accidents mainly starts from accident black spots. Traffic accidents occur more frequently in certain locations, such as urban intersections, locations with imperfect road conditions, et...

AI Technical Summary

Problems solved by technology

However, only static information is considered in the warning process based on road black spots, and targeted warnings are not made in combination with specific driving conditions.

Unsatisfactory in terms of timely and efficient early warning

Secondly, in some cases, although driving states such as rapid acceleration and rapid deceleration do not currently lead to accidents, they are also potential causes of accidents. If the environment changes slightly, it is very likely to turn into a traffic accident

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