Multi-view driving risk assessment method and device based on network connection environment

Abstract

The invention discloses a multi-view driving risk assessment method and a multi-view driving risk assessment device based on a network connection environment. The method comprises the following steps:step 1, dividing a region to be subjected to risk assessment into a plurality of sub-regions; 2, acquiring first environment sensing information of each sub-region through selected roadside sensing equipment, and judging the confidence coefficient of each sub-region; 3, judging whether the sum of the areas of the sub-regions corresponding to a low-confidence region in the region to be subjected to risk assessment is smaller than a preset ratio of the area of the region to be subjected to risk assessment or not; 4, fusing and splicing the first environmental perception information of the low-confidence region and the second environmental perception information of the same sub-region as the first environmental perception information to obtain an enhanced environmental perception result corresponding to the Alow of the low-confidence region; 5, generating a global environment perception result of the region to be subjected to risk assessment; and 6, evaluating the overall risk of the region to be subjected to risk assessment. According to the invention, the purposes of real-time perception and risk assessment without blind areas can be achieved by using a network connection environment.

Description

technical field [0001] The invention relates to the technical field of networked automatic driving vehicles, in particular to a multi-view driving risk assessment method and device based on a networked environment. Background technique [0002] Networked autonomous driving vehicle technology is one of the trends in future transportation development, which can bring convenience and save time for people to travel. However, as a new thing, its safety and reliability are currently research hotspots in academia and industry. In fact, self-driving vehicles should be able to effectively and accurately assess the safety risks of themselves and the environment in real time, just like skilled human drivers, and take the lowest risk areas or paths to achieve safer purposes. However, there is still a lot of room for improvement in the risk assessment of networked autonomous vehicle technology. [0003] The current risk assessment method generally consists of a risk assessment method b...

AI Technical Summary

Problems solved by technology

However, current methods mostly focus on the computing power and perception of single vehicles for real-time evaluation. In the face of factors such as complex traffic intersections with mixed flow of people and vehicles, there are often problems such as "evaluation blind spots" and insufficient computing power due to occlusion between vehicles.

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