Traffic accident responsibility assessment method and device based on deep learning

Abstract

The invention discloses a traffic accident responsibility assessment method and device based on deep learning. The method comprises the following steps: acquiring driving data of a current car througha car driving recorder, and monitoring car driving through road monitoring equipment; inputting information of cars involved in an accident, and retrieving and obtaining data of the cars involved inthe accident from a database; training a traffic accident responsibility assessment model through the neural network algorithm; and inputting the data of the cars involved in the accident into the traffic accident responsibility assessment model, obtaining an accident responsibility assessment result output by the traffic accident responsibility assessment model, and sending the accident responsibility assessment result to user terminals. The method and device disclosed by the invention has the advantages that centralized management of traffic accident evidences can be achieved, and traffic accident responsibility assessment can be automatically and efficiently carried out; the assessment result is accurate and has a sound legal basis; the assessment speed is high, so that the traffic accident handling efficiency can be greatly improved, and waste of manpower and material resources can be avoided; and evidence analysis and judgment can be carried out in an all-around manner, so that the responsibility assessment result is fair and reasonable.

Description

technical field [0001] The invention belongs to the technical field of traffic accident management, and in particular relates to a deep learning-based traffic accident responsibility assessment method and device. Background technique [0002] With the rapid growth of my country's economy, public transportation infrastructure such as expressways and expressways has also been rapidly developed. While bringing efficiency, speed and convenience to people's lives, it also brings negative effects such as increased traffic accidents. And with the economic and social development, the increase of car ownership and road mileage, this situation will become more and more severe. [0003] At present, when judging the responsibility of traffic accidents, the evidence is scattered and the correlation is poor, and effective evidence cannot be obtained quickly. At present, when dividing the responsibilities of traffic accidents, usually through surveillance video or photography, the accident...

AI Technical Summary

Problems solved by technology

[0003] At present, when judging the responsibility of traffic accidents, the evidence is scattered and the correlation is poor, and it is impossible to quickly obtain effective evidence

At present, when dividing the responsibilities of traffic accidents, usually through surveillance video or photography, the accident responsibility assessment is carried out by appraisers to determine the responsible party; however, this method consumes a lot of manpower and material resources, and the work efficiency is extremely low; and in the responsibility In the process of judging, appraisers need to make quick judgments based on limited evidence, and it is impossible to conduct comprehensive evidence analysis and judgment, which greatly affects the judgment results and cannot achieve true fairness and reasonableness, often resulting in misjudgment

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