Unmanned System Safety Decision-Making Support: Analysis and Assessment of Road Traffic Accidents

Traffic accidents occurred frequently on roads, which bring huge losses to society. The purpose of this article is to extract the important influence factors of traffic accidents, reduce the probability of road traffic accidents, and support the basis for the decision-making of unmanned vehicles. For all this, an improved artificial neural network method is proposed to predict and analyze the severity of vehicle accidents. The impact of different factors, such as road, weather, road surface, time, etc., on the traffic accident is huge. Hence, the factors of traffic accidents are quantified and the redundancy between factors is reduced. To obtain the relationship between influence factors and severity, a multiple-input and multiple-output neural network prediction model is constructed. The hyperparameters of the network model are optimized, and the model prediction efficiency is improved. Besides, the correlation between factors and severity is also calculated, through which the impact of factors on the accuracy of the accident model is explored. To verify the validity of the method, the accuracy of the proposed method is counted and analyzed based on the data of more than 10 000 traffic accidents. The results show that the accuracy of the proposed method is higher than other traditional methods and model has high stability. The findings provide effective support to improve safety for unmanned system.

Automated summary

This article proposes an improved artificial neural network method to predict and analyze the severity of vehicle accidents, aiming to reduce the probability of road traffic accidents. By constructing a multiple-input and multiple-output neural network prediction model and optimizing the hyperparameters of the network model, the efficiency of the model prediction is improved. Accuracy calculations and analysis based on a large amount of traffic accident data show that the proposed method has higher accuracy and stability.