Abnormal driving behavior online identification method based on Encoder-Decoder attention network and LSTM

Abstract

The invention discloses an abnormal driving behavior online identification method based on an Encoder-Decoder attention network and an LSTM (Long Short Term Memory). The method is composed of three main modules, namely an encoder-decoder based on LSTM, an attention mechanism and a classifier based on SVM, and comprises the steps of input encoding, attention learning, feature decoding, sequence reconstruction, residual calculation and driving behavior classification. According to the method, on the basis of mobile phone multi-sensor fusion data, on the basis of driving behavior data characteristics and behavior pattern analysis, an Encoder-Decoder deep learning model, an Attention attention mechanism and an SVM classification model are fused to recognize abnormal driving behaviors. The method has the advantages of being easy in data acquisition, non-intrusive, low in cost and the like, not only considers the time correlation of the driving behaviors, but also considers the difference of different moments, can perform online identification on abnormal driving behaviors in an end-to-end mode, can provide a method basis for driving behavior evaluation and safety early warning, and has a good application prospect. The method has significant meaning for intelligent driving system design and traffic safety decision making.

Description

technical field [0001] The invention belongs to the driving behavior recognition technology, in particular to an online recognition method for abnormal driving behavior based on an Encoder-Decoder attention network and LSTM. Background technique [0002] With the development of social economy, the number of motor vehicles has increased rapidly. While vehicles bring convenience to people, they also bring serious hidden dangers to traffic safety. According to data from the National Bureau of Statistics, a total of 247,646 traffic accidents occurred in my country in 2019, resulting in 62,763 deaths, 256,101 injuries, and direct property losses as high as 1,346.18 million yuan. More than 90% of traffic accidents are related to drivers' driving behavior. The main reason is mostly inappropriate driving behaviors such as sudden acceleration and deceleration. Therefore, online analysis and identification of abnormal driving behaviors is an important way to prevent traffic accidents...

AI Technical Summary

Problems solved by technology

However, image data is easily affected by lighting and other factors, and the application scenarios are limited, and the video data volume is huge, which requires a lot of computing resources

Driving behavior analysis based on sensor data usually relies on special sensors and requires the installation of a large number of special on-board equipment for the vehicle, such as Lidar (Lidar), Inertial Measurement Unit (IMU), etc. The cost of installation and maintenance is extremely high, and it is difficult to popularize and apply

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