Research And Implementation Of An Artificial Intelligence Algorithm For Distracted Driving Monitoring

Most traffic accidents are caused by distracted driving.Distraction mitigation systems have become a very active area in intelligent traffic systems.Distraction mitigation systems includes distraction identification and distraction mitigation.How to accurately identify the driving status of the driver in real time,and reasonably remind the driver to avoid traffic accidents caused by distracted driving is an urgent problem to be solved in the field of distracted driving research.Distraction identification usually adopts neural network methods,however,there are many issues concerning using this methods.First,the existing CNN(Convolutional Neural Network)methods only process single frame images,so they cannot capture the real-time continuous motion of the driver.Second,although ResNet-50 can be used in distraction identification,there are certain application areas that the method has failed to extract the required features for use in identification.Third,in static distraction identification mode,neural network is unable to distinguish between normal driving state and daze state.This thesis proposes a real-time dynamic distraction mitigation systems based on neural network to solve the above mentioned issues.First,the combination of CNN and RNN(recurrent neural network)is applied to the real-time distraction identification.This method uses RNN to fuse the information of spaced frames and link the real-time continuous motion of the driver together.Second,this thesis proposes a new ResNet-50_v2,an improved version of ResNet-50,that can solve the failure of required features extraction in ResNet-50 by intelligently making full use of the image information.Third,the real-time dynamic distraction mitigation system is able to analyse the continuous motion of the driver and hence able to distinguish between normal driving state and daze state.The test results show that ResNet-50_v2 performs better than ResNet-50 in static distraction identification mode;ResNet-50_v2 has a distraction identification accuracy of 96.2667%,which is 4.3334% better than ResNet-50 with a distraction identification accuracy of 91.9333%.In real-time dynamic distraction identification mode,the distraction mitigation system is better than ResNet-50_v2 with a distraction identification accuracy of 98.3% because it is able to distinguish between normal driving state and daze state.