Prediction of safety in Autonomous Vehicles using Modified Deep CNN-BiLSTM with attention mechanism

Electronics and automobile engineering

In the present world, the usages of autonomous cars are getting higher because of the emerging technology. These autonomous cars give freedom to the person those who are not able to drive. It can able to control the CO2 gas emission, avoid traffic and accidents and there are no attention issues like human in autonomous cars. However, the autonomous cars are not perfect because sometimes the autonomous cars face some issues while analysing the different human hand gesture, climatic conditions and road sign. To overcome this problem the proposed model use improved search ability based GA (Genetic Algorithm) in feature selection to attain the best features from the dataset and to predict the drivers behaviour and car mechanism the modified deep CNN (Convolutional Neural Network) –BiLSTM (Bidirectional Long Short Term Memory) algorithm with attention mechanism (AM) is used. While analysing the performance of the proposed model with metrics such as precision, recall, and F1 that is obtained, the overall accuracy of 96% thereby significantly enhances the safety prediction in autonomous vehicles.

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