End-to-End Autonomous Decision-Making Method for Intelligent Vehicles Based on ResNet-CBAM-BiLSTM

To solve the difficulty of autonomous decision-making caused by the complex driving environment and changeable weather conditions, an end-to-end autonomous decision-making method based on residual network (ResNet), convolutional block attention module (CBAM) and bidirectional long short-term memory network (BiLSTM) is proposed for intelligent vehicles. Firstly, ResNet is used to extract spatial feature information contained in driving scene images. Then, CBAM is adopted to assign weights to each network channel and dynamically focus on important spatial regions in the image. Finally, BiLSTM is constructed to process the contextual features of continuous scenes, and the autonomous decision-making of intelligent vehicles is achieved through the fusion of spatial features and temporal information. On this basis, the proposed network model is trained using a real-world driving dataset and fully tested in various scenarios. Moreover, ablation experiments are conducted to verify the contribution of each module to the overall performance. The results show that the proposed method has better accuracy and stability compared with multiple existing methods, including PilotNet, FCN-LSTM, and DBNet, and its accuracy reaches 90.16% under clear weather conditions, as well as 81.29% under nighttime and snowy weather conditions.