SNAL: sensitive non-associative learning network configuration for the automatic driving strategy

Abstract

Nowadays, there is a huge gap between autonomous vehicles and mankind in terms of the decision response against some dangerous scenarios, which would has stressed the potential users out and even made them nervous. To efficiently identify the possible sensitivity scenarios, a new neural network configuration, named sensitive non-associative learning network (SNAL), is proposed. In such structure, the modulated interneurons, excited by abnormal scene stimulation for scene processing, are well processed and utilized to improve the training structure which refers to the sensitization mechanism in non-associative learning in neurobiology and the neural structure of Aplysia. When encountering the sensitivity scenes that the automatic driving agent is not good at or has not seen, the modulated interneuron facilitates the full connection layer neurons for the decision-making process, so as to change the final automatic driving strategy. In the process of constructing the model, a method to measure the similarity of the convolution feature map is proposed, which provides a new investigation tool for the properties of convolution networks after the feature extraction. Based on the Morris-Lecar equation in neurobiology, the dynamic model of modulating interneurons in the network is constructed. The automatic control optimization of the model is carried out by imitating the biological properties. The optimization method provides a reference for introducing neurobiological mechanism into deep learning and automatic control. To validate the effectiveness of the proposed method, the simulation test are executed and the existing methods are compared accordingly. The results show that the proposed SNAL algorithm can effectively recognize the sensitivity mechanism. Furthermore, compared with the existing algorithms, such as CNN, LSTM, ViT, the proposed algorithm can make better defensive strategies for potentially dangerous scenes rarely seen or not seen in the training stage. This sensitivity mechanism is more in line with the human driving intuition when dealing with abnormal driving scenes, and makes the decision more interpretable, significantly improving the traffic ability of autonomous vehicles under the sensitive scenes. In addition, this configuration can be easily combined with the existing mainstream neural network models and has good expansibility.