Enhanced Behavioral Cloning Based self-driving Car Using Transfer Learning
This is an incremental improvement for self-driving car research, focusing on enhancing behavioral cloning methods.
The paper tackles the problem of improving self-driving car performance by proposing a transfer learning approach using VGG16 architecture, which outperforms existing NVIDIA architectures and their pruned variants with faster convergence.
With the growing phase of artificial intelligence and autonomous learning, the self-driving car is one of the promising area of research and emerging as a center of focus for automobile industries. Behavioral cloning is the process of replicating human behavior via visuomotor policies by means of machine learning algorithms. In recent years, several deep learning-based behavioral cloning approaches have been developed in the context of self-driving cars specifically based on the concept of transfer learning. Concerning the same, the present paper proposes a transfer learning approach using VGG16 architecture, which is fine tuned by retraining the last block while keeping other blocks as non-trainable. The performance of proposed architecture is further compared with existing NVIDIA architecture and its pruned variants (pruned by 22.2% and 33.85% using 1x1 filter to decrease the total number of parameters). Experimental results show that the VGG16 with transfer learning architecture has outperformed other discussed approaches with faster convergence.