CBAM meets DropBlock: enhancing robot steering-angle prediction with hybrid attention and structured dropout

The prediction of steering-angle for robots in complex scenarios is crucial in intelligent auto-navigation process. However, traditional angle prediction approach of end-to-end auto-driving system often fall short in insufficient prediction accuracy, interference of complex scene features in the steering. This paper mainly proposes a lightweight steering angle prediction network based on the cooperative optimization strategy of bi-domain attention fused region mask and de-fuzzy network. Firstly, this method constructs a three-stage progressive framework featured the multi-scale feature pyramid, the channel/spatial attention decoupling, and the dynamic region feature dropout. The dual-channel feature compression is carried out in the channel dimension by using global average pooling and maximum pooling. Then, the channel weight vectors are generated by a multilayer perceptron and the key semantic features, such as lane lines and obstacles, are autonomously strengthened. Finally, in the spatial dimension, the attention heat map is generated by gated convolutional kernel, and the probabilistic feature masking of non-critical regions is combined with the DropBlock module, so as to effectively suppress the interfering information such as road texture and sky background. The experimental results show that the proposed method performs well in the steering angle prediction, suggesting promising applications and broad prospects for future.