Enhancing Accuracy and Robustness of Steering Angle Prediction with Attention Mechanism

In this paper, our focus is on enhancing steering angle prediction for autonomous driving tasks. We initiate our exploration by investigating two veins of widely adopted deep neural architectures, namely ResNets and InceptionNets. Within both families, we systematically evaluate various model sizes to understand their impact on performance. Notably, our key contribution lies in the incorporation of an attention mechanism to augment steering angle prediction accuracy and robustness. By introducing attention, our models gain the ability to selectively focus on …

accuracy architectures attention autonomous autonomous driving cs.cv driving exploration families focus impact neural architectures paper performance prediction robustness tasks