Enhancing Accuracy and Robustness of Steering Angle Prediction with Attention Mechanism
This work addresses critical accuracy and robustness concerns for autonomous driving systems, though it is incremental as it builds on existing architectures with attention.
The paper tackled steering angle prediction for autonomous driving by incorporating an attention mechanism into ResNets and InceptionNets, achieving over 6% error reduction and up to 56.09% robustness improvement on datasets like Kaggle SAP.
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 crucial regions within the input data, leading to improved predictive outcomes. Our findings showcase that our attention-enhanced models not only achieve state-of-the-art results in terms of steering angle Mean Squared Error (MSE) but also exhibit enhanced adversarial robustness, addressing critical concerns in real-world deployment. For example, in our experiments on the Kaggle SAP and our created publicly available datasets, attention can lead to over 6% error reduction in steering angle prediction and boost model robustness by up to 56.09%.