Polynomial and Differential Networks for End-to-End Autonomous Driving

This study introduces a novel model for predicting control variables in end-to-end autonomous driving by leveraging polynomial and differential networks. Recent advancements in autonomous driving have predominantly focused on methods that incorporate additional supervisory data, such as attention mechanisms and bird's-eye view images. However, these approaches are often hindered by issues related to computational efficiency and the high costs of data acquisition for real-world applications. In contrast, the proposed method enhances the performance by integrating polynomial and differential networks, facilitating efficient learning while accounting for the physical properties inherent in the data. The results of experiments conducted using the CARLA simulator demonstrate that the proposed model outperforms existing state-of-the-art approaches. The model weights and training code used in these experiments are available at https://github.com/choys0401/polydiff.