Work

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

Year: 2025

Type: article

Abstract: 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.

Source: IEEE Access

DOI: 10.1109/access.2025.3562666

Authors: Youngseong Cho, Kyoungil Lim

Institution: Advanced Institute of Convergence Technology

Language: English

Topic: Autonomous Vehicle Technology and Safety

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funders: Ministry of Land, Infrastructure and Transport, Korea Agency for Infrastructure Technology Advancement