Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Integrating Modular Pipelines with End-to-End Learning: A Hybrid Approach for Robust and Reliable Autonomous Driving Systems

Luis Alberto Rosero
* ,
Iago Pachêco Gomes
ORCID logo ,
Júnior Anderson Rodrigues Da Silva
,
Carlos André Braile Przewodowski Filho
,
Denis Fernando Wolf
,
Fernando Santos Osório
* ORCID logo
Version 1 : Received: 6 February 2024 / Approved: 7 February 2024 / Online: 7 February 2024 (11:45:55 CET)

A peer-reviewed article of this Preprint also exists.

Rosero, L.A.; Gomes, I.P.; da Silva, J.A.R.; Przewodowski, C.A.; Wolf, D.F.; Osório, F.S. Integrating Modular Pipelines with End-to-End Learning: A Hybrid Approach for Robust and Reliable Autonomous Driving Systems. Sensors 2024, 24, 2097. Rosero, L.A.; Gomes, I.P.; da Silva, J.A.R.; Przewodowski, C.A.; Wolf, D.F.; Osório, F.S. Integrating Modular Pipelines with End-to-End Learning: A Hybrid Approach for Robust and Reliable Autonomous Driving Systems. Sensors 2024, 24, 2097.

Abstract

Autonomous driving navigation relies on diverse approaches, each with advantages and limitations depending on various factors. For HD maps, modular systems excel, while end-to-end methods dominate mapless scenarios. However, few leverage the strengths of both. This paper innovates by proposing a hybrid architecture that seamlessly integrates modular perception and control modules with data-driven path planning. This innovative design leverages the strengths of both approaches, enabling a clear understanding and debugging of individual components while simultaneously harnessing the learning power of end-to-end approaches. Our proposed architecture achieved 1st and 2nd place in the 2023 CARLA Autonomous Driving Challenge’s SENSORS and MAP tracks, respectively. These results demonstrate the architecture’s effectiveness in both map-based and mapless navigation.

Keywords

Autonomous driving; hybrid architecture; modular architecture; end-to-end; path planning; CARLA simulator; Intelligent and Autonomous Vehicles

Subject

Computer Science and Mathematics, Robotics

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Download PDF

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0
Get PDF Cite Share 0
Bookmark BibSonomy Mendeley Reddit Delicious
×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.