TY - JOUR

T1 - Watch out, pothole! Featuring Road Damage Detection in an End-to-end System for Autonomous Driving

AU - Kortmann, Felix

AU - Faßmeyer, Pascal

AU - Funk, Burkhardt

AU - Drews, Paul

N1 - Publisher Copyright: © 2022 Elsevier B.V.

PY - 2022/11/1

Y1 - 2022/11/1

N2 - While autonomous driving technology made significant progress in the last decade, road damage detection as a relevant challenge for ensuring safety and comfort is still under development. This paper addresses the lack of algorithms for detecting road damages that meet autonomous driving systems’ requirements. We investigate the environmental perception systems’ architecture and current algorithm designs for road damage detection. Based on the autonomous driving architecture, we develop an end-to-end concept that leverages data from low-cost pre-installed sensors for real-time road damage and damage severity detection as well as cloud- and crowd-based HD Feature Maps to share information across vehicles. In a design science research approach, we develop three artifacts in three iterations of expert workshops and design cycles: the end-to-end concept featuring road damages in the system architecture and two lightweight deep neural networks, one for detecting road damages and another for detecting their severity as the central components of the system. The research design draws on new self-labeled automotive-grade images from front-facing cameras in the vehicle and interdisciplinary literature regarding autonomous driving architecture and the design of deep neural networks. The road damage detection algorithm delivers cutting-edge performance while being lightweight compared to the winners of the IEEE Global Road Damage Detection Challenge 2020, which makes it applicable in autonomous vehicles. The road damage severity algorithm is a promising approach, delivering superior results compared to a baseline model. The end-to-end concept is developed and evaluated with experts of the autonomous driving application domain.

AB - While autonomous driving technology made significant progress in the last decade, road damage detection as a relevant challenge for ensuring safety and comfort is still under development. This paper addresses the lack of algorithms for detecting road damages that meet autonomous driving systems’ requirements. We investigate the environmental perception systems’ architecture and current algorithm designs for road damage detection. Based on the autonomous driving architecture, we develop an end-to-end concept that leverages data from low-cost pre-installed sensors for real-time road damage and damage severity detection as well as cloud- and crowd-based HD Feature Maps to share information across vehicles. In a design science research approach, we develop three artifacts in three iterations of expert workshops and design cycles: the end-to-end concept featuring road damages in the system architecture and two lightweight deep neural networks, one for detecting road damages and another for detecting their severity as the central components of the system. The research design draws on new self-labeled automotive-grade images from front-facing cameras in the vehicle and interdisciplinary literature regarding autonomous driving architecture and the design of deep neural networks. The road damage detection algorithm delivers cutting-edge performance while being lightweight compared to the winners of the IEEE Global Road Damage Detection Challenge 2020, which makes it applicable in autonomous vehicles. The road damage severity algorithm is a promising approach, delivering superior results compared to a baseline model. The end-to-end concept is developed and evaluated with experts of the autonomous driving application domain.

KW - Informatics

KW - Business informatics

KW - Autonomous driving architecture

KW - Computer vision

KW - Road damage detection

KW - Road damage severity

UR - http://www.scopus.com/inward/record.url?scp=85140312732&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/809783fd-897a-3069-be9b-ccf1be61f7b0/

U2 - 10.1016/j.datak.2022.102091

DO - 10.1016/j.datak.2022.102091

M3 - Journal articles

VL - 142

JO - Data and Knowledge Engineering

JF - Data and Knowledge Engineering

SN - 0169-023X

M1 - 102091

ER -