TY - CHAP

T1 - Creating Value from in-Vehicle Data

T2 - 23rd IEEE International Conference on Intelligent Transportation 2020

AU - Kortmann, Felix

AU - Hsu, Yi-Chen

AU - Warnecke, Alexander

AU - Meier, Nicolas

AU - Heger, Jens

AU - Funk, Burkhardt

AU - Drews, Paul

N1 - Conference code: 23

PY - 2020/9/20

Y1 - 2020/9/20

N2 - An important component for the realization of the automated driving task is a holistic environment model. Connected and Autonomous Vehicles (CAVs) must be capable of detecting other vehicles, road markings, dangerous obstacles and upcoming road conditions. Apart from the comfort dependency on the road condition, friction values are calculated on the basis of road properties, which in turn are relevant for e.g. breaking and safety distances of CAVs. Due to the substitution of the human control task by the machine, this information must in future be detected by the vehicle itself. Based on the existing Vehicle Level Sensors (VLSs) and Acceleration Sensors (ASs) data, which are standard components in modern vehicles, a machine-learning approach of determining road surface materials and road hazards is presented. Our software solution of determining different road surface materials as asphalt, concrete, cobblestone or gravel with a total accuracy of 92.36% is presented. Furthermore, the results of the road hazards detection as potholes and speed bumps with a total accuracy of 92.39% is stated. Additionally to the edge calculations in the vehicle, our idea resolves in connected vehicles being capable of classifying road conditions enabling them to provide road analyses to a cloud platform. The goal is to establish a holistic cloud solution for road conditions to enable CAVs for the consumption of road condition data of upcoming road segments and empower them to adjust to those.

AB - An important component for the realization of the automated driving task is a holistic environment model. Connected and Autonomous Vehicles (CAVs) must be capable of detecting other vehicles, road markings, dangerous obstacles and upcoming road conditions. Apart from the comfort dependency on the road condition, friction values are calculated on the basis of road properties, which in turn are relevant for e.g. breaking and safety distances of CAVs. Due to the substitution of the human control task by the machine, this information must in future be detected by the vehicle itself. Based on the existing Vehicle Level Sensors (VLSs) and Acceleration Sensors (ASs) data, which are standard components in modern vehicles, a machine-learning approach of determining road surface materials and road hazards is presented. Our software solution of determining different road surface materials as asphalt, concrete, cobblestone or gravel with a total accuracy of 92.36% is presented. Furthermore, the results of the road hazards detection as potholes and speed bumps with a total accuracy of 92.39% is stated. Additionally to the edge calculations in the vehicle, our idea resolves in connected vehicles being capable of classifying road conditions enabling them to provide road analyses to a cloud platform. The goal is to establish a holistic cloud solution for road conditions to enable CAVs for the consumption of road condition data of upcoming road segments and empower them to adjust to those.

KW - Business informatics

KW - Roads

KW - Sensors

KW - Hazards

KW - Rough surfaces

KW - Cloud computing

KW - task analysis

KW - wheels

UR - http://www.scopus.com/inward/record.url?scp=85099662575&partnerID=8YFLogxK

U2 - 10.1109/ITSC45102.2020.9294684

DO - 10.1109/ITSC45102.2020.9294684

M3 - Article in conference proceedings

SN - 978-1-7281-4150-3

T3 - 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020

BT - 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

CY - Piscataway

Y2 - 20 September 2020 through 23 September 2020

ER -