TY  - JOUR
A1  - Schulte-Tigges, Joschua
A1  - Förster, Marco
A1  - Nikolovski, Gjorgji
A1  - Reke, Michael
A1  - Ferrein, Alexander
A1  - Kaszner, Daniel
A1  - Matheis, Dominik
A1  - Walter, Thomas
T1  - Benchmarking of various LiDAR sensors for use in self-driving vehicles in real-world environments
JF  - Sensors
N2  - Abstract
In this paper, we report on our benchmark results of the LiDAR sensors Livox Horizon, Robosense M1, Blickfeld Cube, Blickfeld Cube Range, Velodyne Velarray H800, and Innoviz Pro. The idea was to test the sensors in different typical scenarios that were defined with real-world use cases in mind, in order to find a sensor that meet the requirements of self-driving vehicles. For this, we defined static and dynamic benchmark scenarios. In the static scenarios, both LiDAR and the detection target do not move during the measurement. In dynamic scenarios, the LiDAR sensor was mounted on the vehicle which was driving toward the detection target. We tested all mentioned LiDAR sensors in both scenarios, show the results regarding the detection accuracy of the targets, and discuss their usefulness for deployment in self-driving cars.
KW  - Lidar
KW  - Benchmark
KW  - Self-driving
Y1  - 2022
U6  - https://doi.org/10.3390/s22197146
SN  - 1424-8220
N1  - This article belongs to the Special Issue "Sensor Fusion for Vehicles Navigation and Robotic Systems"
VL  - 22
IS  - 19
PB  - MDPI
CY  - Basel
ER  - 
TY  - JOUR
A1  - Coll-Perales, Baldomero
A1  - Schulte-Tigges, Joschua
A1  - Rondinone, Michele
A1  - Gozalvez, Javier
A1  - Reke, Michael
A1  - Matheis, Dominik
A1  - Walter, Thomas
T1  - Prototyping and evaluation of infrastructure-assisted transition of control for cooperative automated vehicles
JF  - IEEE Transactions on Intelligent Transportation Systems
N2  - Automated driving is now possible in diverse road and traffic conditions. However, there are still situations that automated vehicles cannot handle safely and efficiently. In this case, a Transition of Control (ToC) is necessary so that the driver takes control of the driving. Executing a ToC requires the driver to get full situation awareness of the driving environment. If the driver fails to get back the control in a limited time, a Minimum Risk Maneuver (MRM) is executed to bring the vehicle into a safe state (e.g., decelerating to full stop). The execution of ToCs requires some time and can cause traffic disruption and safety risks that increase if several vehicles execute ToCs/MRMs at similar times and in the same area. This study proposes to use novel C-ITS traffic management measures where the infrastructure exploits V2X communications to assist Connected and Automated Vehicles (CAVs) in the execution of ToCs. The infrastructure can suggest a spatial distribution of ToCs, and inform vehicles of the locations where they could execute a safe stop in case of MRM. This paper reports the first field operational tests that validate the feasibility and quantify the benefits of the proposed infrastructure-assisted ToC and MRM management. The paper also presents the CAV and roadside infrastructure prototypes implemented and used in the trials. The conducted field trials demonstrate that infrastructure-assisted traffic management solutions can reduce safety risks and traffic disruptions.
KW  - Automated driving
KW  - automated vehicles
KW  - connected automated vehicles
KW  - CAV
KW  - experimental evaluation
Y1  - 2021
U6  - https://doi.org/10.1109/TITS.2021.3061085
SN  - 1524-9050 (Print)
SN  - 1558-0016 (Online)
VL  - 23
IS  - 7
SP  - 6720
EP  - 6736
PB  - IEEE
ER  - 
TY  - JOUR
A1  - Ulmer, Jessica
A1  - Braun, Sebastian
A1  - Cheng, Chi-Tsun
A1  - Dowey, Steve
A1  - Wollert, Jörg
T1  - A human factors-aware assistance system in manufacturing based on gamification and hardware modularisation
JF  - International Journal of Production Research
N2  - Assistance systems have been widely adopted in the manufacturing sector to facilitate various processes and tasks in production environments. However, existing systems are mostly equipped with rigid functional logic and do not provide individual user experiences or adapt to their capabilities. This work integrates human factors in assistance systems by adjusting the hardware and instruction presented to the workers’ cognitive and physical demands. A modular system architecture is designed accordingly, which allows a flexible component exchange according to the user and the work task. Gamification, the use of game elements in non-gaming contexts, has been further adopted in this work to provide level-based instructions and personalised feedback. The developed framework is validated by applying it to a manual workstation for industrial assembly routines.
KW  - Human factors
KW  - assistance system
KW  - gamification
KW  - adaptive systems
KW  - manufacturing
Y1  - 2023
U6  - https://doi.org/10.1080/00207543.2023.2166140
SN  - 0020-7543 (Print)
SN  - 1366-588X (Online)
PB  - Taylor & Francis
ER  -