TY - GEN
T1 - Optimizing Autonomous Vehicle Sensor Setups
T2 - 2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration, SDF-MFI 2023
AU - Hafemann, Philipp
AU - Hahn, Simon Enrico
AU - Lienkamp, Markus
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The performance of environment perception in autonomous vehicles is significantly influenced by the sensor setup, which is determined in the early design phase. The selection of the type and position of sensors in this phase often occurs before the availability of data processing algorithms. Therefore, the evaluation of the sensor setup can only be based on coverage metrics. This paper presents a novel framework for modeling the sensor coverage of autonomous vehicles. By discretizing the environment into grid cells, our framework analyzes each sensor and the whole setup. Our methodology systematically determines the coverage, identifies redundancy and blind spot areas, and obtains quantifiable metrics for evaluating the sensor setup efficiency. Utilizing the PyVista visualization library, we present the individual building blocks of the framework and their open-source implementation. The results from a real-world case study demonstrate our framework's ability to identify weaknesses in sensor setup coverage. Our approach helps to develop a comprehensive understanding of the sensor coverage and, therefore, contributes to designing more effective and reliable sensing systems.
AB - The performance of environment perception in autonomous vehicles is significantly influenced by the sensor setup, which is determined in the early design phase. The selection of the type and position of sensors in this phase often occurs before the availability of data processing algorithms. Therefore, the evaluation of the sensor setup can only be based on coverage metrics. This paper presents a novel framework for modeling the sensor coverage of autonomous vehicles. By discretizing the environment into grid cells, our framework analyzes each sensor and the whole setup. Our methodology systematically determines the coverage, identifies redundancy and blind spot areas, and obtains quantifiable metrics for evaluating the sensor setup efficiency. Utilizing the PyVista visualization library, we present the individual building blocks of the framework and their open-source implementation. The results from a real-world case study demonstrate our framework's ability to identify weaknesses in sensor setup coverage. Our approach helps to develop a comprehensive understanding of the sensor coverage and, therefore, contributes to designing more effective and reliable sensing systems.
KW - Autonomous Vehicle
KW - Field of View
KW - Sensor Coverage
KW - Sensor Setup
UR - http://www.scopus.com/inward/record.url?scp=85182395557&partnerID=8YFLogxK
U2 - 10.1109/SDF-MFI59545.2023.10361427
DO - 10.1109/SDF-MFI59545.2023.10361427
M3 - Conference contribution
AN - SCOPUS:85182395557
T3 - IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
BT - 2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration, SDF-MFI 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 November 2023 through 29 November 2023
ER -