Evaluation of Autonomous Vehicle Sensing and Compute Load on a Chassis Dynamometer

The sensing and compute load auxiliary energy consumption in autonomous vehicles may be significant due to the large number of sensors and the high compute load from sensor processing and route planning. To understand this issue, this study investigates the top-down energy usage of an electric 2015...

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Bibliographic Details
Published in2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) pp. 1989 - 1995
Main Authors Brown, Nicholas E., Motallebiaraghi, Farhang, Rojas, Johan Fanas, Ayantayo, Sherif, Meyer, Richard, Asher, Zachary D, Ekti, Ali Riza, Wang, Chieh Ross, Goberville, Nicholas A., Feinberg, Ben
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.09.2023
Subjects
Autonomous Electric Vehicle
Autonomous vehicles
Connected Autonomous Vehicle
Dynamometers
Electric vehicles
Instruments
Load modeling
Power demand
Sensors
Vehicle Battery Performance
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Summary:The sensing and compute load auxiliary energy consumption in autonomous vehicles may be significant due to the large number of sensors and the high compute load from sensor processing and route planning. To understand this issue, this study investigates the top-down energy usage of an electric 2015 Kia Soul fully instrumented with state sensors and a state-specific computer for path planning and sensor processing. A chassis dynamometer was then used to evaluate the cases of (1) no sensors or computation, (2) only sensors operating, and (3) sensors plus compute load. The vehicle was operated autonomously on the dynamometer using a PolySync drive-kit with drive-by-wire longitudinal control. The DynoJet model 224xLC was used to adapt the eddy current dynamometer's road load parameters to comply with an Environmental Protection Agency drive schedule and to evaluate performance against the Argonne National Laboratory Digital Dynamometer Dataset. On the UDDS-HWFET combined driving cycle, the stock battery's range was reduced by 5.6% for sensors alone and 12.2% for sensors and compute load. These results show that the added sensing and compute auxiliary load from automated and autonomous systems is significant and that research efforts need to be spent investigating new energy efficient systems.
ISSN:2153-0017
DOI:10.1109/ITSC57777.2023.10422013