Extending behavioral models to generate mission-based driving cycles for data-driven vehicle development
Driving cycles are nowadays to an increasing extent
used as input to model-based vehicle design and as training data
for development of vehicles models and functions with machine
learning algorithms. Recorded real driving data may
underrepresent or even lack important characteristics, and
therefore there is a need to complement driving cycles obtained
from real driving data with synthetic data that exhibit various
desired characteristics. In this paper, an efficient method for
generation of mission-based driving cycles is developed for this
purpose. It is based on available effective methods for traffic
simulation and available maps to define driving missions. By
comparing the traffic simulation results with real driving data,
insufficiencies in the existing behavioral model in the utilized
traffic simulation tool are identified. Based on these findings, four
extensions to the behavioral model are suggested, staying within
the same class of computational complexity so that it can still be
used in large scale. The evaluation results show significant
improvements in the match between the data measured on the
road and the outputs of the traffic simulation with the suggested
extensions of the behavioral model. The achieved improvements
can be observed with both visual inspection and objective
measures. For instance, the 40% difference in the relative positive
acceleration (RPA) of the originally simulated driving cycle
compared to real driving data was eliminated using the suggested
model.
Sogol Kharrazi, Marcus Almen, Erik Frisk and Lars Nielsen
IEEE Transactions on Vehicular Technology,
2019
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