Efficient normalization for quantitative evaluation of the driving behavior using a gated auto-encoder

Abstract

Driving behavior normalization is important for a fair evaluation of the driving style. The longitudinal control of a vehicle is investigated in this study. The normalization task can be considered as mapping of the driving behavior in a different environment to the uniform condition. Unlike the model-based approach as in previous work, where a necessary driver model is employed to conduct the driving cycle test, the approach we propose directly normalizes the driving behavior using an autoencoder (AE) when following a standard speed profile. To ensure a positive correlation between the vehicle speed and driving behavior, a gate constraint is imposed in between the encoder and decoder to form a gated AE (gAE). This approach is model-free and efficient. The proposed approach is tested for consistency with the model-based approach and for its applications to quantitative evaluation of the driving behavior and fuel consumption analysis. Simulations are conducted to verify the effectiveness of the proposed scheme.

Keywords

Driving behavior / Normalization / Gated auto-encoder / Quantitative evaluation

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2022 Zhejiang University Press