A method to assess individualized driver models: Descriptiveness, identifiability and realism. (February 2019) - Barendswaard, Sarah; Pool, Daan M.; Abbink, David A.
Highlights: A novel model assessment method is proposed to assess driver model capabilities of capturing individual steering behaviour for practical identification purposes. The model assessment method is demonstrated on two driver steering models, one of which has previously been used for individualized driver identification. Key findings are that optimising and identification in purely the steering angle domain are insufficient and can be misleading. Model assessment criteria provide an in-depth analysis why a model has satisfactory performance for the purpose of online identification of individualized driving behaviour. Model assessment criteria give a quantitative grade on the models that also allows for comparison with other models. Abstract: This paper introduces a systematic assessment method which quantitatively assesses computational driver steering models with respect to their suitability for online identification of individual driver steering behaviour. This methodology is based on three criteria: (1) descriptiveness, the model's ability to capture different types of steering behaviour, (2) identifiability, the ability of the model for unique mapping between a steering behaviour and a parameter combination, and (3) realism, the parameter span resulting in realistic steering behaviour. The utility of the introduced assessment method is shown by analysing and comparing two driver models from literature which are based on the same high-level concept. Both models assume proportional control on a predicted lateral position, however one uses a linear prediction for lateral position and the other uses a nonlinear prediction. The proposed assessment method distinguishes between the performance of the models by showing that the nonlinear model outperforms the linear model in terms of descriptiveness (66% compared to 33% of the linear model), better inherent identifiability for steering angle (3.8 compared to 7.5), better inherent identifiability for lateral position (0.01 compared to 0.5), better curve-cutting experimental identifiability and a 2.72 times larger realistic parameter span allowing for more flexibility for parameter selection. This quantitative assessment method has successfully reflected the effect of merely altering the way the lateral position is predicted in two driver models. Thereby, this method can be used to give a fair assessment by giving a model an absolute classification that also allows for quantitative comparison with many more driver models.