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Analysis of a Generalized Intelligent Driver Model for merging situations

Karsten Kreutz and Julian Eggert, "Analysis of a Generalized Intelligent Driver Model for merging situations", IEEE Intelligent Vehicle Symposium 2021, pp. 34-41, 2021.


In this paper, we analyze an extension of the Intelligent Driver Model (IDM) for its application on single situation prediction in merging situations. For this purpose, we first extend the original, longitudinal single car following IDM with several terms. First, we include a consideration of more than a single leading car to be able to deal with pressure from back, as required for anticipatory acceleration. Second, we use a virtual projection of other cars onto the considered car driving path and a smooth weighting for a continuous blending to the longitudinal situation. And third, we introduce temporal shift terms that increase the considered car anticipatory capabilities. We analyze this model in systematic simulations, and show that it provides the model to (i) deal with these situations (which the original IDM cannot) and (ii) systematic benefits of the model in all the dimensions safety, utility and comfort as compared to a baseline extended model without temporal shift terms. As a conclusion, we argue that such a model can be utilized for safety relevant situation prediction purposes and applied in similar ways to many other situation classes.

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