{'fraction_non_alphanumeric': 0.02830188679245283, 'fraction_numerical': 0.0031446540880503146, 'mean_word_length': 5.725352112676056, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Analysis of the Generalized Intelligent Driver Model (GIDM) for Uncontrolled Intersections

This paper proposes a Generalized Intelligent Driver Model (GIDM) as an extension of the Intelligent Driver Model (IDM) and analyzes its applicability to model uncontrolled intersection scenarios. We extend the original longitudinal car-following IDM with several terms: (1) for anticipatory acceleration capabilities, we include the most nearby backward agent, (2) for consideration of traffic participants on paths that will cross the own path, we propose continuous virtual projections onto the ego vehicle path, and (3) for foresighted behavior, we introduce a longitudinal anticipatory shift. We analyze the model in extensive simulations of crossing cases, quantitatively measuring safety, comfort and utility. Compared to a baseline IDM, we can show the advantages of the GIDM, reflected in a systematic improvement of the GIDM in all considered measures.