Tootfinder

This https://arxiv.org/abs/2502.16644 has been replaced.
link: https://scholar.google.com/scholar?q=a

Mapping out AI Functions in Intelligent Disaster (Mis)Management and AI-Caused Disasters
This study maps the functions of artificial intelligence in disaster (mis)management. It begins with a classification of disasters in terms of their causal parameters, introducing hypothetical cases of independent or hybrid AI-caused disasters. We then overview the role of AI in disaster management and mismanagement, where the latter includes possible ethical repercussions of the use of AI in intelligent disaster management (IDM), as well as ways to prevent or mitigate these issues, which inclu…

Twenty-Five Years of the Intelligent Driver Model: Foundations, Extensions, Applications, and Future Directions
Shirui Zhou, Shiteng Zheng, Junfang Tian, Rui Jiang, and H. M. Zhang
https://arxiv.org/abs/2506.05909

Twenty-Five Years of the Intelligent Driver Model: Foundations, Extensions, Applications, and Future Directions
The Intelligent Driver Model (IDM), proposed in 2000, has become a foundational tool in traffic flow modeling, renowned for its simplicity, computational efficiency, and ability to capture diverse traffic dynamics. Over the past 25 years, IDM has significantly advanced car-following theory and found extensive application in intelligent transportation systems, including driver assistance systems and autonomous vehicle control. However, IDM's deterministic framework and simplified assumptions fac…

Markov Regime-Switching Intelligent Driver Model for Interpretable Car-Following Behavior
Chengyuan Zhang, Cathy Wu, Lijun Sun
https://arxiv.org/abs/2506.14762

Markov Regime-Switching Intelligent Driver Model for Interpretable Car-Following Behavior
Accurate and interpretable car-following models are essential for traffic simulation and autonomous vehicle development. However, classical models like the Intelligent Driver Model (IDM) are fundamentally limited by their parsimonious and single-regime structure. They fail to capture the multi-modal nature of human driving, where a single driving state (e.g., speed, relative speed, and gap) can elicit many different driver actions. This forces the model to average across distinct behaviors, red…