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In traffic flow modeling, the intelligent driver model (IDM) is a time-continuous car-following model for the simulation of freeway and urban traffic. It was developed by Treiber, Hennecke and Helbing in 2000 to improve upon results provided with other "intelligent" driver models such as Gipps' model, which loses realistic properties in the deterministic limit.
The main idea of this model is that a vehicle will maintain a minimum space and time gap between it and the vehicle that precedes it. Thus, under congested conditions, if the leading car changes its speed, the following vehicle will also change speed at a point in time-space along the traffic wave speed, -w. [1]
The Dubins' path gives the shortest path joining two oriented points that is feasible for the wheeled-robot model. The optimal path type can be described using an analogy with cars of making a 'right turn (R)', 'left turn (L)' or driving 'straight (S).' An optimal path will always be at least one of the six types: RSR, RSL, LSR, LSL, RLR, LRL.
In information theory, linguistics, and computer science, the Levenshtein distance is a string metric for measuring the difference between two sequences. The Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other.
The English word car is believed to originate from Latin carrus/carrum "wheeled vehicle" or (via Old North French) Middle English carre "two-wheeled cart", both of which in turn derive from Gaulish karros "chariot". [20] [21] It originally referred to any wheeled horse-drawn vehicle, such as a cart, carriage, or wagon. [22]
The dynamic predecessor problem is also commonly analyzed in the word RAM model, and was the original motivation for the model. Dan Willard used y-fast tries to solve this in O ( log w ) {\displaystyle O(\log w)} time, or, more precisely, O ( log log U ) {\displaystyle O(\log \log U)} where U is a bound on the values stored. [ 5 ]