Open AccessMacroscopic model for multi-anticipation self-driving cars
Author(s) -
Takla Nateeboon,
Teerasit Termsaithong,
Ekapong Hirunsirisawat
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1380/1/012099
Subject(s) - anticipation (artificial intelligence) , traffic flow (computer networking) , flow (mathematics) , self driving , work (physics) , mechanics , instability , statistical physics , simulation , computer science , physics , automotive engineering , engineering , mechanical engineering , artificial intelligence , computer security
Self-driving cars technology and vehicle connectivity enable self-driving cars to precisely receive information of many cars leading them. By multi-anticipation, a flow of self-driving car is proved to be more stable than without multi-anticipation. The instability of traffic flow can cause a traffic jam as a little velocity fluctuation is amplified. In this work, we discuss the macroscopic effect of parameters in the microscopic model for a self-driving car. Those parameters are the number of the vehicle the car anticipate (field size) and weight for anticipation with the leadings (strength factor). The macroscopic model is derived from the microscopic model. We obtained a partial derivative equation that describe how the velocity fluctuation change through time. According to our model, these two variables play a crucial role in the traffic flow harmonization. Comparison between each model was carried out and discussed.