TY  - JOUR
PY  - 2022//
TI  - Physics of microscopic vehicular traffic prediction for automated driving
JO  - Physical review. E
A1  - Kerner, Boris S.
A1  - Klenov, Sergey L.
SP  - e044307
EP  - e044307
VL  - 106
IS  - 4-1
N2  - With the use of microscopic traffic simulations, physical features of microscopic traffic prediction for automated driving that should improve traffic harmonization and safety have been found: During a short-time prediction horizon (about 10 s), online prediction of the locations and speeds of all vehicles in some limited area around the automated-driving vehicle is possible; this enables the automated vehicle control in complex traffic situations in which the automated-driving vehicle is not able to make a decision based on current traffic information without the use of the microscopic traffic prediction. Through a more detailed analysis of an unsignalized city intersection, when the automated vehicle wants to turn right from a secondary road onto the priority road, the statistical physics of the effect of a data uncertainty caused by errors in data measurements on the prediction reliability has been studied: (i) probability of the prediction reliability has been found; (ii) there is a critical uncertainty, i.e., a maximum amplitude of errors in data measurements: when the uncertainty does not exceed the critical uncertainty, the prediction reliability probability is equal to 1, otherwise, the prediction is not applicable for a reliable automated vehicle control; (iii) physical characteristics of the microscopic traffic prediction, at which the critical uncertainty can be increased considerably, have been found; and (iv) there is an optimal automated vehicle control at which the critical uncertainty reaches a maximum value.<p />  <p>Language: en</p>
LA  - en
SN  - 2470-0045
UR  - http://dx.doi.org/10.1103/PhysRevE.106.044307
ID  - ref1
ER  -