Multi-signal-lamp intersection vehicle passing auxiliary optimization method considering driving style

Abstract

The invention discloses a multi-signal-lamp intersection vehicle passing auxiliary optimization method considering a driving style. The method comprises the steps of: 1, collecting the traffic flow information of a road, and calculating the traffic signal lamp timing of each intersection of a road section to be subjected to passing optimization; 2, collecting vehicle information of each vehicle and road information of the road section to be subjected to passing optimization; 3, calculating the expected vehicle speed and the expected vehicle speed interval of each vehicle passing through a first intersection in front of the vehicle without stopping; 4, judging the type of each vehicle, and if the vehicle is a manual driving vehicle, entering step 5; and if the vehicle is an automatic driving vehicle, entering step 6; 5, providing personalized vehicle speed and lane suggestions for a driver of the manual driving vehicle; 6, providing an expected vehicle speed and an expected lane for theautomatic driving vehicle, and calculating the control quantity required by the vehicle; and 7, according to the expected vehicle speed, calculating expected time required for each vehicle to arriveat the stop line, and optimizing traffic signal lamp timing by taking the shortest sum of the time of the vehicles passing through the intersection as a target. The traffic efficiency can be improved,the traffic safety can be improved, and the vehicle fuel economy can be improved.

Description

technical field [0001] The invention relates to the technical field of intelligent traffic systems, in particular to a method for assisting and optimizing vehicle passage at multi-signal intersections in consideration of driving styles. Background technique [0002] With the development of our country's social economy and the continuous improvement of people's living standards, the number of cars in our country continues to rise, which has brought severe traffic safety tests. At the same time, urban congestion has brought additional time costs and fuel losses to people's travel. On the other hand, motor vehicle exhaust emissions have also become the main source of smog. Therefore, it is of great research significance to ensure driving safety, optimize energy saving and emission reduction, and improve travel efficiency. [0003] In recent years, with the emergence and development of intelligent networked vehicles, new optimization methods have been provided for intelligent t...

AI Technical Summary

Problems solved by technology

In addition, the existing optimization methods do not consider the individual differences of drivers, that is, the d

Images