Emergency rescue vehicle driving optimization method driven by intelligent Internet of Vehicles

Abstract

The invention provides an emergency rescue vehicle driving optimization method driven by an intelligent Internet of Vehicles, and the method comprises the steps: 1, an optimal driving path of an emergency rescue vehicle is selected, the full consideration is carried out, the length of a road, the average passing time and the driving speed of the emergency rescue vehicle are weighed, and the weightis calculated; secondly, the driving path of the emergency rescue vehicle is optimized and perfected; when the emergency rescue vehicle runs to a traffic intersection, a motor vehicle signal lamp passing priority guarantee scheme is implemented for the emergency rescue vehicle, smooth passing of the emergency rescue vehicle at the traffic intersection is guaranteed, meanwhile, in the running process of the emergency rescue vehicle, other vehicles can take corresponding avoiding measures, the influence on the emergency rescue vehicle is reduced, and the road passing priority of the emergency rescue vehicle is guaranteed. According to the invention, the smooth driving of the emergency rescue vehicle is ensured, the delay caused by traffic jam is avoided, the time for the emergency rescue vehicle to arrive at a rescue site is reduced, and various losses caused by untimely rescue can be greatly reduced.

Description

technical field [0001] The invention relates to a driving optimization method for an emergency rescue vehicle, in particular to a driving optimization method for an emergency rescue vehicle driven by an intelligent Internet of Vehicles, and belongs to the technical field of driving methods for an emergency rescue vehicle. Background technique [0002] The rapid economic development and the rapid progress of society have greatly improved people's living standards, and the number of motor vehicles is increasing. While it is convenient for people's daily travel, it also brings serious traffic congestion problems. In the urban traffic environment, although the travel of emergency rescue vehicles has the priority of passage given by law, it is difficult to guarantee the priority of passage of emergency rescue vehicles in actual travel. [0003] Intelligent transportation system is an urban transportation system that integrates various high-tech for transportation and management. ...

AI Technical Summary

Problems solved by technology

[0004] Urban traffic congestion is becoming more and more serious, and the number of vehicles is increasing. The driving of emergency rescue vehicles is also facing many problems: how to quickly reach the scene when an emergency occurs; how to choose the most suitable driving route; give the rescue route Finally, how to ensure that this route becomes a green channel; in fact, tragedies caused by the delay of emergency rescue vehicles caused by traffic congestion have occurred frequently, and ensuring the priority and safe driving of emergency rescue vehicles is an urgent problem that needs to be solved at present

By using a different set of local rules at traffic intersections, the VTL-PIC protocol can detect the presence of emergency vehicles and assign control of intersections to emergency vehicles preferentially, but this method does not take into account the impact on the passing time of other vehicles. influences

[0007] The second is emergency rescue vehicle dispatching management and path management; the existing technology proposes a mixed traffic flow emergency vehicle channel based on cellular automata, and by analyzing the behavior of other vehicles to avoid emergency vehicles, the rules for changing lanes are modified, and the emergency vehicle channel Making lane changes is less disruptive than not changing lanes. Traffic flow conditions are different when emergency vehicles are present than when emergency vehicles are not present. In moderate or high-density traffic conditions, emergency vehicles can facilitate emergency response. This method optimizes the driving of emergency vehicles to a certain extent; however, this method has many problems in practical application, the overall efficiency is not high, and sometimes it even has counterproductive effects. with great difficulty

[0008] The third is the signal light control of emergency rescue vehicles; for the signal control of multiple emergency rescue vehicles, the prior art proposes an adaptive motor vehicle signal light control method based on various factors such as vehicle residence time and traffic flow priority of each phase. Use fuzzy Petri net to determine the green light time of the current phase, whether to shorten the red light time in the next direction, but the traffic light priority control strategy of emergency rescue vehicles, on the one hand, should consider the priority of emergency rescue vehicles, and on the other hand, should also consider minimizing The impact on normal traffic flow due to the passage of emergency rescue vehicles, but this method has a great impact on queue delays caus

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