Intelligent urban traffic management platform

Abstract

Provided is an intelligent urban traffic management platform, which comprises an intelligent vehicle system, a high-precision map system, a traffic management system and a vehicle cloud system. The intelligent vehicle system comprises a 3G / 4G SIM card, a man-machine interaction module, a wireless communication module, a sensor, a microprocessor, an executor and a storage device. The high-precision map system is used for receiving information and requests sent by the intelligent vehicle system, carrying out processing on the information and sending map information according to the requests and sending the processed map information to the traffic management system; the traffic management system is used for integrating the information provided by the high-precision map system and the intelligent vehicle system, displaying traffic situations, carrying out self-learning optimization and sending the traffic information to the intelligent vehicle system according to the requests; and the vehicle cloud system is used for carrying out identification learning and fusion on the data collected by different intelligent vehicle systems and sending learning information to the intelligent vehicle systems. The platform solves the problem of information lack among vehicles, roads and traffic management systems.

Description

technical field [0001] The invention belongs to the field of traffic management systems, and in particular relates to a management platform for information sharing and coordinated control among intelligent vehicles, intelligent traffic systems and roads. Background technique [0002] With the development of network communication and artificial intelligence technology, intelligent transportation system (ITS) and unmanned driving have attracted much attention in recent years. The intelligence of transportation systems and vehicles can improve traffic efficiency and driving safety, and reduce energy consumption. However, the current research has the following shortcomings: First, the current research on unmanned driving is divided into two directions, (1) Google and Baidu represent the use of high-precision maps combined with expensive sensors such as lidar to achieve unmanned driving; (2) ) represented by automobile factories adopts a gradual intelligent route, gradually trans...

AI Technical Summary

Problems solved by technology

However, the current research has the following shortcomings: First, the current research on unmanned driving is divided into two directions, (1) Google and Baidu represent the use of high-precision maps combined with expensive sensors such as lidar to achieve unmanned driving; (2) ) represented by automobile factories adopts a gradual intelligent route, gradually transitioning from advanced intelligent driving assistance system (ADAS) to unmanned driving; but the cost of the former is too high, and the intelligence level of the latter is too low; secondly, vehicles and Traffic systems interact and influence each other. Existing ITS research focuses on signal management and traffic flow analysis at intersections. It does not consider the real-time update of maps and the impact of vehicle route planning on traffic conditions in the future, and vehicles do not know the future. The status of traffic over a period of time, that is, the lack of information sharing between vehicles, roads and traffic management systems; moreover, most of the current V2V and V2X use DSRC (Dedicated Short-Range Communication), which requires dedicated communication equipment and infrastructure, and vehicle costs The cost of reconstruction and transportation system is relatively high; finally, the intelligentization of vehicles and transportation systems requires machines to identify and deal with some uncertain things and situations. Current research mainly uses neural networks for deep learning, but the offline learning database is not complete, and online learning The real-time performance is not enough and the initial effect is not good

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