Automobile cooperative type self-adaptive cruise control system and method with learning ability

Abstract

The invention discloses an automobile cooperative type self-adaptive cruise control system with the learning ability. The automobile cooperative type self-adaptive cruise control system comprises a training set and testing set generating module, a training set supplementing module, a training module, a learning algorithm module, an automobile travelling state set module, an automobile control motion set module, an inter-automobile safe distance module, an evaluating module, a feedback value R calculating module, a Q weight updating module, an automobile control motion selecting module and an automobile control motion executing module. The automobile cooperative type self-adaptive cruise control system and method with the learning ability are built and can be used for the one-lane or one-way automobile travelling environment. According to the automobile cooperative type self-adaptive cruise control system and method with the learning ability, each automobile is an intelligent agent, travelling conditions of other automobiles in the travelling environment are sensed through communication, and certainly the sensed environment is a part of the automobile travelling environment due to limitations of communication ranges, data volume, communication channels and the like.

Description

technical field [0001] The invention relates to a car cooperative adaptive cruise control system and method with learning ability. Background technique [0002] With the increase in the number of cars on the road for personal daily travel, most cities face traffic problems, especially in densely populated areas. For most people living in these areas, getting stuck in traffic while commuting to and from get off work is part of their daily lives. People's frequent acceleration and braking control switching will cause traffic congestion when driving. Moreover, people's slow response and the limited range of traffic conditions that can be seen will all aggravate traffic jams. From an environmental point of view, unnecessary acceleration and braking maneuvers increase pollution on the road. In addition, human driving error is also a major factor causing today's traffic problems. [0003] With the development of science and technology, automobile cooperative adaptive cruise co...

AI Technical Summary

Problems solved by technology

[0008] Fulda and Ventura et al proposed the DJAP dynamic adjacent car maneuvering action insight algorithm for car cruise control, in which each agent can dynamically select useful adjacent maneuvering actions during the learning period, however, it only considers the movement of the car when it is driving Adjacent maneuvers

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