Decision tree behavior decision algorithm based on demonstration learning

A decision algorithm and decision tree technology, applied in two-dimensional position/channel control, non-electric variable control, instruments, etc., to reduce frequency and ensure maximum rationality and safety.

Active Publication Date: 2017-12-15
TONGJI UNIV
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Problems solved by technology

[0006] The purpose of the present invention is to provide a decision tree behavior decision-making algorithm based on teaching and learning, to solve the problem that the existing decision-making algorithm is difficult to avoid the irrationality of behavior to the greatest extent from practical problems

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  • Decision tree behavior decision algorithm based on demonstration learning
  • Decision tree behavior decision algorithm based on demonstration learning
  • Decision tree behavior decision algorithm based on demonstration learning

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Embodiment Construction

[0036] The present invention will be further described below with reference to the accompanying drawings and examples. The embodiments of the present invention include but are not limited to the following examples.

[0037]In the entire algorithm framework, the decision tree algorithm is in the middle position, accepting the law of state transition upward, and connecting downward to strengthen or modify the law of state transition. For the teaching rules of human drivers, the present invention defines two matrices of state transition frequency and state transition probability for description. The state transition frequency is to record the number of times the state will be visited in the current state, and the state transition probability is the transition probability value obtained by calculating such times. When the transition probability outputs the upcoming selection action, the decision tree algorithm needs to check and evaluate the rationality or safety of the current ac...

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Abstract

The invention discloses a decision tree behavior decision algorithm based on demonstration learning, and mainly solves the problems in the prior art that the present decision algorithm cannot simultaneously consider the integrated and complex scenes and the requirement of stability. The decision tree behavior decision algorithm based on demonstration learning comprises the steps that the state transfer law of demonstration trajectories is stored; a state transfer frequency matrix and a state transfer probability matrix are obtained; the reward is constructed; the decision tree assesses the motion to be generated; the transfer frequency matrix and the state transfer probability matrix are updated; and the process is repeated until assessment is passed. With application of the scheme, the objectives of the maximum rationality and safety of the driverless behavior decision can be achieved.

Description

technical field [0001] The invention relates to the field of unmanned driving, in particular to a decision tree behavior decision algorithm based on teaching and learning. Background technique [0002] Driverless cars are an advanced form of mobile robots with autonomous driving capabilities. It is an intelligent computing system that can realize the three functions of environment perception, decision planning and motion control. Compared with other small mobile robots, the system has a more complicated structure. In addition to the basic mobile driving ability, it can use various sensors such as radar and camera to cooperate with a special high-precision map for real-time data fusion and positioning, so as to realize the perception and understanding of the current environment. At the same time, according to the road and moving obstacle information understood by the sensor, the vehicle uses the decision-making planning algorithm to cut out a reasonable and feasible expecte...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D1/02
CPCG05D1/0214G05D1/0276G05D2201/0212
Inventor 王祝萍邢文治张皓陈启军
Owner TONGJI UNIV
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