A real-time identification method of driver's braking behavior and state

Abstract

The invention discloses a real-time driver's braking behavior and state distinguishing method. The method comprises the following steps that 1, original test data of an actual vehicle of a driver is obtained through an actual vehicle testing platform; 2, the test data is preprocessed, wherein the preprocessing includes noise reduction preprocessing and abnormal data processing; 3, a principal component analysis method is utilized to conduct feature extraction and data dimension reduction on preprocessed data; 4, a braking pedal force, a braking pedal stroke and braking pedal stroke change rate data are clustered; 5, offline learning of different braking behaviors and states is completed based on a statistic pattern recognition model, and a behavior training library and a state training library of corresponding quantization representation values are obtained; 6, braking behaviors and states of different drivers under different driving working conditions are distinguished in real time. The method can be used in the technical fields of a brake-by-wire system, a braking energy recovery strategy, vehicle running state monitoring and the like, and can also create conditions for automatic driving decision-making control based on driver's behaviors.

Description

technical field [0001] The invention relates to vehicle intelligent assisted driving technology, in particular to a method for real-time identification of a driver's braking behavior and state. Background technique [0002] As vehicle technology continues to advance towards intelligence and sharing, a series of intelligent assisted driving systems around the vehicle braking system have been proposed, which put forward higher requirements for the control decision-making of the traditional braking system. In the "Technology Roadmap for Energy Saving and New Energy Vehicles" issued by the Society of Automotive Engineers of China, the development requirements for automatic driving control functions based on driver behavior are proposed for the control decision-making of intelligent networked vehicles, emphasizing the impact of driving behavior on the environment, vehicle characteristics, The dynamic movement mechanism adapts to the necessity of learning. [0003] Braking state ...

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