Multi-factor influence-based analysis method for curve driving track of driver

Abstract

The present invention provides a method for analyzing the driver's curve trajectory based on the influence of multiple factors, including step S1, proposing the influencing factors and evaluation indicators of the driver's curve trajectory: the influencing factors include objective factors and subjective factors, and the objective factors include driving The driving speed of the driver and the curvature of the curve, the subjective factors include the driving experience of excellent drivers, and the evaluation indicators include objective evaluation and subjective evaluation; step S2, establish an orthogonal table according to the orthogonal test method; step S3, according to the obtained orthogonal table Carry out the test of the driver's curve driving trajectory; step S4, the obtained test data is subjected to variance analysis, and the significance analysis of the influencing factors is carried out. The present invention proposes an analysis method that combines objective factors and subjective factors that affect the driver's driving track on a curve, analyzes the significance of the objective factors and subjective factors based on the orthogonal test method, and ranks the significance of the proposed influencing factors, It lays the foundation for the research on the humanoid control of intelligent vehicles under curve conditions.

Description

technical field [0001] The invention belongs to the field of intelligent car humanoid control, and in particular relates to a driver's curve driving trajectory analysis method based on the influence of multiple factors. Background technique [0002] Smart cars have functions such as environment perception, rule decision-making, multi-level assisted driving, etc., and integrate technologies such as computer, communication, artificial intelligence and automatic control. At present, the focus of smart car research is to improve the safety and comfort of the car and provide an excellent human-vehicle interface. Excellent drivers have rich driving experience and skills. If smart cars can learn or imitate human drivers to control the car steering system, it will make smart cars more acceptable to humans. [0003] Among the existing intelligent vehicle route planning methods, the Chinese patent application: Intelligent vehicle route planning method based on threat estimation (appl...

AI Technical Summary

Problems solved by technology

Chinese patent application: A vehicle route planning method based on experience route (Application No.: CN201610417918.5) When optimizing the experience route, only the straight-line distance from the starting point to the experience route and the straight-line distance from the key points to the experience route are considered. Ignoring the subjective factors in the experience path and the rest of the environmental factors in the experience path

This method is obviously different from the actual driving trajectory, and cannot meet the requirements of human-like intelligent driving

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