Patent ID: 11919536
Assignee: TSINGHUA UNIVERSITY
Field: Transport (Mechanical engineering)
Classification: CPC B  G | IPC B  G

Claim 0:
1. A method for steering comfort in a human machine cooperative take-over control process of an autonomous vehicle, comprising:
acquiring vehicle data information collected by control mechanism sensors and vehicle sensors in a current driving cycle, and preprocessing the vehicle data information to form preprocessed vehicle data information;
selecting a training data set of a comfort model, wherein the training data set comprises subjective evaluation data information and the preprocessed vehicle data information, with the preprocessed vehicle data information being in a one-to-one correspondence with the subjective evaluation data information; and the subjective evaluation data information comprises several comfort levels;
describing the preprocessed vehicle data information using a Gaussian mixture model to form representation information, and associating the representation information with the subjective evaluation data information to form associated data information at different comfort levels;
using the associated data information as an input to the comfort model for model training;
in the process of training the comfort model, using different comfort levels as hidden states in the comfort model, and iteratively calculating parameters corresponding to the different comfort levels by using an expectation-maximization algorithm until convergence;
forming, according to different parameters that have been trained, comfort models corresponding to the different comfort levels, wherein a plurality of comfort models form a comfort model library;
using the preprocessed vehicle data information as an input to the comfort model library, and generating a comfort recognition result corresponding to comfort by performing log probability matching calculation on the basis of the comfort model library;
determining a recognition degree of operational disturbance based on a driver's tactile interaction with the autonomous vehicle and a recognition degree of control disturbance based on the driver's recognition of control;
determining a control risk according to the recognition degree of the operational disturbance and the recognition degree of the control disturbance;
determining a take-over risk according to the control risk;
acquiring a surrounding environmental risk;
determining a unified risk of the autonomous vehicle based on the take-over risk and the surrounding environment risk;
determining, according to the unified risk, a set of points with the lowest risk intensity in path planning for the autonomous vehicle, recording the set of points as an optimal safe trajectory, and redefining a finite set of path points that constitute the optimal safe trajectory, to obtain an optimal path result; and
planning an intelligent vehicle path based on the unified risk of autonomous driving by controlling a front wheel turning angle of the vehicle according to the optimal path result.