Path construction method and apparatus, terminal, and storage medium

Patent metadata

Field	Value
Publication number	US-2024133696-A1
Application number	US-202118276332-A
Country	US
Kind code	A1
Filing date	Feb 8, 2021
Priority date	Feb 8, 2021
Publication date	Apr 25, 2024
Grant date	—

Abstract

The present disclosure discloses a path construction method, device, terminal and storage medium, and the method comprises: acquiring vehicle travel state information and an initial image of a surrounding environment of a preset driving path corresponding to the vehicle travel state information in real time when the vehicle is traveling on the preset driving path; calculating a target top view corresponding to the initial image via a nonlinear difference correction algorithm according to the initial image; inputting the target top view into a preset deep learning model, and classifying pixel points of the target top view input into the preset deep learning model to obtain a partitioned image, the partitioned image being an image after partitioning the target top view; scanning the partitioned image to recognize a travelable region of the vehicle; generating a path trajectory corresponding to the vehicle travel state information based on the travelable region.

First claim

1 . A path construction method comprising:
acquiring vehicle travel state information and an initial image of a surrounding environment of a preset driving path corresponding to the vehicle travel state information in real time when the vehicle is traveling on the preset driving path; calculating a target top view corresponding to the initial image via a nonlinear difference correction algorithm according to the initial image; inputting the target top view into a preset deep learning model, and classifying pixel points of the target top view input into the preset deep learning model to obtain a partitioned image, the partitioned image comprising a travelable region image and a non-travelable region image; scanning the partitioned image to recognize a travelable region of the vehicle; and generating a path trajectory corresponding to the vehicle travel state information based on the travelable region, the path trajectory being one of the preset driving paths.

2 . The path construction method according to claim 1 , wherein generating a path trajectory corresponding to the vehicle travel state information based on the travelable region further comprises:
constructing a map corresponding to the preset driving path based on the path trajectory.

3 . The path construction method according to claim 1 , wherein the acquiring vehicle travel state information and an initial image of a surrounding environment of a preset driving path corresponding to the vehicle travel state information in real time comprises:
acquiring the vehicle travel state information when the vehicle is traveling on the preset driving path in real time, the vehicle travel state information including a travel strategy when the vehicle is traveling on the preset driving path and a driving habit of a driver; and acquiring the initial image of the surrounding environment of the preset driving path when the vehicle is traveling on the preset driving path in real time according to the travel strategy and the driving habit of the driver.

4 . The path construction method according to claim 3 , wherein the travelable region includes a travelable road and a travelable intersection, and acquiring the travel strategy when the vehicle is traveling on the preset driving path comprises:
acquiring a vehicle speed and a steering wheel angle of the vehicle in real time; determining a driving range and a heading angle of the vehicle based on the vehicle speed and the steering wheel angle of the vehicle; and determining the travel strategy of the vehicle from the driving range and the heading angle of the vehicle, the travel strategy of the vehicle including the driving range on the travelable road and whether to turn at the travelable intersection.

5 . The path construction method according to claim 3 , wherein the travelable region includes a travelable road and a travelable intersection; acquiring the driving habit of the driver when the vehicle is traveling on the preset driving path comprises:
acquiring operation data when the vehicle is traveling on the preset driving path in real time; pre-processing the operation data of the vehicle to obtain target operation data; inputting the target operation data into a cyclic neural network, and extracting features of the target operation data from the cyclic neural network; inputting the features into a full connection network, and predicting a driving habit of a driver when the vehicle is traveling on the preset driving path; and the driving habit including a traveling speed on a travelable road and a steering angle at a travelable intersection.

6 . The path construction method according to claim 1 , wherein the generating a path trajectory corresponding to the vehicle travel state information based on the travelable region further comprises:
when the vehicle is traveling on the preset driving path again, acquiring vehicle travel state information and an initial image of a surrounding environment of a preset driving path corresponding to the vehicle travel state information in real time;
calculating a target top view corresponding to the initial image via a nonlinear difference correction algorithm according to the initial image;
inputting the target top view into a preset deep learning model, and classifying pixel points of the target top view input into the preset deep learning model to obtain a partitioned image, the partitioned image comprising a travelable region image and a non-travelable region image; scanning the partitioned image to recognize a travelable region of the vehicle; and
generating a current path trajectory corresponding to the vehicle travel state information based on the travelable region, the path trajectory being one of the preset driving paths.

7 . The path construction method according to claim 6 , characterized in that generating a current path trajectory corresponding to the vehicle travel state information based on the travelable region further comprises:
performing multi-trajectory fusion on the current path trajectory and the path trajectory obtained the last time, and reconstructing a map corresponding to the preset driving path.

8 . The path construction method according to claim 7 , wherein before performing multi-trajectory fusion on the current path trajectory and the path trajectory obtained the last time, and reconstructing a map corresponding to the preset driving path, further comprising:
judging whether the coincidence degree between the current path trajectory and the path trajectory obtained the last time is greater than or equal to a preset first threshold value; if the coincidence degree between the current path trajectory and the path trajectory obtained the last time is greater than or equal to the preset first threshold value, fusing the current path trajectory and the path trajectory obtained the last time.

9 . The path construction method according to claim 8 , further comprising:
if the coincidence degree between the current path trajectory and the path trajectory obtained the last time is less than a preset first threshold value, determining whether a matching degree between the current path trajectory and the preset driving path is less than a matching degree between the path trajectory obtained the last time and the preset driving path; and if so, regenerating the current path trajectory.

10 . The path construction method according to claim 1 , wherein calculating a target top view corresponding to the initial image via a nonlinear difference correction algorithm according to the initial image comprises:
obtaining a target image based on the initial image, the target image comprising a top view of a region image coinciding with a region in which the target top view is located; acquiring a number of times the target image appears; judging whether the number of times the target image appears is greater than or equal to a preset second threshold value; if so, extracting a feature point of the region image in each of the target images; and matching the feature points of each of the region images to reconstruct the target top view.

11 . The path construction method according to claim 1 , wherein calculating a target top view corresponding to the initial image via a nonlinear difference correction algorithm according to the initial image further comprises:
acquiring a corresponding relationship between a top view of the initial image and the initial image based on the non-linear difference correction algorithm, the corresponding relationship comprising corresponding coordinate points between the top view of the initial image and the initial image; acquiring a target coordinate point from the initial image based on t

Assignees

• Geely Holding Group Co Ltd
• Ningbo Geely Automobile Res And Development Co Ltd

Inventors

• Zhang Jianfeng
• Lin Xiao
• Yuwen Zhiqiang

Classifications

• G06N3/08

  Learning methods · CPC title

• G01C21/3804

  Creation or updating of map data · CPC title

• G01C21/3492

  employing speed data or traffic data, e.g. real-time or historical (traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968) · CPC title

• G01C21/3484

  Personalized, e.g. from learned user behaviour or user-defined profiles · CPC title

• G01C21/3602

  Input other than that of destination using image analysis, e.g. detection of road signs, lanes, buildings, real preceding vehicles using a camera · CPC title