Method and apparatus for determining the speed of a vehicle travelling along a road by processing images of the road

Patent metadata

Field	Value
Publication number	US-12417641-B2
Application number	US-202217942406-A
Country	US
Kind code	B2
Filing date	Sep 12, 2022
Priority date	Sep 10, 2021
Publication date	Sep 16, 2025
Grant date	Sep 16, 2025

Abstract

An apparatus for determining a speed of a vehicle along a road by processing a first image and a second image of the road captured by a camera on the vehicle and comprising respective road marker images of a road marker, the apparatus arranged to: determine a location of the road marker in the first image; predict a location of the road marker in the second image based on the determined location, an estimate of the vehicle speed, and a time period between capture of the images; detect the road marker in a portion of the second image at the predicted location; estimate a distance moved by the vehicle during the time period based on the determined location, and a location of the detected road marker in the portion of the second image; and calculate the speed based on the estimated distance and the time period.

First claim

The invention claimed is:

1. A method of determining a speed of a vehicle travelling along a road by processing pairs of images of the road that have been captured by a camera mounted on the vehicle, each of the pairs of images having a common image coordinate system and including a first image of the road and a second image of the road, the first image having been captured at a first time instant, the second image having been captured at a second time instant that is a predefined time period after the first time instant, and the first image and the second image including respective road marker images of a road marker on the road, the method comprising processing each of the pairs of images by:
determining a location, in the image coordinate system, of the road marker image in the first image by:
generating an LL sub-band image of an Mth level of an (M+1)-level discrete wavelet transform (DWT) decomposition of the first image by iteratively low-pass filtering and down-sampling the first image M times, wherein M is an integer equal to or greater than one;
generating a sub-band image of an (M+1)th level of the (M+1)-level DWT decomposition of the first image by high-pass filtering the LL sub-band image of the Mth level, and down-sampling a result of the high-pass filtering;
generating boundary data indicative of a boundary of the road marker image of the first image, by determining a boundary of a region of pixels of the sub-band image of the (M+1)th level, the region of pixels being surrounded by pixels of different pixel values to the pixel values of the pixels in the region; and
determining the location of the road marker image in the first image by upscaling the boundary data of the region of pixels of the sub-band image of the (M+1)th level by a factor of 2M+1;

predicting a location, in the image coordinate system, of an image of the road marker in the second image based on the determined location of the road marker image, an estimate of the speed of the vehicle, and the predefined time period;
detecting an image of the road marker in a portion of the second image at the predicted location;
estimating a distance by which the vehicle has travelled along the road during the predefined time period based on the determined location of the road marker in the first image, and a location of the detected image of the road marker in the portion of the second image; and
calculating the speed of the vehicle based on the estimated distance and the predefined time period.

2. The method according to claim 1 , wherein a calculation consisting of a cross-correlation between a portion of the first image at the determined location and a portion of the second image at the predicted location is performed, and a result of the calculation is used to detect, and determine the location of, the image of the road marker in the portion of the second image, and estimate the distance by which the vehicle has travelled along the road during the predefined time period.

3. The method according to claim 1 , wherein the estimate of the speed of the vehicle, which is used to predict the location of the image of the road marker in the second image during processing of a pair of the pairs of images, is one of a speed of the vehicle measured by a speedometer of the vehicle and a speed of the vehicle calculated during the processing of one of the pairs of images previously captured by the camera.

4. The method according to claim 1 , wherein the predicted location of the image of the road marker in the second image is predicted using a mapping between a first variable, which is indicative of a position in the image coordinate system of a portion of the image, and second variable, which is indicative of a distance from the vehicle of a portion of the road represented by the portion of the image.

5. The method according to claim 4 , wherein the camera is arranged to capture, as the images:
images of the road to a side of the vehicle as the vehicle travels along the road, and the mapping is a linear mapping; or
images of the road behind or ahead of the vehicle as the vehicle travels along the road, and the mapping is a non-linear mapping.

6. The method according to claim 5 , wherein the mapping is one of a polynomial relating the first variable to the second variable, a look-up table relating the first variable to the second variable, and a polyline relating the first variable to the second variable.

7. The method according to claim 1 , wherein estimating the distance by which the vehicle as travelled along the road during the predefined time period is further based on a mapping between a first variable, which is indicative of a position in the image coordinate system of a portion of the image, and second variable, which is indicative of a distance from the vehicle of a portion of the road represented by the portion of the image.

8. The method according to claim 7 , wherein estimating the distance based on the mapping includes:
determining, using the determined location of the road marker image in the first image and the mapping, a first value indicative of a distance from the vehicle of a portion of the road represented by the determined location of the road marker image in the first image;
determining, using the determined location of the road marker image in the second image and the mapping, a second value indicative of a distance from the vehicle of a portion of the road represented by the determined location of the road marker image in the second image; and
estimating the distance by which the vehicle has travelled along the road during the predefined time period using the first value and the second value.

9. The method according to claim 7 , wherein the predicted location of the image of the road marker in the second image is predicted using the mapping.

10. The method according to claim 1 , wherein a first low-pass filter having a first sequence of filter coefficients that are symmetrical is used in at least one iteration of the iterative process.

11. The method according to claim 10 , wherein the filter coefficients in the first sequence of filter coefficients are set to values in a row of Pascal's triangle having the same number of values as an order of the first low-pass filter.

12. The method according to claim 1 , wherein generating the sub-band image of the (M+1)th level of the (M+1)-level DWT decomposition of the first image includes generating an LH sub-band image of the (M+1)th level of the (M+1)-level DWT decomposition of the first image by one of:
a first process including:
generating a low-pass filtered LL sub-band image by applying a row kernel across the rows of the LL sub-band image of the Mth level, the row kernel corresponding to a low-pass filter;
down-sampling the columns of the low-pass filtered LL sub-band image by a factor of two to generate a down-sampled sub-band image;
generating a high-pass filtered LL sub-band image by applying a column kernel across the columns of the down-sampled sub-band image, the column kernel corresponding to a high-pass filter; and
down-sampling the rows of the high-pass filtered LL sub-band image by a factor of two to generate the LH sub-band image of the (M+1)th level;

a second process including:
generating a high-pass filtered LL sub-band image by applying a column kernel across the columns of the LL sub-band image of the Mth level, the column kernel corresponding to a high-pass filter;
down-sampling the rows of the high-pass filtered LL sub-band image by a factor of two to generate a down-sampled sub-band image;
generating a low-pass filtered sub-band image by applying a row kernel across the rows of the down-sampled sub-band image of the

Assignees

• Aptiv Tech Ltd
• Aptiv Technologies AG

Inventors

• Bogacki Piotr
• Dlugosz Rafal
• Banach Marzena

Classifications

• G06T2207/30252

  Vehicle exterior; Vicinity of vehicle · CPC title

• G06V10/443

  by matching or filtering · CPC title

• G06T7/73

  using feature-based methods · CPC title

• G06T7/168

  involving transform domain methods · CPC title

• G06T7/12

  Edge-based segmentation · CPC title