Method to determine distance of an object from an automated vehicle with a monocular device

• US 9,862,318 B2
• Filed: 11/30/2015
• Issued: 01/09/2018
• Est. Priority Date: 12/22/2014
• Status: Active Grant

• Current Assignee: Aptiv Technologies AG
• Original Assignee: Delphi Technologies, Inc. (BorgWarner Incorporated)
• Inventors: Lessmann, Stephanie, Meuter, Mirko, Mueller, Dennis
• Primary Examiner(s): Yentrapati, Avinash
• Application Number:
  US14/953,848
• Publication Number:
  US 20160180531A1
• Time in Patent Office: 771 Days
• Field of Search: None
• CPC Class Codes: B60R 2300/8033 for pedestrian protection
• CPC Class Codes: B60R 2300/804 for lane monitoring
• CPC Class Codes: G01C 3/12 with monocular observation at a single point, e.g. coincidence type G01C3/20 takes precedence
• CPC Class Codes: G06F 18/24155 Bayesian classification
• CPC Class Codes: G06T 7/246 using feature-based methods, e.g. the tracking of corners or segments
• CPC Class Codes: G06T 7/73 using feature-based methods
• CPC Class Codes: G06V 10/255 Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 20/58 Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
• CPC Class Codes: H04N 23/00 Cameras or camera modules comprising electronic image sensors; Control thereof

Abstract

A method of determining the distance of an object from an automated vehicle based on images taken by a monocular image acquiring device. The object is recognized with an object-class by means of an image processing system. Respective position data are determined from the images using a pinhole camera model based on the object-class. Position data indicating in world coordinates the position of a reference point of the object with respect to the plane of the road is used with a scaling factor of the pinhole camera model estimated by means of a Bayes estimator using the position data as observations and under the assumption that the reference point of the object is located on the plane of the road with a predefined probability. The distance of the object from the automated vehicle is calculated from the estimated scaling factor using the pinhole camera model.

Claims

1. A method of determining the distance of an object from an automated vehicle, said method comprising:

equipping an automated vehicle with a monocular image acquiring device, said device located on the automated vehicle at a predefined height above a plane of a road on which the automated vehicle travels;

capturing images of an environment proximate to the vehicle, wherein the images of the environment are taken at time intervals by the device;

classifying, by an image processing system, an object in the images;

associating, by the image processing system, the object with an object-class;

determining, by the image processing system, respective position data from the images using a pinhole camera model and based on the object-class, said position data indicative of a position of a reference point of the object with respect to the plane of the road in world coordinates;

estimating a scaling factor of the pinhole camera model using a Bayes estimator on the position data as observations and under the assumption that the reference point of the object is located on the plane of the road with a predefined probability; and

calculating a distance of the object from the automated vehicle based on the scaling factor using the pinhole camera model, wherein an angle of inclination is used as the position indication that a view ray adopts with respect to the plane of the road, said view ray leading from a center of the automated vehicle and projected perpendicular onto the plane of the road and from a fixed point arranged at a known distance therefrom to the reference point of the object.

2. A method in accordance with claim 1, wherein a further assumption is used as the basis for the Bayes estimator in that a width of the object is associated with the object-class and adopts a standard value associated with of the object-class with a predefined probability.

3. A method in accordance with claim 2, wherein the assumption that the reference point of the object is located on the plane of the road with a predefined probability is modeled by a continuous a priori distribution, whereas the assumption that the width of the object adopts a standard value with a predefined probability is modeled by a discrete a priori distribution.

4. A method in accordance with claim 1, wherein results of a time filtering of the position data are used as observations for the Bayes estimator, wherein mean time values over a respective plurality of successively taken images are formed for the position data.

5. A method in accordance with claim 1, wherein the scaling factor (p) of the pinhole camera is estimated by determining a modal value of a posteriori distribution of the observations.

6. A method in accordance with claim 1, wherein a normal distribution can be fixed around the value zero as an a priori distribution of the angle of inclination.

7. A method in accordance with claim 1, wherein movement of the object is tracked by a tracking process that uses a recursive state switch characterized by a Kalman filter, and that the estimated scaling factor is used as an input for the recursive state switch.

8. A method in accordance with claim 1, wherein the object is associated with one of a plurality of object-classes by a classifier, wherein a separate width range and a separate set of discrete width values are defined for each object-class for fixing the a priori distribution of the width of the object.

9. A method in accordance with claim 8, wherein the object is associated with one of the object-classes “

four-wheeled vehicle” and

“

two-wheeled vehicle”

by the classifier, wherein, an object associated with the object-class “

four-wheeled vehicle”

is associated with one of the subclasses “

passenger vehicle”

, “

van” and

“

truck”

.

10. A method in accordance with claim 8, wherein a basic truth of the classifier is used for solving the equations of the Bayes estimator.

11. A method in accordance with claim 1, wherein a smallest enclosing rectangle is determined for the object with a base located at the lower margin of the smallest enclosing rectangle being selected as the reference point of the object.