Patent Description:
It is known to estimate a length of a trailer towed by a vehicle using radar sensors. A stable estimation of the trailer length may take several minutes.

<CIT> discloses a system that determines the presence and size of a trailer towed by a host-vehicle while the host-vehicle is turning.

The present disclosure provides a detection method and system according to the independent claims. Embodiments are given in the dependent subclaims.

The present invention proposes to solve the above mentioned problem by providing a detection system according to claim <NUM>.

According to other advantageous features of the present invention:.

The present invention also proposes a detection method according to claim <NUM>.

<FIG> illustrates a non-limiting example of a detection system <NUM>, hereafter referred to as the system <NUM>, installed on a host-vehicle <NUM> towing a trailer <NUM>. As will be described in more detail below, the system <NUM> in an improvement over other detection systems because the system <NUM> estimates a trailer-length <NUM> based on detected targets by classifying a distribution of data points and performing a regression on the distribution of the data points. The system <NUM> provides the technical benefit of enabling an adjustment of a blind-zone (not shown) of the host-vehicle <NUM> based on a size of the trailer <NUM>, improving safety for the driver and other vehicles. In one embodiment, the trailer <NUM> is a cargo-trailer that may be an enclosed-type with solid panels, while in another embodiment the cargo-trailer is an open-type with an exposed frame. In yet another embodiment the trailer <NUM> is a boat-trailer. In yet another embodiment the trailer <NUM> is a travel-trailer.

The system <NUM> includes a radar-unit <NUM>. The radar-unit <NUM> is configured to detect objects <NUM> proximate the host-vehicle <NUM>. The radar-unit <NUM> detects a radar-signal that is reflected by the features of the trailer <NUM> towed by the host-vehicle <NUM>, as illustrated in <FIG>. Typical radar-systems on vehicles are capable of only determining a distance <NUM> (i.e. range) and azimuth-angle <NUM> to the target so may be referred to as a two-dimensional (2D) radar-system. Other radar-systems are capable of determining an elevation-angle to the target so may be referred to as a three-dimensional (3D) radar-system. In the non-limiting example illustrated in <FIG>, the 2D radar-unit <NUM> includes a left-sensor 20A and a right-sensor 20B. A radar sensor-system with a similarly configured radar-unit <NUM> is available from Aptiv of Troy, Michigan, USA and marketed as an Electronically Scanning Radar (ESR) or a Rear-Side-Detection-System (RSDS). It is contemplated that the teachings presented herein are applicable to radar-systems with one or more sensor devices. It is also contemplated that the teachings presented herein are applicable to both 2D radar-systems and <NUM>-D radar-systems with one or more sensor devices, i.e. multiple instances of the radar-unit <NUM>. The radar-unit <NUM> is generally configured to detect the radar-signal that may include data indicative of the detected-target present on the trailer <NUM>. As used herein, the detected-target present on the trailer <NUM> may be a feature of the trailer <NUM> that is detected by the radar-unit <NUM> and tracked by a controller-circuit <NUM>, as will be described in more detail below.

Referring back to <FIG>, the system <NUM> also includes the controller-circuit <NUM> in communication with the radar-unit <NUM>. The radar-unit <NUM> may be hardwired to the controller-circuit <NUM> through the host-vehicle's <NUM> electrical-system (not shown), or may communicate through a wireless network (not shown). The controller-circuit <NUM> may include a processor (not shown) such as a microprocessor or other control circuitry such as analog and/or digital control circuitry including an application specific integrated circuit (ASIC) for processing data as should be evident to those in the art. The controller-circuit <NUM> includes a memory <NUM>, including non-volatile-memory, such as electrically-erasable-programmable read-only-memory (EEPROM) for storing one or more routines, thresholds, and captured data. The one or more routines may be executed by the processor to perform steps for detecting the objects <NUM> based on signals received by the controller-circuit <NUM> from the radar-unit <NUM> as described herein. The controller-circuit <NUM> is configured to determine that the trailer <NUM> is being towed by the host-vehicle <NUM> (i.e. determine a trailer-presence) using the known methods of zero-range-rate (ZRR) detection of targets that will be understood by those in the art.

<FIG> illustrates a plot of multiple radar-sensors 20A, 20B data acquisition cycles that locate the ZRR targets along a host-vehicle-longitudinal-axis <NUM> and a host-vehicle-lateral-axis <NUM>. The trailer <NUM> has a known-trailer-length of <NUM>. Each data acquisition cycle consists of <NUM>-detections per radar-sensor 20A, 20B within a time interval of <NUM>-milliseconds (<NUM>), or a total of <NUM>-detections for the two radar-sensors 20A and 20B. The origin of the plot is located at a center of the host-vehicle's <NUM> front-bumper (not specifically shown).

<FIG> illustrates a detection-distribution <NUM> determined by the controller-circuit <NUM> that is characterized by a longitudinal-distribution of ZRR detections associated with the trailer <NUM> towed by the host-vehicle <NUM>. That is, the detection-distribution <NUM> is a plot of the groups of the ZRR targets from <FIG> along the host-vehicle-longitudinal-axis <NUM> only. Note that the x-axis for the plot in <FIG> is the distance <NUM> from a rear-end of the host-vehicle <NUM>, and not the distance from the front-bumper as illustrated in <FIG>. The controller-circuit <NUM> determines the detection-distribution <NUM> in a finite time-period, which in the examples illustrated herein, is about <NUM>-minute in duration.

The detection-distribution <NUM> is characterized by groups of ZRR targets detected within sequential predetermined length-intervals extending for a predetermined-distance <NUM> behind the host-vehicle <NUM>. In the examples illustrated herein, the groups represent the ZRR targets detected in increments of <NUM>-meters (<NUM>) extending from the rear-end of the host-vehicle <NUM> for the distance <NUM> of up to about <NUM>. For example, every <NUM> points along the x-axis of the plot in <FIG> represents <NUM> of distance <NUM> from the rear-end of the <NUM> long host-vehicle <NUM>. The Y-axis in <FIG> represents the cumulative number of detections in a group. Some of the groups represent real-objects and others represent phantom-objects. Experimentation by the inventors has discovered that the predetermined length-intervals of less than or equal to about <NUM>-meters provides an adequate balance between memory <NUM> utilization and accuracy of the trailer-length <NUM> determination. The predetermined-distance <NUM> of <NUM> is selected as representative of a typical longest-trailer that may be legally towed on roadways by the host-vehicle <NUM>. However, the predetermined-distance <NUM> may be user defined and adjusted to other distances <NUM> in excess of <NUM>.

Referring again to <FIG>, the controller-circuit <NUM> is further configured to determine a trailer-classification <NUM> based on a comparison of the detection-distribution <NUM> and longitudinal-distribution-models <NUM> stored in the controller-circuit <NUM>. The trailer-classification <NUM> is indicative of a dimension of the trailer <NUM> and includes a first-class <NUM> (e.g. trailers <NUM> having a trailer-length <NUM> between <NUM> and <NUM>), a second-class <NUM> (e.g. trailers <NUM> having the trailer-length <NUM> between <NUM> and <NUM>), and a third-class <NUM> (e.g. trailers <NUM> having the trailer-length <NUM> between <NUM> and <NUM>). The longitudinal-distribution-models <NUM> are trained (i.e. calibrated or optimized) to determine the trailer-classification <NUM> using known data (i.e. training-data collected from the detection-distributions <NUM> of various trailers <NUM> with known-trailer-lengths) using a machine learning algorithm with Supervised Learning (e.g., "examples" x with "labels" y), wherein the x-training-data are the cumulative-detections at each of the predetermined length-intervals (i.e., every <NUM> up to <NUM>), and the y-training-data are the associated known-trailer-classification (i.e., first-class <NUM>, second-class <NUM>, and third-class <NUM>). The machine learning algorithm creates a model based on the training-data that determines the trailer-classification <NUM>. Any applicable machine learning algorithm may be used to develop the longitudinal-distribution-models <NUM>. One such machine learning algorithm is the MATLAB ® "fitensemble()" by The MathWorks, Inc. of Natick, Massachusetts, USA. The prediction of the trailer-classification <NUM> based on the longitudinal-distribution-models <NUM> and the detection-distribution <NUM> is executed using the MATLAB ® "predict()" function, by The MathWorks, Inc. of Natick, Massachusetts, USA, or similarly known algorithm. In the example illustrated in <FIG>, the trailer <NUM> is classified by the system <NUM> as a first-class <NUM> trailer <NUM>.

The controller-circuit <NUM> determines the trailer-length <NUM> based on the detection-distribution <NUM> and the trailer-classification <NUM> by applying regression-models <NUM> to the detection-distribution <NUM>. The regression-models <NUM> are associated with each of the trailer-classifications <NUM> and are stored in the controller-circuit <NUM>. Each trailer-classification <NUM> has associated with it a unique regression-model <NUM> to more accurately determine the trailer-length <NUM>. The regression-models <NUM> are trained to determine the trailer-length <NUM> using known training-data using the same machine learning algorithm with supervised learning as described above, wherein the x-training-data are the cumulative-detections at each of the predetermined length-intervals (i.e., every <NUM>) and the y-training-data are the associated known-trailer-lengths. The regression-models <NUM> are developed using the MATLAB ® "fitrensemble()" by The MathWorks, Inc. of Natick, Massachusetts, USA, and use <NUM> iterations to converge on the model having an acceptable error or residual values. The controller-circuit <NUM> uses the detection-distribution <NUM> as input into the regression-model <NUM> to estimate or predict the trailer-length <NUM>. The prediction of the trailer-length <NUM> is also executed using the MATLAB ® "predict()" function, by The MathWorks, Inc. of Natick, Massachusetts, USA, or similarly known algorithm, based on the regression-model <NUM> and the detection-distribution <NUM>.

In the example illustrated in <FIG> the trailer-length <NUM> is predicted to be <NUM> compared to the known length of <NUM>. <FIG> illustrate the trailer <NUM> classified as the second-class <NUM> with the known length of <NUM>, and the trailer-length <NUM> predicted by the system <NUM> of <NUM>. <FIG> illustrate the trailer <NUM> classified as the third-class <NUM> with the known length of <NUM>, and trailer-length <NUM> predicted by the system <NUM> of <NUM>. Experimentation by the inventors has discovered that the prediction of the trailer-length <NUM> using the above system <NUM> has been shown to reduce the error to less than <NUM>% of the known-trailer-length.

<FIG> illustrates an example of an iterative process for determining the longitudinal-distribution-models <NUM> using the MATLAB ® functions described above and known training-data. Iteration-<NUM> initially applies a linear function representing a mean value of the training-data, after which an error residual (i.e. a difference between the mean-value and the particular data-point) is calculated. The plot of the error residual from Iteration-<NUM> is fit with a step-function which is used to update the linear function in iteration-<NUM>. The iterative process continues for N iterations (preferably N = <NUM>) until the resulting longitudinal-distribution-model <NUM> is characterized as having the error residual close to zero.

<FIG> is a flow chart illustrating another embodiment of a detection method <NUM>.

Step <NUM>, DETECT OBJECTS, includes detecting objects <NUM> proximate a host-vehicle <NUM> with a radar-unit <NUM> as described above.

Step <NUM>, DETERMINE DETECTION-DISTRIBUTION, includes determining the detection-distribution <NUM> based on the radar-unit <NUM> with the controller-circuit <NUM> in communication with the radar-unit <NUM>. The detection-distribution <NUM> is characterized by a longitudinal-distribution of zero-range-rate detections associated with a trailer <NUM> towed by the host-vehicle <NUM>. The detection-distribution <NUM> is determined in a finite time-period of about <NUM>-minute. The controller-circuit <NUM> detects the groups of zero-range-rate detections within the sequential predetermined length-intervals extending for a predetermined-distance <NUM> behind the host-vehicle <NUM> as described above.

Step <NUM>, DETERMINE TRAILER-CLASSIFICATION, includes determining the trailer-classification <NUM>, with the controller-circuit <NUM>, based on a comparison of the <NUM> detection-distribution <NUM> and the longitudinal-distribution-models <NUM> stored in the controller-circuit <NUM>. The trailer-classifications <NUM> include a first-class <NUM>, a second-class <NUM>, and a third-class <NUM> as described above.

Step <NUM>, DETERMINE TRAILER-LENGTH, includes determining the trailer-length <NUM> of the trailer <NUM>, with the controller-circuit <NUM>, based on the detection-distribution <NUM> and the trailer-classification <NUM> as described above. The trailer-length <NUM> is determined by regression-models <NUM> stored in the memory <NUM> of the controller-circuit <NUM> as described above. Each trailer-classification <NUM> has a unique regression-model <NUM>.

In another embodiment a first device includes one or more processors, a memory, and one or more programs stored in memory, the one or more programs including instructions for performing the method described above.

In yet another embodiment, a non-transitory computer-readable storage-medium comprising one or more programs for execution by one or more processors of a first device, the one or more programs including instructions which, when executed by the one or more processors, cause the first device to perform the method described above.

Accordingly, a detection system <NUM> (the system <NUM>), a controller-circuit <NUM> for the system <NUM>, and a detection method <NUM> are provided. The system <NUM> is an improvement over other detection systems because the system <NUM> estimates the trailer-length <NUM> in a time-period of less than <NUM>-minute and reduces a measurement error.

Claim 1:
A detection method (<NUM>) comprising:
determining (<NUM>), by a controller circuit (<NUM>) and with a radar unit (<NUM>) of a host vehicle (<NUM>), a distribution of zero-range-rate detections (<NUM>) detected (<NUM>) from behind the host vehicle; and
determining (<NUM>), by the controller circuit (<NUM>), based on the distribution of zero-range-rate detections, a length (<NUM>) of a trailer (<NUM>) being towed behind the host vehicle, the method being characterized by:
inputting the distribution of zero-range-rate detections (<NUM>) into a model that predicts the trailer classification from a plurality of different trailer classifications using a unique regression model for each of the different trailer classification; and
determining, based in part on the trailer classification predicted by the model, the length of the trailer.