Gap measurement for vehicle convoying

Abstract

A variety of methods, controllers and algorithms are described for identifying the back of a particular vehicle (e.g., a platoon partner) in a set of distance measurement scenes and/or for tracking the back of such a vehicle. The described techniques can be used in conjunction with a variety of different distance measuring technologies including radar, LIDAR, camera based distance measuring units and others. The described approaches are well suited for use in vehicle platooning and/or vehicle convoying systems including tractor-trailer truck platooning applications.

What is claimed is: 
     
         1 . A method of identifying a position of a back of a first vehicle using radar scenes received from a radar unit on a second vehicle, the comprising:
 a) estimating a position of the first vehicle relative to a second vehicle;   b) receiving a radar scene sample from the radar unit on the second vehicle, the radar scene including a set of zero or more detected radar object points, each radar object point corresponding to a detected object;   c) identifying first vehicle radar point candidates within the set of received detected radar object points;   d) categorizing the first vehicle radar point candidates based on distance that the detected objects that they represent are from the estimated first vehicle position;   e) repeating steps (a)-(d) a multiplicity of times, whereby the categorized first vehicle radar point candidates include candidates from multiple sequential radar scene samples; and   f) identifying the position of the back of the first vehicle based at least in part of the categorization of the first vehicle radar point candidates.   
     
     
         2 . A method as recited in  claim 1  further comprising identifying a bounding box around the estimated position of the first vehicle, wherein radar object points within the set of received detected radar object points that are not located within the bounding box are not considered first vehicle radar point candidates. 
     
     
         3 . A method as recited in  claim 2  wherein the bounding box defines a region that exceeds a maximum expected size of the first vehicle. 
     
     
         4 . A method as recited in  claim 1  further comprising estimating a speed of the first vehicle relative to the second vehicle, the estimated relative speed having an associated speed uncertainty, wherein radar object points within the set of detected radar object points that correspond to detected objects that are moving at a relative speed that is not within the speed uncertainty of the estimated speed are not considered first vehicle radar point candidates. 
     
     
         5 . A method as recited in  claim 1  wherein the identified back of the first vehicle or an effective vehicle length that is determined based at least in part on the identified back of the first vehicle is used in the control of the second vehicle. 
     
     
         6 . A method as recited in  claim 1  wherein steps (a)-(c) are repeated at a sample rate of at least 10 Hertz. 
     
     
         7 . A method as recited in  claim 1  wherein categorizing the first vehicle radar point candidates includes populating a histogram with the first vehicle radar point candidates, the histogram including a plurality of bins, each bin representing a longitudinal distance range relative to the estimated position of the first vehicle. 
     
     
         8 . A method as recited in  claim 7  wherein the identification of the back of the first vehicle is only done after the histogram contains at least a predetermined number of first vehicle radar point candidates. 
     
     
         9 . A method as recited in  claim 7  further comprising applying a clustering algorithm to the first vehicle radar point candidates to identify one or more clusters of first vehicle radar point candidates. 
     
     
         10 . A method as recited in  claim 9  wherein the clustering algorithm is a modified mean shift algorithm. 
     
     
         11 . A method as recited in  claim 9  wherein the cluster located closest to the second vehicle is selected to represent the back of the first vehicle. 
     
     
         12 . A method as recited in  claim 9  wherein the cluster located closest to the second vehicle that includes at least a predetermined threshold percentage or number of first vehicle radar point candidates is selected to represent the back of the first vehicle. 
     
     
         13 . A method as recited in  claim 12  wherein the predetermined threshold percentage is at least 10% of first vehicle radar point candidates in the histogram. 
     
     
         14 . A method as recited in  claim 12  wherein the predetermined number of first vehicle radar point candidates is a number that is at least 40. 
     
     
         15 . A method as recited in  claim 1  further comprising determining an effective length of the first vehicle based at least in part on the identified back of the vehicle. 
     
     
         16 . A method as recited in  claim 1  wherein Kalman filtering is used to estimate the position of the first vehicle. 
     
     
         17 . A method as recited in  claim 7  further comprising comparing properties of the histogram or mean shift clusters derived from the histogram to a known set of data representative of a target partner vehicle to verify whether the first vehicle is the target partner vehicle. 
     
     
         18 . A method as recited in  claim 7  further comprising comparing properties of the histogram or mean shift clusters derived from the histogram to a radar scene received when the back of the first vehicle is not within the radar unit's field of view but a portion of the first vehicle is within the radar unit's field of view to help determine a current relative position of the first vehicle. 
     
     
         19 . A method as recited in  claim 1  wherein the first and second vehicles are trucks. 
     
     
         20 . A method as recited in  claim 19  wherein the first vehicle is a tractor-trailer truck. 
     
     
         21 . A method of identifying a position of a back of a first vehicle using scenes received from a distance measuring unit on a second vehicle, the comprising:
 a) estimating a position of the first vehicle relative to a second vehicle;   b) receiving a scene sample from the distance measuring unit on the second vehicle, the scene including a set of zero or more detected object points, each object point corresponding to a detected object;   c) identifying first vehicle point candidates within the set of received detected object points;   d) categorizing the first vehicle point candidates based on distance that the detected objects that they represent are from the estimated first vehicle position;   e) repeating steps (a)-(d) a multiplicity of times, whereby the categorized first vehicle point candidates include candidates from multiple sequential distance measuring unit scene samples; and   f) identifying the position of the back of the first vehicle based at least in part of the categorization of the first vehicle point candidates.   
     
     
         22 . A method of tracking a specific lead vehicle using a distance measurement unit mounted on a trailing vehicle, the method comprising:
 (a) obtaining a current sample from the distance measurement unit, the current sample including a set of zero or more object points;   (b) obtaining a current estimate of a state of the lead vehicle corresponding to the current sample, wherein the current estimate of the state of the lead vehicle has an associated state uncertainty and does not take into account any information from the current sample;   (c) determining whether any of the object points match the estimated state of the lead vehicle within the state uncertainty; and   (d) when at least one of the object points matches the estimated state of the lead vehicle within the state uncertainty, selecting the matching object point that best matches the estimated state of the lead vehicle as a measured state of the lead vehicle, and using the measured state of the lead vehicle in the determination of a sequentially next estimate of a state of the lead vehicle corresponding to a sequentially next sample; and   repeating steps (a)-(d) a multiplicities of times to thereby track the lead vehicle.   
     
     
         23 . A method as recited in  claim 22  wherein the current state estimate includes a plurality of state parameters, the state parameters including a position parameter indicative of a position of the lead vehicle relative to the trailing vehicle and a speed parameter indicative of a velocity of the lead vehicle relative to the trailing vehicle. 
     
     
         24 . A method as recited in  claim 22  further comprising at least partially automatically controlling the trailing vehicle to maintain a desired gap between the lead vehicle and the trailing vehicle and wherein each selected object point has an associated longitudinal distance from the distance measurement unit, and wherein the associated longitudinal distance is treated by a gap controller responsible for maintaining the desired gap as a current measured longitudinal distance from the distance measurement unit to the back of the lead vehicle. 
     
     
         25 . A method as recited in  claim 22  wherein:
 each sample indicates a position of each of the object points; and 
 each current estimate of the state of the lead vehicle includes a current estimate of the position of the lead vehicle and has an associated position uncertainty; 
 the selected matching object point must match the estimated position of the lead vehicle within the position uncertainty. 
 
     
     
         26 . A method as recited in  claim 25  wherein the current estimate of the position of the lead vehicle estimates the current position of a back of the lead vehicle. 
     
     
         27 . A method as recited in  claim 25  wherein the estimated position of the lead vehicle is a relative position relative to the trailing vehicle. 
     
     
         28 . A method as recited in  claim 25  wherein:
 each sample also indicates a relative velocity of each of the object points; and 
 each current estimate of the state of the lead vehicle further includes a current estimate of a relative velocity of the lead vehicle and has an associated velocity uncertainty; 
 the selected matching object point must both (i) match the estimated position of the lead vehicle within the position uncertainty, and (ii) match the estimated velocity of the lead vehicle within the velocity uncertainty. 
 
     
     
         29 . A method as recited in  claim 22  wherein when none of the object points in a particular sample match the estimated state of the lead vehicle within the state uncertainty, then the state uncertainty is increased for the sequentially next estimate of the state of the lead vehicle. 
     
     
         30 . A method as recited in  claim 29  wherein the estimate state includes a plurality of state parameters, the state parameters including a position parameter, a speed parameter and an orientation parameter. 
     
     
         31 . A method as recited in  claim 22  further comprising:
 periodically receiving global navigation satellite systems (GNSS) position updates based at least in part on detected GNSS positions of the lead and trailing vehicles; and 
 each time a GNSS position update is received, updating the estimated state of the lead vehicle and the state uncertainty based on such GNSS position update. 
 
     
     
         32 . A method as recited in  claim 22  further comprising:
 periodically receiving vehicle speed updates based at least in part on detected wheel speeds of the lead and trailing vehicles; and 
 each time a vehicle speed update is received, updating the estimated state of the lead vehicle and the state uncertainty based on such vehicle speed update. 
 
     
     
         33 . A method as recited in  claim 22  wherein steps (a)-(d) are repeated at a sample rate of at least 10 Hertz. 
     
     
         34 . A method as recited in  claim 22  wherein Kalman filtering is used to estimate the state of the lead vehicle and the associated state uncertainty. 
     
     
         35 . A method as recited in  claim 22  wherein the estimated state of the lead vehicle includes an estimated position of the back of the lead vehicle and the selected matching object point is considered a measurement of the relative position of the back of the lead vehicle. 
     
     
         36 . A method as recited in  claim 22  wherein a controller on the trailing vehicle maintains a profile of point clusters representative of the lead vehicle and the selected matching point corresponds to one of the point clusters. 
     
     
         37 . A method as recited in  claim 22  wherein the lead and trailing vehicles are trucks involved in a platoon. 
     
     
         38 . A method as recited in  claim 22  wherein the distance measurement unit is a radar unit. 
     
     
         39 . A method of tracking a specific lead vehicle using a radar unit mounted on a trailing vehicle, the method comprising:
 (a) obtaining a current radar sample from the radar unit, the current radar sample including a set of zero or more radar object points, each radar object point indicating a relative a position of such radar object point relative to the radar unit;   (b) obtaining a current estimate of a state of the lead vehicle corresponding to the current radar sample, wherein the current estimate of the state of the lead vehicle has an associated state uncertainty and includes a current estimate of the position of a back of the lead vehicle relative to the radar unit, wherein the current estimate of the position of the back of lead vehicle has an associated position uncertainty that is at least a part of the state uncertainty;   (c) determining whether any of the radar object points match the estimated state of the lead vehicle within the state uncertainty, wherein to match the estimated state of the lead vehicle within the state uncertainty, a matching radar object point must match the estimated position of the back of the lead vehicle within the position uncertainty; and   (d) when at least one of the radar object points matches the estimated state of the lead vehicle within the state uncertainty, selecting the matching radar object point that best matches the estimated state of the lead vehicle as a measured state of the lead vehicle, and using the measured state of the lead vehicle in the determination of a sequentially next estimate of a state of the lead vehicle corresponding to a sequentially next radar sample;   repeating steps (a)-(d) a multiplicities of times;   periodically receiving vehicle global navigation satellite systems (GNSS) position updates based at least in part on detected GNSS positions of the lead and trailing vehicles;   each time a vehicle GNSS position update is received, updating the estimated state of the lead vehicle and the state uncertainty based on such vehicle GNSS position update;   periodically receiving vehicle speed updates based at least in part on detected wheel speeds of the lead and trailing vehicles; and   each time a vehicle speed update is received, updating the estimated state of the lead vehicle and the state uncertainty based on such vehicle speed update; and   at least partially automatically controlling the trailing vehicle to maintain a desired gap between the lead vehicle and the trailing vehicle based at least in part on an aspect of the measured state of the lead vehicle.   
     
     
         40 . A method as recited in  claim 39  wherein:
 each radar sample also indicates a relative velocity of each of the radar object points; and 
 each current estimate of the state of the lead vehicle further includes a current estimate of a relative velocity of the lead vehicle and has an associated velocity uncertainty; 
 the selected matching radar object point must both (i) match the estimated position of the lead vehicle within the position uncertainty, and (ii) match the estimated velocity of the lead vehicle within the velocity uncertainty. 
 
     
     
         41 . A method as recited in  claim 39  wherein:
 when none of the radar object points in a particular radar sample match the estimated position of the lead vehicle within the position uncertainty, then the position uncertainty is increased for the sequentially next estimate of the position of the lead vehicle; and 
 when none of the radar object points in a particular radar sample match an estimated velocity of the lead vehicle within an velocity uncertainty, then the velocity uncertainty is increased for the sequentially next estimate of the position of the lead vehicle. 
 
     
     
         42 . A method as recited in  claim 39  wherein the estimated state of the lead vehicle includes an estimated position of the back of the lead vehicle and the selected matching radar object point is considered a measurement of the relative position of the back of the lead vehicle.