Ultrasonic system and method for tuning a machine learning classifier used within a machine learning algorithm
A method and system is disclosed for tuning a machine learning classifier. An object class requirement may be provided and include rank thresholds. The object class requirements may also include a range goal that defines a minimum distance from the object the machine learning algorithm should not provide false positive results. A base classifier may be trained using a weighted loss function that includes one or more weight values that are computed using the one or more object class requirements. An output of the weighted loss function may be evaluated using an objective function which may be established using the one or more object class requirements. The one or more weights may also be re-tuned using the weighted loss function if the output of the weighted loss function does not converge within a predetermined loss threshold.
1 . A method for tuning a machine learning classifier, comprising:
receiving one or more object class requirements, wherein the one or more object class requirements include one or more rank thresholds used to rank an object, and wherein the one or more object class requirements include (i) a traversability rank of the object and (ii) a range goal that defines a minimum distance from the object a machine learning algorithm should not provide false positive results;
training a base classifier using a weighted loss function, wherein the weighted loss function includes one or more weight values that are computed using the one or more object class requirements;
evaluating an output of the weighted loss function using an objective function, wherein the objective function is established with the one or more object class requirements;
tuning the one or more weight values using the weighted loss function if the output of the weighted loss function does not converge within a predetermined loss threshold;
maneuvering a vehicle to avoid the object in response to (i) the traversability rank of the object being less than a rank threshold and (ii) a distance between the vehicle and the object being less than the minimum distance; and
forgoing maneuvering the vehicle to avoid the object in response to (i) the traversability rank of the object being less than, greater than or equal to the rank threshold and (ii) the distance between the vehicle and the object being greater than the minimum distance.
2 . The method of claim 1 , wherein the weighted loss function is a cross entropy classification loss function.
3 . The method of claim 1 , wherein the object class requirements are established for an ultra-sonic sensor system.
4 . The method of claim 1 , wherein the object class requirements are established for differing vehicle platforms.
5 . The method of claim 1 , wherein the base classifier is used to determine a true positive output used for classifying the object.
6 . The method of claim 1 , wherein the base classifier is used to determine the false positive results used for classifying the object.
7 . The method of claim 1 , wherein the object class requirements are used to define the object having a specific geometry.
8 . The method of claim 1 , wherein the object class requirements are used to define the rank thresholds for the object having a rigid structure.
9 . The method of claim 1 , wherein the objective function is a squared distance to object error.
10 . The method of claim 1 , wherein the base classifier is used to define a predetermined distance between a vehicle and the object before a vehicle safety system is activated.
11 . The method of claim 1 further comprising forgoing maneuvering the vehicle to avoid the object such that the vehicle traverses the object in response to (i) the traversability rank of the object being greater than the rank threshold and (ii) the distance between the vehicle and the object being less than the minimum distance.
12 . A system for tuning a machine learning classifier, comprising:
one or more controllers configured to:
receive one or more object class requirements, wherein the one or more object class requirements include one or more rank thresholds used to rank an object classified by a machine learning algorithm, and wherein the one or more object class requirements include (i) a traversability rank of the object and (ii) a range goal that defines a minimum distance from the object the machine learning algorithm should not provide false positive results;
train a base classifier using a weighted loss function, wherein the weighted loss function includes one or more weight values that are computed using the one or more object class requirements;
evaluate an output of the weighted loss function using an objective function, wherein the objective function is established with the one or more object class requirements;
tune the one or more weight values using the weighted loss function if the output of the weighted loss function does not converge within a predetermined loss threshold,
maneuver a vehicle to avoid the object in response to (i) the traversability rank of the object being less than a rank threshold and (ii) a distance between the vehicle and the object being less than the mi m distance; and
forgo maneuvering the vehicle to avoid the object in response to (i) the traversability rank of the object being less than, greater than, or equal to the rank threshold and (ii) the distance between the vehicle and the object being greater than the minimum distance.
13 . The system of claim 12 , wherein the weighted loss function is a cross entropy classification loss function.
14 . The system of claim 12 , wherein the object class requirements are established for an ultra-sonic sensor system.
15 . The system of claim 12 , wherein the object class requirements are established for differing vehicle platforms.
16 . The system of claim 12 , wherein the base classifier is used to determine a true positive output used for classifying the object.
17 . The system of claim 12 , wherein the base classifier is used to determine the false positive results used for classifying the object.
18 . The system of claim 12 , wherein the controller is further configured to forgo maneuvering the vehicle to avoid the object such that the vehicle traverses the object in response to (i) the traversability rank of the object being greater than the rank threshold and (ii) the distance between the vehicle and the object being less than the minimum distance.
19 . A method for tuning a machine learning classifier, comprising:
receiving one or more object class requirements, wherein the one or more object class requirements include one or more rank thresholds used to rank an object classified by a machine learning algorithm based on received ultra-sonic sensor data used within a vehicle, and wherein the one or more object class requirements include (i) a traversability rank of the object and (ii) a range goal that defines a minimum distance from the object the machine learning algorithm should not provide false positive results:
training a base classifier using a weighted loss function, wherein the weighted loss function includes one or more weight values that are computed using the one or more object class requirements;
evaluating an output of the weighted loss function using an objective function, wherein the objective function is established with the one or more object class requirements;
tuning the one or more weight values using the weighted loss function if the output of the weighted loss function does not converge within a predetermined loss threshold; and
maneuvering the vehicle to avoid the object in response to (i) the traversability rank of the object being less than a rank threshold and (ii) a distance between the vehicle and the object being less than the minimum distance; and
forgoing maneuvering the vehicle to avoid the object in response to (i) the traversability rank of the object being less than, greater than, or equal to the rank threshold and (ii) the distance between the vehicle and the object being greater than the minimum distance.
20 . The method of claim 19 further comprising forgoing maneuvering the vehicle to avoid the object such that the vehicle traverses the object in response to (i) the traversability rank of the object being greater than the rank threshold and (ii) the distance between the vehicle and the object being less than the minimum distance.