Proximity detection for automotive vehicles and other systems based on probabilistic computing techniques
A method includes identifying, using at least one processor, a first point associated with an uncertain location of an object in a space and a polynomial curve associated with an uncertain location of a feature in the space. The method also includes determining, using the at least one processor, a probabilistic proximity of the object and the feature. The probabilistic proximity is determined by identifying a second point on the polynomial curve, transforming an uncertainty associated with the polynomial curve into an uncertainty associated with the second point, and identifying the probabilistic proximity of the object and the feature using the first and second points and the uncertainty associated with the second point.
1. A method comprising:
identifying, using at least one processor, a first point associated with an uncertain location of an object in a space and a first uncertainty of the first point;
identifying, using the at least one processor, a polynomial curve associated with an uncertain location of a feature in the space and a second uncertainty of the polynomial curve;
determining, using the at least one processor, a probabilistic proximity of the object and the feature by:
identifying a second point on the polynomial curve, including identifying a location on the polynomial curve where a line between the first point and the location on the polynomial curve is a normal vector of the polynomial curve;
transforming the second uncertainty of the polynomial curve into a third uncertainty of the second point by transforming a covariance matrix of the polynomial curve into a covariance matrix of the second point using eigenvalues of the covariance matrix of the polynomial curve; and
identifying the probabilistic proximity of the object and the feature using the first and second points, the first uncertainty associated with the first point, and the third uncertainty associated with the second point;
identifying at least one action based on the probabilistic proximity; and
performing the at least one action,
wherein the at least one action is associated with a vehicle, and
wherein the at least one action comprises at least one of:
an adjustment to at least one of: a steering of the vehicle, a speed of the vehicle, an acceleration of the vehicle, and a braking of the vehicle; and
an activation of an audible, visible, or haptic warning.
2. The method of claim 1 , wherein the at least one action comprises an adjustment to a trajectory of a robotic arm.
3. The method of claim 1 , wherein identifying the probabilistic proximity comprises:
determining a statistical distance between the first and second points; and
determining a probability that the first point is on the polynomial curve.
4. The method of claim 1 , wherein:
the first point is associated with an object around the vehicle, the object sensed using at least one sensor of the vehicle; and
the polynomial curve is associated with an estimated path of the vehicle, an estimated center location of a lane, or an estimated location of a lane-marking line.
5. The method of claim 1 , wherein:
the first point is associated with an object around an autonomous robot, the object sensed using at least one sensor of the autonomous robot; and
the polynomial curve is associated with an estimated trajectory of a robotic arm of the autonomous robot.
6. An apparatus comprising:
at least one processor configured to:
identify a first point associated with an uncertain location of an object in a space and a first uncertainty of the first point;
identify a polynomial curve associated with an uncertain location of a feature in the space and a second uncertainty of the polynomial curve;
determine a probabilistic proximity of the object and the feature;
identify at least one action based on the probabilistic proximity; and
initiate performance of the at least one action,
wherein the at least one action is associated with a vehicle, and
wherein the at least one action comprises at least one of:
an adjustment to at least one of: a steering of the vehicle, a speed of the vehicle, an acceleration of the vehicle, and a braking of the vehicle; and
an activation of an audible, visible, or haptic warning,
wherein, to determine the probabilistic proximity, the at least one processor is configured to:
identify a second point on the polynomial curve by an identification of a location on the polynomial curve where a line between the first point and the location on the polynomial curve is a normal vector of the polynomial curve;
transform the second uncertainty of the polynomial curve into a third uncertainty of the second point by performance of a transform of a covariance matrix of the polynomial curve into a covariance matrix of the second point using eigenvalues of the covariance matrix of the polynomial curve; and
identify the probabilistic proximity of the object and the feature using the first and second points, the first uncertainty associated with the first point, and the third uncertainty associated with the second point.
7. The apparatus of claim 6 , wherein the at least one action comprises an adjustment to a trajectory of a robotic arm.
8. The apparatus of claim 6 , wherein, to identify the probabilistic proximity, the at least one processor is configured to:
determine a statistical distance between the first and second points; and
determine a probability that the first point is on the polynomial curve.
9. The apparatus of claim 6 , further comprising:
one or more sensors configured to generate sensor measurements;
wherein the first point is associated with an object around the vehicle sensed using at least one of the one or more sensors; and
wherein the at least one processor is further configured to generate the polynomial curve as an estimated path of the vehicle, an estimated center location of a lane, or an estimated location of a lane-marking line.
10. The apparatus of claim 6 , further comprising:
one or more sensors configured to generate sensor measurements;
wherein the first point is associated with an object around an autonomous robot sensed using at least one of the one or more sensors; and
wherein the polynomial curve is associated with an estimated trajectory of a robotic arm of the autonomous robot.
11. A non-transitory machine-readable medium containing instructions that when executed cause at least one processor to:
identify a first point associated with an uncertain location of an object in a space and a first uncertainty of the first point;
identify a polynomial curve associated with an uncertain location of a feature in the space and a second uncertainty of the polynomial curve;
determine a probabilistic proximity of the object and the feature;
identify at least one action based on the probabilistic proximity; and
initiate performance of the at least one action,
wherein the at least one action is associated with a vehicle, and
wherein the at least one action comprises at least one of:
an adjustment to at least one of: a steering of the vehicle, a speed of the vehicle, an acceleration of the vehicle, and a braking of the vehicle; and
an activation of an audible, visible, or haptic warning,
wherein the instructions that when executed cause the at least one processor to determine the probabilistic proximity comprise instructions that when executed cause the at least one processor to:
identify a second point on the polynomial curve by an identification of a location on the polynomial curve where a line between the first point and the location on the polynomial curve is a normal vector of the polynomial curve;
transform the second uncertainty of the polynomial curve into a third uncertainty of the second point by performance of a transform of a covariance matrix of the polynomial curve into a covariance matrix of the second point using eigenvalues of the covariance matrix of the polynomial curve; and
identify the probabilistic proximity of the object and the feature using the first and second points, the first uncertainty associated with the first point, and the third uncertainty associated with the second point.
12. The non-transitory machine-readable medium of claim 11 , wherein the at least one action comprises an adjustment to a trajectory of a robotic arm.
13. The non-transitory machine-readable medium of claim 11 , wherein the instructions that when executed cause the at least one processor to identify the probabilistic proximity comprise instructions that when executed cause the at least one processor to:
determine a statistical distance between the first and second points; and
determine a probability that the first point is on the polynomial curve.
14. The non-transitory machine-readable medium of claim 11 , wherein:
the first point is associated with an object around the vehicle that is sensed using at least one sensor of the vehicle; and
the instructions when executed further cause the at least one processor to generate the polynomial curve as an estimated path of the vehicle, an estimated center location of a lane, or an estimated location of a lane-marking line.
15. The non-transitory machine-readable medium of claim 11 , wherein:
the first point is associated with an object around an autonomous robot that is sensed using at least one sensor of the autonomous robot; and
the instructions when executed further cause the at least one processor to generate the polynomial curve as an estimated trajectory of a robotic arm of the autonomous robot.