Method for object avoidance during autonomous navigation

1. A method for autonomous navigation of an autonomous vehicle comprising:

for a first scan cycle:

accessing a first scan image containing data captured by a sensor on the autonomous vehicle at a first time;

identifying a first group of points in the first scan image representing a first object in a field proximal the autonomous vehicle, each point in the first group of points comprising:

a first position of a surface on the first object relative to the autonomous vehicle; and

a first radial velocity of the surface of the first object relative to the sensor;

calculating a first radial velocity of the first object, relative to the autonomous vehicle, at the first time based on a first measure of central tendency of first radial velocities of points in the first group of points; and

characterizing a first direction of uncertainty of motion of the first object at the first time along a first tangential direction perpendicular to the first radial velocity of the first object;

calculating a predicted second direction of uncertainty of motion of the first object at a second time succeeding the first time based on motion of the autonomous vehicle at the first time; and

in response to the second direction of uncertainty differing from the first direction of uncertainty, muting the first object from braking consideration for object avoidance by the autonomous vehicle at the second time.

2. The method of claim 1 :

further comprising accessing a set of predefined motion limit assumptions for generic objects proximal public roads; and

wherein calculating the predicted second direction of uncertainty of motion of the first object at the second time comprises:

calculating a maximum tangential velocity of the first object, toward the autonomous vehicle, that is congruent with the set of predefined motion limit assumptions and first radial velocities of points in the first group of points; and

calculating the predicted second direction of uncertainty of motion of the first object at the second time based on motion of the autonomous vehicle at the first time and the maximum tangential velocity of the first object at the first time.

3. The method of claim 1 :

further comprising:

calculating a critical time offset from the first time;

based on the first scan image, deriving a first location of the first object;

calculating a first correlation between first radial velocities and first positions of points in the first group of points;

based on the first correlation, calculating a first function that relates possible tangential velocities of the first object and possible angular velocities of the first object at the first time; and

calculating a first future state boundary that represents a first ground area accessible to the first object from the first time to the critical time based on:

the first location of the first object at the first time;

first radial velocity of the first object;

possible tangential velocities of the first object and possible angular velocities of the first object at the first time defined by the first function; and

a set of predefined motion limit assumptions for generic objects proximal public roads; and

wherein muting the first object from braking consideration for object avoidance by the autonomous vehicle at the second time comprises muting the first object from braking consideration for object avoidance by the autonomous vehicle at the second time further in response to a location of the autonomous vehicle at the first time falling outside of the first future state boundary by greater than a threshold distance.

4. The method of claim 3 , wherein calculating the critical time comprises:

estimating a stopping duration, for the autonomous vehicle to reach a full stop, based on a speed of the autonomous vehicle at the first time; and

calculating the critical time offset from the first time by the stopping duration.

5. The method of claim 1 , further comprising:

for the first scan cycle:

identifying a second group of points in the first scan image representing a second object in the field, each point in the second group of points comprising:

a second position of a surface on the second object relative to the autonomous vehicle; and

a second radial velocity of the surface of the second object relative to the sensor;

calculating a second radial velocity of the second object, relative to the autonomous vehicle, at the first time based on a second measure of central tendency of second radial velocities of points in the second group of points; and

characterizing a third direction of uncertainty of motion of the second object at the first time along a second tangential direction perpendicular to the second radial velocity of the second object;

calculating a predicted fourth direction of uncertainty of motion of the second object at the second time based on motion of the autonomous vehicle at the first time; and

in response to the predicted fourth direction of uncertainty differing from the third direction of uncertainty by less than a threshold difference, electing a navigational action to modify motion of the autonomous vehicle, relative to the second object, at the second time.

6. The method of claim 5 , wherein electing the navigational action comprises identifying the navigational action that locates the autonomous vehicle at an alternate location relative to the second object at the second time to yield an alternate fourth direction of uncertainty of motion of the second object that differs from the first direction of uncertainty by greater that the threshold difference, the navigational action selected from a group of navigation actions consisting of: a braking input; an acceleration input; and a steering input.

7. The method of claim 5 , wherein electing the navigational action comprises electing the navigational action further in response to the third direction of uncertainty intersecting a first trajectory of the autonomous vehicle at the first time within a threshold distance ahead of a location of the autonomous vehicle at the first time.

8. A method for autonomous navigation of an autonomous vehicle comprising:

for a first scan cycle at the autonomous vehicle:

accessing a first scan image containing data captured by a sensor on the autonomous vehicle at a first time;

identifying a first group of points in the first scan image representing a first object in a field proximal the autonomous vehicle; and

characterizing a first motion of the first object at the first time based on the first group of points;

for a second scan cycle at the autonomous vehicle:

accessing a second scan image containing data captured by the sensor at a second time succeeding the first time;

identifying a second group of points in the second scan image representing the first object; and

characterizing a second motion of the first object at the second time based on the second group of points and the first motion;

characterizing a second uncertainty of the second motion of the first object at the second time;

calculating a predicted third uncertainty of a third motion of the first object at a third time succeeding the second time based on the second motion of the first object at the second time and motion of the autonomous vehicle at the second time; and

in response to the predicted third uncertainty falling below the second uncertainty, muting the first object from braking consideration for object avoidance by the autonomous vehicle at the second time.

9. The method of claim 8 :

wherein characterizing the first motion of the first object at the first time comprises:

calculating a first correlation between first radial velocities and first azimuthal positions of points in the first group of points;

based on the first correlation, calculating a first function that relates possible tangential velocities of the first object and possible angular velocities of the first object at the first time;

wherein characterizing the second motion of the first object at the second time comprises:

calculating a second correlation between second radial velocities and second azimuthal positions of points in the second group of points;

based on the second correlation, calculating a second function that relates possible tangential velocities of the first object and possible angular velocities of the first object at the second time; and

estimating a second range of tangential velocities of the first object and a second range of angular velocities of the first object, relative to the autonomous vehicle, at the second time based on an intersection of the first function and the second function; and

wherein characterizing the second uncertainty of the second motion of the first object at the second time comprises characterizing the second uncertainty of the second motion of the first object at the second time proportional to a ratio of an intersection of the first function and the second function to a union of the first function and the second function.

10. The method of claim 9 , wherein calculating the first correlation comprises:

calculating a first linear trend line through first radial velocities versus first azimuthal positions of points in the first group of points; and

calculating the first correlation based on a first slope of the first linear trend line, the first slope representing a relationship between a first tangential velocity of the first object and a first angular velocity of the first object at the first time.

11. The method of claim 10 :

wherein calculating the second correlation comprises

calculating a second linear trend line through second radial velocities versus second azimuthal positions of points in the second group of points;

calculating the second correlation based on a second slope of the second linear trend line, the second slope representing a relationship between a second tangential velocity of the first object and a second angular velocity of the first object at the second time; and

further comprising:

characterizing a first error of the first linear trend line based on deviation of first radial velocities of points in the first group of points from the first linear trend line;

characterizing a second error of the second linear trend line based on deviation of second radial velocities of points in the second group of points from the second linear trend line;

wherein calculating the first function comprises:

calculating a first line that relates possible tangential velocities of the first object and possible angular velocities of the first object, relative to the autonomous vehicle, at the first time based on the first correlation; and

calculating a first width of the first line based on the first error; and

wherein calculating the second function comprises:

calculating a second line that relates possible tangential velocities of the first object and possible angular velocities of the first object, relative to the autonomous vehicle, at the second time based on the second correlation; and

calculating a first width of the first line based on the first error;

wherein characterizing the second uncertainty of the second motion of the first object at the second time comprises characterizing the second uncertainty of the second motion of the first object at the second time proportional to an area of an intersection of the first line, of the first width, and the second line, of the second width.

12. The method of claim 9 , wherein calculating the predicted third uncertainty of the third motion of the first object at the third time comprises:

calculating a predicted third position of the first object relative to the autonomous vehicle at the third time based on the second motion of the first object at the second time and motion of the autonomous vehicle at the second time;

calculating a predicted third direction of uncertainty of motion of the first object at the third time based on the predicted third position of the first object relative to the autonomous vehicle at the third time; and

calculating the predicted third uncertainty of the third motion of the first object at the third time based on an intersection of the third direction of uncertainty and the second function.

13. The method of claim 12 , wherein calculating the predicted third position of the first object relative to the autonomous vehicle at the third time comprises:

calculating a second radial velocity of the first object, relative to the autonomous vehicle, at the second time based on a first measure of central tendency of second radial velocities of points in the second group of points;

estimating a second tangential velocity of the first object, relative to the autonomous vehicle, at the second time based on a second measure of central tendency of the second range of tangential velocities; and

estimating a second angular velocity of the first object, relative to the autonomous vehicle, at the second time based on a third measure of central tendency of the second range of angular velocities; and

calculating the predicted third position of the first object relative to the autonomous vehicle at the third time based on the second radial velocity of the first object, the second tangential velocity of the first object, the second angular velocity of the first object, and motion of the autonomous vehicle at the second time.

14. The method of claim 8 :

further comprising:

calculating a critical time offset from the second time;

based on the second scan image, deriving a second location of the first object; and

calculating a first future state boundary that represents a second ground area accessible to the second object from the second time to the critical time based on:

the second location of the first object at the second time;

second motion of the first object at the second time; and

a set of predefined motion limit assumptions for generic objects proximal public roads; and

wherein muting the first object from braking consideration for object avoidance by the autonomous vehicle at the third time comprises muting the first object from braking consideration for object avoidance by the autonomous vehicle at the third time further in response to a location of the autonomous vehicle at the second time falling outside of the second future state boundary by greater than a threshold distance.

15. The method of claim 14 , wherein calculating the critical time comprises:

estimating a stopping duration, for the autonomous vehicle to reach a full stop, based on a speed of the autonomous vehicle at the first time; and

calculating the critical time offset from the first time by the stopping duration.

16. The method of claim 8 , further comprising:

for the first scan cycle:

identifying a third group of points in the first scan image representing a second object in the field; and

characterizing a third motion of the second object at the first time based on the third group of points;

for the second scan cycle:

identifying a fourth group of points in the second scan image representing the second object in the field;

characterizing a fourth motion of the second object at the second time based on the second group of points and the third motion;

characterizing a fourth uncertainty of the fourth motion of the second object at the second time;

calculating a predicted fifth uncertainty of a fifth motion of the second object at the third time based on the fourth motion of the second object at the second time and motion of the autonomous vehicle at the second time; and

in response to the predicted fifth uncertainty differing from the fourth uncertainty by less than a threshold difference, electing a navigational action to modify motion of the autonomous vehicle, relative to the second object, at the third time.

17. The method of claim 16 , wherein electing the navigational action comprises identifying the navigational action that locates the autonomous vehicle at an alternate location relative to the second object at the second time to reduce uncertainty of motion of the second object at the third time, the navigational action selected from a group of navigation actions consisting of: a braking input; an acceleration input; and a steering input.

18. The method of claim 16 , wherein electing the navigational action comprises electing the navigational action further in response to the fourth motion of the second object at the second time intersecting a second trajectory of the autonomous vehicle at the second time.

19. A method for autonomous navigation of an autonomous vehicle comprising:

for a first scan cycle at the autonomous vehicle:

accessing a first scan image containing data captured by a sensor on the autonomous vehicle at a first time;

identifying a first group of points in the first scan image representing a first object in a field proximal the autonomous vehicle; and

characterizing a first motion of the first object at the first time based on the first group of points;

characterizing a first uncertainty of the first motion of the first object at the first time;

calculating a predicted second uncertainty of a second motion of the first object at a second time succeeding the first time based on the first motion of the first object at the first time and motion of the autonomous vehicle at the first time; and

in response to the predicted second uncertainty falling below the first uncertainty, muting the first object from braking consideration for object avoidance by the autonomous vehicle at the second time.

20. The method of claim 19 , further comprising:

for a second scan cycle succeeding the first scan cycle:

accessing a second scan image containing data captured by the sensor at a second time;

identifying a second group of points in the second scan image representing the first object; and

characterizing a second motion of the second object at the second time based on the second group of points and the first motion of the second object at the first time;

characterizing a second uncertainty of the second motion of the first object at the second time; and

in response to the second uncertainty exceeding the predicted second uncertainty, electing a navigational action to modify motion of the autonomous vehicle, relative to the second object, at a third time succeeding the second time.