Processing a request signal regarding operation of an autonomous vehicle
Among other things, a vehicle drives autonomously on a trajectory through a road network to a goal location based on an automatic process for planning the trajectory without human intervention; and an automatic process alters the planning of the trajectory to reach a target location based on a request received from an occupant of the vehicle to engage in a speed-reducing maneuver.
1. A method comprising:
a planning process associated with a vehicle driving on a road network, the planning process comprising, by computer:
receiving an input signal representing a request for the vehicle to engage in a speed-reducing safety maneuver,
computationally analyzing data to update a target location and a trajectory to the target location, including:
determining a minimum quality threshold for the target location by applying rules to at least one feature of the request for the vehicle to engage in the speed-reducing safety maneuver,
identifying a conflict among the rules that affects the minimum quality threshold,
responsive to the conflict, determining the minimum quality threshold for the target location based on the rule that results in the lowest in the lowest minimum quality threshold, and
selecting the target location from a plurality of target locations by evaluating whether a quality value of the target location satisfies the minimum quality threshold, and
providing output signals to control the vehicle to move to the target location along the trajectory.
2. The method of claim 1 , in which the target location comprises a target stopping place.
3. The method of claim 1 , in which the speed-reducing maneuver comprises stopping the vehicle.
4. The method of claim 1 , comprising receiving the input signal based on a request from an occupant, a teleoperator, or a software or hardware process.
5. The method of claim 1 , in which receiving the input signal comprises receiving at least some of the data from a user interface in the vehicle resulting from an interaction by an occupant of the vehicle.
6. The method of claim 5 , in which the user interface comprises a button, a switch, a pull-cord, a pedal, a pad, a sensor, a whistle, a display, or a voice assistant, or a combination of two or more of them.
7. The method of claim 4 , in which receiving the input signal comprises receiving a command from the teleoperator via telecommunications.
8. The method of claim 4 , in which the input signal is initiated from the software or hardware process based on degradation in performance of the vehicle.
9. The method of claim 8 , in which the degradation in performance comprises degradation in performance of a sensor or a component of the vehicle.
10. The method of claim 4 , in which the input signal is initiated from detection of an event on the road network.
11. The method of claim 1 , in which the data comprises a degree of urgency.
12. The method of claim 11 , in which the degree of urgency has been indicated by an initiator of the input signal.
13. The method of claim 11 , comprising inferring the degree of urgency by an algorithmic analysis on the input signal.
14. The method of claim 13 , in which the algorithmic analysis comprises analysis of a frequency, a volume, a sound, a voice, or a type, or two or more of them, of an oral request.
15. The method of claim 1 , in which the data comprises an expected stopping time interval.
16. The method of claim 15 , in which the expected stopping time interval has been indicated by an initiator of the input signal.
17. The method of claim 15 , in which the expected stopping time interval is inferred by an algorithmic analysis of the input signal.
18. The method of claim 17 , in which the algorithmic analysis comprises an analysis of data associated with the input signal.
19. The method of claim 1 , in which the data comprises traffic data, sensor data, or map data, or two or more of them.
20. The method of claim 1 , in which analyzing the data comprises evaluating quality of one or more target locations.
21. The method of claim 19 , in which the quality is evaluated offline relative to the vehicle.
22. The method of claim 20 , in which the quality is evaluated online when the vehicle is driving.
23. The method of claim 20 , in which evaluating the quality comprises computing a quality based on one or more of the following factors: an emergency condition, a location of the vehicle on the road network, a traffic speed, a traffic volume, a traffic composition, a lane choice, a blockage degree, a sightline from another vehicle, a distance from an intersection, presence of a dedicated lane, terrain, and a road slope.
24. The method of claim 23 , in which one or more of the factors are represented by numerical values.
25. The method of claim 24 , in which each of the numerical values is mapped to a pre-defined range.
26. The method of claim 23 , in which computing the quality comprises assigning weights to one or more of the factors.
27. The method of claim 26 , in which the weights are based on one or more of the following: features of the request, a type of the vehicle, a regulation, a degree of urgency, and an expected stopping time interval.
28. The method of claim 23 , comprising ignoring a target location based on its quality value or information specifying its exclusion.
29. The method of claim 23 , comprising classifying target locations in quality categories.
30. The method of claim 1 , in which the analyzing of the data comprises identifying a region for choosing a target location.
31. The method of claim 30 , in which the region comprises a drivable area or a non-drivable area or both.
32. The method of claim 30 , in which the region comprises an area faced by a forward-facing side of the vehicle.
33. The method of claim 30 , in which the region comprises a shape or a size or both.
34. The method of claim 33 , comprising determining the shape or the size based on a traffic condition or on a degree of urgency or on both.
35. The method of claim 30 , comprising determining the region based on qualities of one or more target locations.
36. The method of claim 30 , comprising expanding the region when no target location is identified.
37. The method of claim 30 , comprising updating the region based on a new location of the vehicle.
38. The method of claim 30 , in which choosing a target location comprises discretizing the region into potential target places.
39. The method of claim 30 , in which choosing a target location comprises using availability data.
40. The method of claim 39 , comprising acquiring the availability data from one or more of the following: a crowd-sourced database, a sensor, a perception process, a historical database, a parking lot database, a parking space database, the vehicle, and another vehicle.
41. The method of claim 1 , wherein computationally analyzing data to update the target location and the trajectory to the target location comprises computing a numeric score for each of the plurality of target locations and comparing the numeric scores of the target locations.
42. The method of claim 41 , wherein computing the numeric score for each of the target locations comprises:
converting metrics of criteria for evaluating the target locations to numbers;
normalizing the numbers to lie within pre-defined ranges; and
using pre-defined weights to account for differences in importance of different criteria.
43. The method of claim 1 , wherein the minimum quality threshold is determined based at least in part on a degree of urgency of the request for the vehicle to engage in the speed-reducing safety maneuver and an expected stopping time interval.
44. The method of claim 1 , further comprising adjusting the minimum quality threshold when no target location is selected.