Planning for unknown objects by an autonomous vehicle
Among other things, a world model is maintained of an environment of a vehicle. A hypothetical object in the environment that cannot be perceived by sensors of the vehicle is included in the world model.
1. An apparatus comprising:
an autonomous vehicle comprising:
a) steering, acceleration, deceleration, or gear selection devices or combinations of them configured to effect movement of the autonomous vehicle on a road network; and
b) a computer having a processor to execute a process (i) to generate commands to the steering, acceleration, deceleration, or gear selection devices or combinations of them to move the autonomous vehicle in accordance with driving decisions; and (ii) to update the commands (a) in response to data representing motion characteristics of a hypothetical vehicle being driven on the road network and (b) based on discretized points of a lane that cannot be perceived by one or more sensors.
2. The apparatus of claim 1 , in which data representing motion characteristics of a hypothetical vehicle is probabilistically derived based on vehicles previously observed in an environment of the autonomous vehicle.
3. The apparatus of claim 1 , in which regions perceivable and unperceivable by the sensors are separated by a boundary.
4. The apparatus of claim 3 , in which the computer detects the boundary.
5. The apparatus of claim 4 , in which detecting of the boundary is based on using data from the sensors to distinguish an perceivable ground from a foreground that obscures a portion of the ground.
6. The apparatus of claim 5 , in which the sensors comprise sensors onboard the autonomous vehicle.
7. The apparatus of claim 5 , in which the sensors comprise sensors offboard the autonomous vehicle.
8. The apparatus of claim 1 , in which the computer generates the data based on known objects perceived by the sensors.
9. The apparatus of claim 8 , in which generating of the data comprises querying traffic lane information from a road network database.
10. The apparatus of claim 8 , in which generating of the data comprises using stored data to infer a possible location of the hypothetical vehicle.
11. The apparatus of claim 8 , in which generating of the data comprises determining a location of the autonomous vehicle based on a road network database and the sensors.
12. The apparatus of claim 8 , in which generating of the data comprises querying traffic lane information from a database and discretizing the traffic lane into discretized points.
13. The apparatus of claim 8 , in which generating of the data comprises (a) generating a representative shape at a discretized point of the lane that cannot be perceived by sensors, and (b) evaluating if the representative shape is completely within an unperceivable region.
14. The apparatus of claim 13 , in which generating of the data comprises treating the representative shape as the hypothetical vehicle.
15. The apparatus of claim 8 , in which generating of the data comprises applying temporal filtering to determine a location of the hypothetical vehicle.
16. The apparatus of claim 15 , in which applying the temporal filtering comprises smoothing an unknown skeleton by a forward propagated unknown skeleton, wherein the forward propagated unknown skeleton is generated by moving forward an old unknown skeleton along a traffic lane.
17. The apparatus of claim 8 , in which generating of the data comprises associating one or more attributes with the hypothetical vehicle.
18. The apparatus of claim 17 , in which the one or more of the attributes are related to a possible motion state of the hypothetical vehicle.
19. The apparatus of claim 18 , in which the possible motion state comprises a stationary condition.
20. The apparatus of claim 1 , in which the motion characteristics comprise a hypothetical speed being set to less than or equal to a predetermined maximum value.
21. The apparatus of claim 20 , in which the predetermined maximum value comprises a speed limit.
22. The apparatus of claim 20 , in which the predetermined maximum value comprises a quantity derived from other objects concurrently or previously perceived in the environment.
23. The apparatus of claim 20 , in which the predetermined maximum value comprises a quantity derived from historical data, road configuration, traffic rules, an event, a time, a weather condition, or a combination of two or more of them.
24. The apparatus of claim 1 , in which the computer accesses to a database comprising road network information.
25. The apparatus of claim 1 , in which the sensors comprises a radar sensor.
26. The apparatus of claim 1 , in which the sensors comprises a lidar sensor.
27. The apparatus of claim 1 , in which the sensors comprise a camera sensor.
28. The apparatus of claim 27 , in which the camera sensor comprises a stereo camera sensor.
29. The apparatus of claim 27 , in which the camera sensor comprises a monocular camera sensor.
30. An apparatus comprising:
an autonomous vehicle comprising:
a) steering, acceleration, deceleration, or gear selection devices or combinations of them configured to effect movement of the autonomous vehicle on a road network; and
b) a computer having a processor to execute a process (i) to generate commands to the steering, acceleration, deceleration, or gear selection devices or combinations of them to move the autonomous vehicle in accordance with driving decisions; and (ii) to update the commands (a) in response to data representing motion characteristics of a hypothetical vehicle being driven on the road network and (b) based on temporal filtering to determine a location of the hypothetical vehicle.
31. The apparatus of claim 30 , in which data representing motion characteristics of a hypothetical vehicle is probabilistically derived based on vehicles previously observed in an environment of the autonomous vehicle.
32. The apparatus of claim 30 , in which regions perceivable and unperceivable by the sensors are separated by a boundary.
33. The apparatus of claim 32 , in which the computer detects the boundary.
34. The apparatus of claim 33 , in which detecting of the boundary is based on using data from the sensors to distinguish an perceivable ground from a foreground that obscures a portion of the ground.
35. The apparatus of claim 34 , in which the sensors comprise sensors onboard the autonomous vehicle.
36. The apparatus of claim 34 , in which the sensors comprise sensors offboard the autonomous vehicle.
37. The apparatus of claim 1 , in which the computer generates the data based on known objects perceived by the sensors.
38. The apparatus of claim 37 , in which generating of the data comprises querying traffic lane information from a road network database.
39. The apparatus of claim 37 , in which generating of the data comprises using stored data to infer a possible location of the hypothetical vehicle.
40. The apparatus of claim 37 , in which generating of the data comprises determining a location of the autonomous vehicle based on a road network database and the sensors.
41. The apparatus of claim 37 , in which generating of the data comprises querying traffic lane information from a database and discretizing the traffic lane into discretized points.
42. The apparatus of claim 37 , in which generating of the current data comprises generating an unknown skeleton of discretized points of a lane that cannot be perceived by sensors.
43. The apparatus of claim 37 , in which generating of the data comprises (a) generating a representative shape at a discretized point of the lane that cannot be perceived by sensors, and (b) evaluating if the representative shape is completely within an unperceivable region.
44. The apparatus of claim 43 , in which generating of the data comprises treating the representative shape as the hypothetical vehicle.
45. The apparatus of claim 37 , in which applying the temporal filtering comprises smoothing an unknown skeleton by a forward propagated unknown skeleton, wherein the forward propagated unknown skeleton is generated by moving forward an old unknown skeleton along a traffic lane.
46. The apparatus of claim 37 , in which generating of the data comprises associating one or more attributes with the hypothetical vehicle.
47. The apparatus of claim 46 , in which the one or more of the attributes are related to a possible motion state of the hypothetical vehicle.
48. The apparatus of claim 47 , in which the possible motion state comprises a stationary condition.
49. The apparatus of claim 37 , in which the motion characteristics comprise a hypothetical speed is being set to less than or equal to a predetermined maximum value.
50. The apparatus of claim 49 , in which the predetermined maximum value comprises a speed limit.
51. The apparatus of claim 50 , in which the predetermined maximum value comprises a quantity derived from other objects concurrently or previously perceived in the environment.
52. The apparatus of claim 50 , in which the predetermined maximum value comprises a quantity derived from historical data, road configuration, traffic rules, an event, a time, a weather condition, or a combination of two or more of them.
53. The apparatus of claim 37 , in which the computer accesses to a database comprising road network information.
54. The apparatus of claim 37 , in which the sensors comprises a radar sensor.
55. The apparatus of claim 37 , in which the sensors comprises a lidar sensor.
56. The apparatus of claim 37 , in which the sensors comprise a camera sensor.
57. The apparatus of claim 27 , in which the camera sensor comprises a stereo camera sensor.
58. The apparatus of claim 27 , in which the camera sensor comprises a monocular camera sensor.