Facilitating Vehicle Driving and Self-Driving

• US 20170277193A1
• Filed: 04/04/2017
• Published: 09/28/2017
• Est. Priority Date: 03/23/2016
• Status: Abandoned Application

• Current Assignee: Motional AD, LLC (Aptiv PLC)
• Original Assignee: nuTonomy, Inc. (BorgWarner Incorporated)
• Inventors: Frazzoli, Emilio, Iagnemma, Karl
• Application Number:
  US15/478,991
• Publication Number:
  US 20170277193A1
• Time in Patent Office: Days
• CPC Class Codes: B60W 2050/0013 Optimal controllers
• CPC Class Codes: B60W 2050/146 Display means
• CPC Class Codes: B60W 2420/40 Photo, light or radio wave sensitive means, e.g. infrared sensors
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2420/408 Radar; Laser, e.g. lidar
• CPC Class Codes: B60W 2420/54 Audio sensitive means, e.g. ultrasound
• CPC Class Codes: B60W 2510/18 Braking system
• CPC Class Codes: B60W 2510/20 Steering systems
• CPC Class Codes: B60W 2540/18 Steering angle
• CPC Class Codes: B60W 2552/00 Input parameters relating to infrastructure
• CPC Class Codes: B60W 2552/50 Barriers
• CPC Class Codes: B60W 2552/53 Road markings, e.g. lane marker or crosswalk
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2554/20 Static objects
• CPC Class Codes: B60W 2554/4026 Cycles
• CPC Class Codes: B60W 2554/4029 Pedestrians
• CPC Class Codes: B60W 2554/4041 Position
• CPC Class Codes: B60W 2554/406 Traffic density
• CPC Class Codes: B60W 2554/60 Traversable objects, e.g. speed bumps or curbs
• CPC Class Codes: B60W 2554/802 Longitudinal distance
• CPC Class Codes: B60W 2554/804 : Relative longitudinal speed
• CPC Class Codes: B60W 2555/20 : Ambient conditions, e.g. wind or rain
• CPC Class Codes: B60W 2555/60 : Traffic rules, e.g. speed limits or right of way
• CPC Class Codes: B60W 2556/65 : Data transmitted between vehicles
• CPC Class Codes: B60W 2710/18 : Braking system
• CPC Class Codes: B60W 2710/20 : Steering systems
• CPC Class Codes: B60W 2756/10 : Involving external transmission of data to or from the vehicle
• CPC Class Codes: B60W 30/00 : Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
• CPC Class Codes: B60W 30/09 : Taking automatic action to avoid collision, e.g. braking and steering
• CPC Class Codes: B60W 30/0956 : the prediction being responsive to traffic or environmental parameters
• CPC Class Codes: B60W 30/18163 : Lane change; Overtaking manoeuvres
• CPC Class Codes: B60W 60/0011 : involving control alternatives for a single driving scenario, e.g. planning several paths to avoid obstacles
• CPC Class Codes: B60W 60/0015 : specially adapted for safety
• CPC Class Codes: B60W 60/0016 : of the vehicle or its occupants
• CPC Class Codes: B60W 60/00276 : for two or more other traffic participants
• CPC Class Codes: G05D 1/0212 : with means for defining a desired trajectory involving a plurality of land vehicles G05D1/0287
• CPC Class Codes: G05D 1/0214 : in accordance with safety or protection criteria, e.g. avoiding hazardous areas monitoring the location of vehicles within a certain area, e.g. forbidden or allowed areas, in traffic control systems for road vehicles G08G1/13
• CPC Class Codes: G05D 1/0231 : using optical position detecting means position-fixing by using electromagnetic waves other than radio waves, e.g. optical position detecting means G01S5/16
• CPC Class Codes: G05D 1/0251 : extracting 3D information from a plurality of images taken from different locations, e.g. stereo vision stereoscopic image analysis H04N13/00; depth recovery from images G06T7/593
• CPC Class Codes: G05D 1/0257 : using a radar radar systems designed for anti-collision purposes between land vehicles or between land vehicle and fixed obstacles G01S13/931
• CPC Class Codes: G05D 1/0268 : using internal positioning means
• CPC Class Codes: G05D 1/0278 : using satellite positioning signals, e.g. GPS
• CPC Class Codes: G05D 1/0285 : using signals transmitted via a public communication network, e.g. GSM network
• CPC Class Codes: G09B 19/167 : Control of land vehicles

Abstract

Among other things, an operation related to control of a vehicle is facilitated by actions that include the following. A finite set of candidate trajectories of the vehicle is generated that begin at a location of the vehicle as of a given time. The candidate trajectories are based on a state of the vehicle and on possible behaviors of the vehicle and of the environment as of the location of the vehicle and the given time. A putative optimal trajectory is selected from among the candidate trajectories based on costs associated with the candidate trajectories. The costs include costs associated with violations of rules of operation of the vehicle. The selected putative optimal trajectory is used to facilitate the operation related to control of the vehicle.

Claims

1-30. -30. (canceled)

31. A method comprising

effecting an operation related to control of a vehicle by actions that include:

generating a finite set of candidate trajectories of the vehicle that begin at a location of the vehicle as of a given time, the candidate trajectories being based on a state of the vehicle and on possible behaviors of the vehicle and of the environment as of the location of the vehicle and the given time,

expressing each of the candidate trajectories of the finite set as a corresponding logical trajectory of the vehicle,

selecting a putative optimal trajectory from among the candidate trajectories based on the logical trajectories, and

using the selected putative optimal trajectory to effect the operation related to control of the vehicle.

32. The method of claim 31 in which expressing each of the candidate trajectories as a logical trajectory comprises attributing logical predicates to states of the candidate trajectories.

33. The method of claim 31 in which expressing each of the candidate trajectories as a logical trajectory comprises attributing labels to transitions of the candidate trajectories.

34. The method of claim 32 in which the logical trajectory comprises a maximal ordered list of non-repeating entries associated with a sub-sequence of states along the corresponding candidate trajectory.

35. The method of claim 34 in which the maximal ordered list of non-repeating entries describes a physical operation or behavior or both of the vehicle.

36. The method of claim 31 in which expressing each of the candidate trajectories as a logical trajectory comprises using logical statements of operation or behavior or both of the vehicle.

37. The method of claim 31 in which expressing each of the candidate trajectories as a logical trajectory comprises selecting expressions from a list of potential labels relevant to transitions or logical predicates relevant to states.

38. The method of claim 31 in which selecting the putative optimal trajectory from among the candidate trajectories comprises testing the logical trajectories against rules.

39. The method of claim 38 in which the rules are prioritized.

40. The method of claim 38 in which the rules comprise rules of operation.

41. The method of claim 31 in which the effecting of the operation related to control of the vehicle comprises applying a feedback control policy associated with the putative optimal trajectory to control elements of the vehicle.

42. The method of claim 31 in which selecting the putative optimal trajectory comprises determining a minimum-cost path through a directed graph of which the candidate trajectories comprise edges.

43. The method of claim 31 in which generating a finite set of candidate trajectories of the vehicle comprises applying a model that represents the vehicle'"'"'s expected response to a given control policy as of the location of the vehicle and the given time.

44. The method of claim 31 comprising monitoring an actual trajectory of the vehicle for a given time period.

45. The method of claim 44 comprising comparing, for the given time period, the actual trajectory of the vehicle with the putative optimal trajectory.

46. The method of claim 31 in which the effecting of an operation related to control of a vehicle comprises monitoring a driver'"'"'s performance.

47. The method of claim 46 comprising evaluating the driver'"'"'s performance based on one or more performance metrics.

48. The method of claim 46 comprising displaying information related to the driver'"'"'s performance on an in-vehicle display.

49. The method of claim 46 comprising transmitting information related to the driver'"'"'s performance wirelessly to a receiver remote from the vehicle.

50. The method of claim 31 in which the effecting of an operation related to control of a vehicle comprises autonomously driving the vehicle.

51. The method of claim 38 in which the rules are associated with costs based on interactions between states of the vehicle and states of the environment.

52. The method of claim 31 in which the selected putative optimal trajectory is associated with both speed and direction of the vehicle.

53. The method of claim 51 in which the state of the environment comprises the states of other vehicles, pedestrians, and obstacles.

54. The method of claim 41 in which the application of the feedback control policy is based on the states of the vehicle and of the environment.

69. The apparatus of claim 44 comprising comparing, for the given time period, the actual trajectory of the vehicle with the putative optimal trajectory.

55. An apparatus comprising

an autonomous vehicle comprising

controllable devices configured to enable the vehicle to traverse at least part of an optimal trajectory, and

a computational element configured to effect, through the controllable devices, an operation related to control of the vehicle, by actions that include:

generating a finite set of candidate trajectories of the vehicle that begin at a location of the vehicle as of a given time, the candidate trajectories being based on a state of the vehicle and on possible behaviors of the vehicle and of the environment as of the location of the vehicle and the given time,

expressing each of the candidate trajectories of the finite set as a corresponding logical trajectory of the vehicle,

selecting a putative optimal trajectory from among the candidate trajectories based on the logical trajectories, and

using the selected putative optimal trajectory to effect the operation related to control of the vehicle.

56. The apparatus of claim 55 in which expressing each of the candidate trajectories as a logical trajectory comprises attributing logical predicates to states of the candidate trajectories.

57. The apparatus of claim 55 in which expressing each of the candidate trajectories as a logical trajectory comprises attributing labels to transitions of the candidate trajectories.

58. The apparatus of claim 56 in which the logical trajectory comprises a maximal ordered list of non-repeating entries associated with a sub-sequence of states along the corresponding candidate trajectory.

59. The apparatus of claim 58 in which the maximal ordered list of non-repeating entries describes a physical operation or behavior or both of the vehicle.

60. The apparatus of claim 55 in which expressing each of the candidate trajectories as a logical trajectory comprises using logical statements of operation or behavior or both of the vehicle.

61. The apparatus of claim 55 in which expressing each of the candidate trajectories as a logical trajectory comprises selecting expressions from a list of potential labels relevant to transitions or logical predicates relevant to states.

62. The apparatus of claim 55 in which selecting the putative optimal trajectory from among the candidate trajectories comprises testing the logical trajectories against rules.

63. The apparatus of claim 62 in which the rules are prioritized.

64. The apparatus of claim 62 in which the rules comprise rules of operation.

65. The apparatus of claim 55 in which the effecting of the operation related to control of the vehicle comprises applying a feedback control policy associated with the putative optimal trajectory to control elements of the vehicle.

66. The apparatus of claim 55 in which selecting the putative optimal trajectory comprises determining a minimum-cost path through a directed graph of which the candidate trajectories comprise edges.

67. The apparatus of claim 55 in which generating a finite set of candidate trajectories of the vehicle comprises applying a model that represents the vehicle'"'"'s expected response to a given control policy as of the location of the vehicle and the given time.

68. The apparatus of claim 55 comprising monitoring an actual trajectory of the vehicle for a given time period.

70. The apparatus of claim 55 in which the effecting of an operation related to control of a vehicle comprises monitoring a driver'"'"'s performance.

71. The apparatus of claim 68 comprising evaluating the driver'"'"'s performance based on one or more performance metrics.

72. The apparatus of claim 68 comprising displaying information related to the driver'"'"'s performance on an in-vehicle display.

73. The apparatus of claim 68 comprising transmitting information related to the driver'"'"'s performance wirelessly to a receiver remote from the vehicle.

74. The apparatus of claim 55 in which the effecting of an operation related to control of a vehicle comprises autonomously driving the vehicle.

75. The apparatus of claim 62 in which the rules are associated with costs based on interactions between states of the vehicle and states of the environment.

76. The apparatus of claim 55 in which the selected putative optimal trajectory is associated with both speed and direction of the vehicle.

77. The apparatus of claim 75 in which the state of the environment comprises the states of other vehicles, pedestrians, and obstacles.

78. The apparatus of claim 65 in which the application of the feedback control policy is based on the states of the vehicle and of the environment.