Autonomous vehicle fleet service and system

• US 9,606,539 B1
• Filed: 11/04/2015
• Issued: 03/28/2017
• Est. Priority Date: 11/04/2015
• Status: Active Grant

• Current Assignee: Zoox Inc. (Amazon.com, Inc.)
• Original Assignee: Zoox Inc. (Amazon.com, Inc.)
• Inventors: Kentley, Timothy David, Levinson, Jesse Sol, Gamara, Rachad Youssef, Sibley, Gabriel Thurston
• Primary Examiner(s): Tran, Khoi
• Assistant Examiner(s): Rink, Ryan
• Application Number:
  US14/932,959
• Time in Patent Office: 510 Days
• US Class Current: 1/1
• CPC Class Codes: B60Q 1/26 the devices being primarily intended to indicate the vehicle, or parts thereof, or to give signals, to other traffic
• CPC Class Codes: B60Q 1/28 for indicating front of vehicle
• CPC Class Codes: B60Q 1/30 for indicating rear of vehicle, e.g. by means of reflecting surfaces
• CPC Class Codes: B60Q 1/507 specific to autonomous vehicles
• CPC Class Codes: B60Q 1/543 for indicating other states or conditions of the vehicle indicating taxis' state of hire or fare G07B13/045
• 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 60/0027 using trajectory prediction for other traffic participants
• CPC Class Codes: G01S 13/865 Combination of radar systems with lidar systems
• CPC Class Codes: G01S 13/867 Combination of radar systems with cameras
• CPC Class Codes: G01S 13/87 Combinations of radar systems, e.g. primary radar and secondary radar
• CPC Class Codes: G01S 15/86 Combinations of sonar systems with lidar systems; Combinations of sonar systems with systems not using wave reflection
• CPC Class Codes: G01S 15/931 of land vehicles
• CPC Class Codes: G01S 17/86 Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
• CPC Class Codes: G01S 17/875 for determining attitude
• CPC Class Codes: G01S 17/931 of land vehicles
• CPC Class Codes: G01S 2013/9316 combined with communication equipment with other vehicles or with base stations
• CPC Class Codes: G01S 2013/9322 using additional data, e.g. driver condition, road state or weather data
• CPC Class Codes: G01S 7/497 Means for monitoring or calibrating
• CPC Class Codes: G01S 7/4972 Alignment of sensor
• CPC Class Codes: G05D 1/0027 involving a plurality of vehicles, e.g. fleet or convoy travelling fleet control of land vehicles from a control room G05D1/0297; traffic control systems for road vehicles G08G1/00; for marine craft G08G3/00; for aircraft G08G5/00
• CPC Class Codes: G05D 1/0088 : characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• 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 1/0291 : Fleet control monitoring fleets in traffic control systems for road vehicles G08G1/127, G08G1/127
• CPC Class Codes: G07C 5/00 : Registering or indicating the working of vehicles
• CPC Class Codes: G08G 1/005 : including pedestrian guidance indicator
• CPC Class Codes: G08G 1/165 : for passive traffic, e.g. including static obstacles, trees
• CPC Class Codes: G08G 1/166 : for active traffic, e.g. moving vehicles, pedestrians, bikes
• CPC Class Codes: G08G 1/202 : Dispatching vehicles on the basis of a location, e.g. taxi dispatching

Abstract

Various embodiments relate generally to autonomous vehicles and associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to provide an autonomous vehicle fleet as a service. In particular, a method may include monitoring a fleet of vehicles, at least one of which is configured to autonomously transit from a first geographic region to a second geographic region, detecting data indicating an event associated with the vehicle having a calculated confidence level, receiving data representing a subset of candidate trajectories responsive to detecting the event, which is associated with a planned path for the vehicle, identifying guidance data to select from one or more of the candidate trajectories as a guided trajectory, receiving data representing a selection of a candidate trajectory, and transmitting the selection of the candidate trajectory as of the guided trajectory to the vehicle.

Claims

1. A method comprising:

monitoring, at a computing system, a fleet of independent driverless vehicles, at least one of which is configured to autonomously transit from a first geographic region to a second geographic region via a road network, the driverless vehicles being bidirectional autonomous vehicles capable of driving forward in a first direction or driving forward in an opposite second direction and having at least one mechanism for communicating a current forward direction to one or more potential people in a surrounding environment;

receiving, at the computing system, data from a driverless vehicle in the fleet indicating an event encountered by the driverless vehicle that may cause the driverless vehicle to maneuver from a current trajectory;

generating, by the computing system, multiple candidate trajectories for the driverless vehicle to maneuver from the current trajectory;

calculating, by the computing system, confidence levels associated with the multiple candidate trajectories;

ranking, by the computing system, the multiple candidate trajectories according to the confidence levels;

choosing a particular candidate trajectory from among the multiple candidate trajectories based at least in part on the confidence levels associated with the multiple candidate trajectories; and

transmitting, to the driverless vehicle, the particular candidate trajectory for use in maneuvering the driverless vehicle whereby the driverless vehicle uses the mechanism to communicate any change of the first direction to the potential people in the surrounding environment,

wherein the choosing comprises;

presenting, to a human teleoperator, at least a subset of the multiple candidate trajectories in ranked order according to the associated confidence levels; and

receiving input from the human teleoperator to select the particular candidate trajectory.

2. The method of claim 1, wherein the mechanism comprises active lighting, and further comprising placing the active lighting in a first state that indicates the driverless vehicle is moving forward in the first direction and as part of the maneuvering, placing the active lighting in a second state that indicates the driverless vehicle is moving forward in the second direction.

3. The method of claim 1, wherein the mechanism comprises active lighting, and further comprising causing the active lighting to enter an animated state when the driverless vehicle is maneuvering.

4. The method of claim 1, wherein the mechanism comprises a sound generator to emit a pattern of sounds, and further comprising causing the sound generator to emit the pattern of sounds when the driverless vehicle is maneuvering.

5. The method of claim 1, wherein receiving data indicating the event comprises receiving data specifying a detected obstacle in the current trajectory, and further comprising determining whether the obstacle is a static obstacle or a dynamic obstacle.

6. The method of claim 5, wherein choosing a particular candidate trajectory comprises selecting the particular candidate trajectory based at least in part on whether the obstacle is determined to be a static obstacle or a dynamic obstacle.

7. The method of claim 1, wherein the associated confidence levels are related to a probability that the event impacts operation of the vehicle.

8. The method of claim 1, wherein the associated confidence levels are related to respective degrees of certainty that a particular candidate trajectory will reduce a probability that the event impacts operation of the vehicle.