Autonomous operation suitability assessment and mapping

• US 10,295,363 B1
• Filed: 01/18/2017
• Issued: 05/21/2019
• Est. Priority Date: 01/22/2016
• Status: Active Grant

• Current Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Original Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Inventors: Konrardy, Blake, Christensen, Scott T., Hayward, Gregory, Farris, Scott
• Primary Examiner(s): Heinle, Courtney D
• Application Number:
  US15/409,143
• Time in Patent Office: 853 Days
• CPC Class Codes: B60L 53/36 by positioning the vehicle
• CPC Class Codes: B60L 58/12 responding to state of charge [SoC]
• CPC Class Codes: B60P 3/12 for salvaging damaged vehicles
• CPC Class Codes: B60R 16/0234 related to maintenance or repairing of vehicles
• CPC Class Codes: B60R 2021/0027 Post collision measures, e.g. notifying emergency services
• CPC Class Codes: B60R 2021/01013 Means for detecting collision, impending collision or roll-over
• CPC Class Codes: B60R 2025/1013 Alarm systems characterised by the type of warning signal, e.g. visual, audible
• CPC Class Codes: B60R 21/0136 responsive to actual contact with an obstacle , e.g. to vehicle deformation, bumper displacement or bumper velocity relative to the vehicle monitoring crash strength in combination with passenger seat detection B60R21/01558
• CPC Class Codes: B60R 21/34 Protecting non-occupants of a vehicle, e.g. pedestrians B60R19/02 takes precedence
• CPC Class Codes: B60R 25/04 operating on the propulsion system, e.g. engine or drive motor
• CPC Class Codes: B60R 25/10 actuating a signalling device
• CPC Class Codes: B60R 25/1001 Alarm systems associated with another car fitting or mechanism, e.g. door lock or knob, pedals features related to the general power supply B60R25/1018
• CPC Class Codes: B60R 25/102 a signal being sent to a remote location, e.g. a radio signal being transmitted to a police station, a security company or the owner
• CPC Class Codes: B60R 25/104 characterised by the type of theft warning signal, e.g. visual or audible signals with special characteristics
• CPC Class Codes: B60R 25/252 Fingerprint recognition
• CPC Class Codes: B60R 25/255 Eye recognition
• CPC Class Codes: B60R 25/305 using a camera
• CPC Class Codes: B60R 25/31 of human presence inside or outside the vehicle
• CPC Class Codes: B60W 10/04 including control of propulsion units
• CPC Class Codes: B60W 10/18 including control of braking systems
• CPC Class Codes: B60W 10/20 : including control of steering systems
• 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 2530/209 : Fuel quantity remaining in tank
• CPC Class Codes: B60W 2540/229 : Attention level, e.g. attentive to driving, reading or sleeping
• CPC Class Codes: B60W 2552/05 : Type of road, e.g. motorways, local streets, paved or unpaved roads
• CPC Class Codes: B60W 2552/35 : Road bumpiness, e.g. potholes
• 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 2556/10 : Historical data
• CPC Class Codes: B60W 30/0956 : the prediction being responsive to traffic or environmental parameters
• CPC Class Codes: B60W 30/12 : Lane keeping
• CPC Class Codes: B60W 30/16 : Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
• CPC Class Codes: B60W 30/18163 : Lane change; Overtaking manoeuvres
• CPC Class Codes: B60W 40/04 : Traffic conditions
• CPC Class Codes: B60W 60/0023 : in response to energy consumption
• CPC Class Codes: B60W 60/0053 : from vehicle to occupant
• CPC Class Codes: B60W 60/0059 : Estimation of the risk associated with autonomous or manual driving, e.g. situation too complex, sensor failure or driver incapacity
• CPC Class Codes: G01B 21/00 : Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
• CPC Class Codes: G01C 21/34 : Route searching; Route guidance
• CPC Class Codes: G01C 21/3415 : Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
• CPC Class Codes: G01C 21/343 : Calculating itineraries, i.e. routes leading from a starting point to a series of categorical destinations using a global route restraint, round trips, touristic trips travelling salesman problem G06Q10/04; optimisation of routes G06Q10/047
• CPC Class Codes: G01C 21/3438 : Rendez-vous, i.e. searching a destination where several users can meet, and the routes to this destination for these users; Ride sharing, i.e. searching a route such that at least two users can share a vehicle for at least part of the route
• CPC Class Codes: G01C 21/3453 : Special cost functions, i.e. other than distance or default speed limit of road segments
• CPC Class Codes: G01C 21/3461 : Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types, segments such as motorways, toll roads, ferries
• CPC Class Codes: G01C 21/3469 : Fuel consumption; Energy use; Emission aspects
• CPC Class Codes: G01C 21/3617 : using user history, behaviour, conditions or preferences, e.g. predicted or inferred from previous use or current movement
• CPC Class Codes: G01C 21/362 : received from an external device or application, e.g. PDA, mobile phone or calendar application
• CPC Class Codes: G01C 21/3697 : Output of additional, non-guidance related information, e.g. low fuel level G01C21/3679 takes precedence
• CPC Class Codes: G01S 19/13 : Receivers
• CPC Class Codes: G01S 19/42 : Determining position
• CPC Class Codes: G05B 15/02 : electric
• CPC Class Codes: G05B 23/0245 : based on a qualitative model, e.g. rule based; if-then decisions
• CPC Class Codes: G05D 1/0011 : associated with a remote control arrangement
• CPC Class Codes: G05D 1/0055 : with safety arrangements
• 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 1/0231 : 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 1/0246 : using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0255 : using acoustic signals, e.g. ultra-sonic singals sonar systems designed for anti-collision purposes G01S15/93
• CPC Class Codes: G05D 1/0285 : using signals transmitted via a public communication network, e.g. GSM network
• CPC Class Codes: G05D 1/0287 : involving a plurality of land vehicles, e.g. fleet or convoy travelling traffic control systems for road vehicles G08G1/00, particularly anticollision systems G08G1/16
• CPC Class Codes: G05D 1/0289 : with means for avoiding collisions between vehicles vehicle fittings for automatically controlling speed including means for detecting potential obstacles B60K31/0008; avoiding obstacles by action on the steering system B62D; radar, sonar, lidar systems designed for anti-collision purposes G01S13/93, G01S15/93, G01S17/93
• CPC Class Codes: G05D 1/0293 : Convoy travelling
• CPC Class Codes: G05D 1/0295 : by at least one leading vehicle of the fleet
• CPC Class Codes: G05D 1/223 : Command input arrangements on the remote controller, e.g. joysticks or touch screens
• CPC Class Codes: G05D 1/227 : Handing over between remote control and on-board control; Handing over between remote control arrangements
• CPC Class Codes: G05D 1/228 : Command input arrangements located on-board unmanned vehicles
• CPC Class Codes: G05D 1/247 : using signals provided by artificial sources external to the vehicle, e.g. navigation beacons
• CPC Class Codes: G05D 1/249 : from positioning sensors located off-board the vehicle, e.g. from cameras
• CPC Class Codes: G05D 1/617 : Safety or protection, e.g. defining protection zones around obstacles or avoiding hazards arrangements for controlling the position or course of two or more vehicles for avoiding collisions therebetween G05D1/693; arrangements for reacting to or preventing system or operator failure G05D1/80
• CPC Class Codes: G05D 1/646 : Following a predefined trajectory, e.g. a line marked on the floor or a flight path
• CPC Class Codes: G05D 1/69 : Coordinated control of the position or course of two or more vehicles
• CPC Class Codes: G05D 1/692 : involving a plurality of disparate vehicles
• CPC Class Codes: G05D 1/693 : for avoiding collisions between vehicles
• CPC Class Codes: G05D 1/695 : for maintaining a fixed relative position of the vehicles, e.g. for convoy travelling or formation flight
• CPC Class Codes: G05D 1/697 : for rendezvous of two or more vehicles, e.g. for in-flight refuelling docking at a base station G05D1/661
• CPC Class Codes: G06F 11/3688 : for test execution, e.g. scheduling of test suites
• CPC Class Codes: G06F 11/3692 : for test results analysis
• CPC Class Codes: G06F 16/2455 : Query execution
• CPC Class Codes: G06F 16/90335 : Query processing
• CPC Class Codes: G06F 17/00 : Digital computing or data processing equipment or methods, specially adapted for specific functions information retrieval, database structures or file system structures therefor G06F16/00
• CPC Class Codes: G06F 21/32 : using biometric data, e.g. fingerprints, iris scans or voiceprints
• CPC Class Codes: G06F 21/55 : Detecting local intrusion or implementing counter-measures
• CPC Class Codes: G06F 2221/034 : Test or assess a computer or a system
• CPC Class Codes: G06F 30/15 : Vehicle, aircraft or watercraft design
• CPC Class Codes: G06F 30/20 : Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06Q 10/1095 : Meeting or appointment
• CPC Class Codes: G06Q 10/20 : Administration of product repair or maintenance
• CPC Class Codes: G06Q 30/0284 : Time or distance, e.g. usage of parking meters or taximeters
• CPC Class Codes: G06Q 30/0645 : Rental transactions; Leasing transactions
• CPC Class Codes: G06Q 40/08 : Insurance
• CPC Class Codes: G06Q 50/163 : Real estate management
• CPC Class Codes: G06Q 50/265 : Personal security, identity or safety
• CPC Class Codes: G06Q 50/40 : Business processes related to the transportation industry shipping G06Q10/083
• CPC Class Codes: G07C 5/006 : Indicating maintenance
• CPC Class Codes: G07C 5/008 : communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/0808 : Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005
• CPC Class Codes: G07C 5/0816 : Indicating performance data, e.g. occurrence of a malfunction
• CPC Class Codes: G07C 5/0841 : Registering performance data recording measured values G01D; information storage G11B
• CPC Class Codes: G07C 9/00563 : using personal physical data of the operator, e.g. finger prints, retinal images, voicepatterns
• CPC Class Codes: G08B 21/00 : Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
• CPC Class Codes: G08B 21/02 : Alarms for ensuring the safety of persons
• CPC Class Codes: G08B 21/18 : Status alarms G08B21/02 takes precedence
• CPC Class Codes: G08B 25/00 : Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
• CPC Class Codes: G08B 25/014 : Alarm signalling to a central station with two-way communication, e.g. with signalling back
• CPC Class Codes: G08G 1/017 : identifying vehicles G08G1/015, G08G1/054 take precedence
• CPC Class Codes: G08G 1/0965 : responding to signals from another vehicle, e.g. emergency vehicle
• CPC Class Codes: G08G 1/096725 : where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096783 : where the origin of the information is a roadside individual element
• CPC Class Codes: G08G 1/143 : inside the vehicles
• CPC Class Codes: G08G 1/146 : where the parking area is a limited parking space, e.g. parking garage, restricted space
• CPC Class Codes: G08G 1/148 : Management of a network of parking areas
• CPC Class Codes: G08G 1/161 : Decentralised systems, e.g. inter-vehicle communication
• 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 1/167 : Driving aids for lane monitoring, lane changing, e.g. blind spot detection
• CPC Class Codes: G08G 1/20 : Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
• CPC Class Codes: G08G 1/205 : Indicating the location of the monitored vehicles as destination, e.g. accidents, stolen, rental
• CPC Class Codes: G16Y 10/80 : Homes; Buildings
• CPC Class Codes: G16Y 30/00 : IoT infrastructure
• CPC Class Codes: H04L 12/2803 : Home automation networks
• CPC Class Codes: H04L 12/2816 : Controlling appliance services of a home automation network by calling their functionalities arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station; in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom H04Q9/00
• CPC Class Codes: H04L 12/2825 : Reporting to a device located outside the home and the home network access arrangements H04L12/2856; for remote control or remote monitoring of applications H04L67/025; telephonic communication systems adapted for combination with telemetering systems H04M11/002
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 67/306 : User profiles
• CPC Class Codes: H04N 7/183 : for receiving images from a single remote source
• CPC Class Codes: Y02A 10/40 : Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
• CPC Class Codes: Y02T 10/70 : Energy storage systems for electromobility, e.g. batteries
• CPC Class Codes: Y02T 10/7072 : Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
• CPC Class Codes: Y02T 90/12 : Electric charging stations

Abstract

Methods and systems for autonomous and semi-autonomous vehicle routing are disclosed. Roadway suitability for autonomous operation is scored to facilitate use in route determination. Maps of roadways suitable for various levels of autonomous operation may be generated. Such map data may be used by autonomous vehicles or other computer devices in determining routes based upon criteria for vehicle trips. Such routes may be automatically updated based upon changes in road conditions, vehicle conditions, operator conditions, or environmental conditions. Emergency routing using such map data is described, such as automatic routing and travel when a passenger is experiencing a medical emergency.

Claims

1. A computer-implemented method for analyzing roadway suitability for autonomous or semi-autonomous vehicle operation, comprising:

receiving, at one or more processors, map data including indications of a plurality of road segments;

receiving, at the one or more processors, operating data from a plurality of vehicles having at least one autonomous operation feature, wherein the operating data includes location data associated with operation of the plurality of vehicles and wherein the operating data further includes data regarding accidents and data regarding hazardous events other than accidents, the hazardous events other than accidents including near collisions in which some of the plurality of vehicles came within threshold distances of other vehicles;

selecting, by the one or more processors, a road segment to analyze from the plurality of road segments;

associating, by the one or more processors, a subset of the operating data with the road segment based upon the location data;

determining, by the one or more processors, one or more risk levels associated with the road segment based upon the associated subset of the operating data;

determining, by the one or more processors, one or more suitability scores for the road segment based upon the one or more risk levels, each suitability score indicating a category of suitability for autonomous or semi-autonomous vehicle operation; and

storing, in a database, the one or more suitability scores, each suitability score further stored with an indication of the associated road segment.

2. The computer-implemented method of claim 1, further comprising:

generating, by the one or more processors, one or more map tiles including visual representations of the one or more suitability scores.

3. The computer-implemented method of claim 2, further comprising:

receiving, at the one or more processors, route information associated with a fleet of vehicles having autonomous operation features;

analyzing, by the one or more processors, the received route information using the one or more suitability scores;

identifying, by the one or more processors, one or more optimal routes associated with the fleet of vehicles; and

transmitting, to one or more additional processors associated with at least one vehicle of the fleet of vehicles, an indication of the one or more optimal routes.

4. The computer-implemented method of claim 3, further comprising:

receiving, at the one or more processors, operating data from the fleet of vehicles, the operating data including location data indicating a plurality of locations of the vehicles of the fleet of vehicles; and

determining, by the one or more processors, an extent to which the fleet of vehicles follow the optimal routes.

5. The computer-implemented method of claim 1, further comprising:

requesting, by the one or more processors, the map data from a third-party data source using an application program interface associated with the third-party data source.

6. The computer-implemented method of claim 1, wherein the operating data includes usage information regarding one or more autonomous operation feature settings.

7. The computer-implemented method of claim 1, wherein the operating data includes data regarding a plurality of types of environmental conditions associated with the plurality of vehicles.

8. The computer-implemented method of claim 1, wherein the operating data for at least one of the plurality of vehicles is generated by a data application running on a mobile device associated with the vehicle.

9. The computer-implemented method of claim 1,

wherein the hazardous events other than accidents further include occurrences of (i) loss of control or (ii) hard braking.

10. The computer-implemented method of claim 1, wherein

determining the one or more risk levels includes using machine learning techniques to automatically determine magnitudes of one or more risk factors associated with autonomous operation feature usage.

11. The computer-implemented method of claim 1, wherein:

the map data includes one or more indications of location for each of the plurality of road segments, wherein the indications of location utilize a first format for indicating geospatial location;

the location data utilizes a second format distinct from the first format for indicating geospatial location; and

associating the subset of the operating data with the identified road segment includes converting one or both of the indication of location or the location data to a compatibility format for association.

12. The computer-implemented method of claim 1, wherein the one or more suitability scores include a minimum safety rating for the road segment.

13. The computer-implemented method of claim 1, wherein the one or more suitability scores indicate the suitability of the road segment for fully autonomous vehicle usage.

14. A computer system for analyzing roadway suitability for autonomous or semi-autonomous vehicle operation, comprising:

one or more processors;

a map database communicatively connected to the one or more processors; and

a program memory coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the computer system to;

receive map data including indications of a plurality of road segments;

receive operating data from a plurality of vehicles having at least one autonomous operation feature, wherein the operating data includes location data associated with operation of the plurality of vehicles and wherein the operating data further includes data regarding accidents and data regarding hazardous events other than accidents, the hazardous events other than accidents including near collisions in which some of the plurality of vehicles came within threshold distances of other vehicles;

select a road segment to analyze from the plurality of road segments;

associate a subset of the operating data with the road segment based upon the location data;

determine one or more risk levels associated with the road segment based upon the associated subset of the operating data;

determine one or more suitability scores for the road segment based upon the one or more risk levels, each suitability score indicating a category of suitability for autonomous or semi-autonomous vehicle operation; and

store the one or more suitability scores in the map database, each suitability score further stored with an indication of the associated road segment.

15. The computer system of claim 14, wherein the program memory further stores executable instructions that cause the computer system to:

generate one or more map tiles including visual representations of the one or more suitability scores.

16. The computer system of claim 15, wherein the program memory further stores executable instructions that cause the computer system to:

receive route information associated with a fleet of vehicles having autonomous operation features;

analyze the received route information using the one or more suitability scores;

identify one or more optimal routes associated with the fleet of vehicles; and

transmit an indication of the one or more optimal routes to one or more additional processors associated with at least one vehicle of the fleet of vehicles.

17. The computer system of claim 14, wherein:

the map data includes one or more indications of location for each of the plurality of road segments, wherein the indications of location utilize a first format for indicating geospatial location;

the location data utilizes a second format distinct from the first format for indicating geospatial location; and

associating the subset of the operating data with the identified road segment includes converting one or both of the indication of location or the location data to a compatibility format for association.

18. The computer system of claim 14, wherein the operating data for at least one of the plurality of vehicles is generated by a data application running on a mobile device associated with the vehicle.

19. A tangible, non-transitory computer-readable medium storing instructions for analyzing roadway suitability for autonomous or semi-autonomous vehicle operation that, when executed by at least one processor of a computer system, cause the computer system to:

receive map data including indications of a plurality of road segments;

receive operating data from a plurality of vehicles having at least one autonomous operation feature, wherein the operating data includes location data associated with operation of the plurality of vehicles and wherein the operating data further includes data regarding accidents and data regarding hazardous events other than accidents, the hazardous events other than accidents including near collisions in which some of the plurality of vehicles came within threshold distances of other vehicles;

select a road segment to analyze from the plurality of road segments;

associate a subset of the operating data with the road segment based upon the location data;

determine one or more risk levels associated with the road segment based upon the associated subset of the operating data;

determine one or more suitability scores for the road segment based upon the one or more risk levels, each suitability score indicating a category of suitability for autonomous or semi-autonomous vehicle operation; and

store the one or more suitability scores in a map database, each suitability score further stored with an indication of the associated road segment.

20. The tangible, non-transitory computer-readable medium of claim 19, further storing instructions that cause the computer system to:

generate one or more map tiles including visual representations of the one or more suitability scores.