Road construction detection systems and methods

• US 10,282,999 B2
• Filed: 03/17/2017
• Issued: 05/07/2019
• Est. Priority Date: 03/17/2017
• Status: Active Grant

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations LLC (General Motors Company)
• Inventors: Creusot, Clement
• Primary Examiner(s): Marc-Coleman, Marthe Y
• Application Number:
  US15/461,762
• Publication Number:
  US 20170242436A1
• Time in Patent Office: 781 Days
• Field of Search: 701 27, 701 23, 701 28, 701 41, 901 46
• CPC Class Codes: G06F 18/24143 Distances to neighbourhood prototypes, e.g. restricted Coulomb energy networks [RCEN]
• CPC Class Codes: G06V 10/454 Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
• CPC Class Codes: G06V 10/82 using neural networks
• CPC Class Codes: G06V 20/56 exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G06V 20/588 Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
• CPC Class Codes: G08G 1/005 including pedestrian guidance indicator
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0133 for classifying traffic situation
• CPC Class Codes: G08G 1/0141 for traffic information dissemination
• CPC Class Codes: G08G 1/09623 Systems involving the acquisition of information from passive traffic signs by means mounted on the vehicle G08G1/0967 takes precedence
• CPC Class Codes: G08G 1/09626 where the origin of the information is within the own vehicle, e.g. a local storage device, digital map
• CPC Class Codes: G08G 1/096816 where the complete route is transmitted to the vehicle at once
• CPC Class Codes: G08G 1/096844 where the complete route is dynamically recomputed based on new data
• CPC Class Codes: G08G 1/202 Dispatching vehicles on the basis of a location, e.g. taxi dispatching

Abstract

Systems and method are provided for controlling a vehicle. In one embodiment, a method of detecting road construction includes receiving sensor data relating to an environment associated with a vehicle, determining that construction-related objects are present within the environment based on the sensor data, and determining whether a travel-impacting construction zone is present within the environment based on the presence of the construction-related objects in the environment.

Claims

1. A method of detecting road construction comprising:

receiving, from a sensor system, sensor data including optical sensor data, relating to an environment associated with a vehicle;

determining, with a processor, that construction-related objects are present within the environment based on the sensor data by applying the optical sensor data to a pre-trained machine-learning model stored within the vehicle;

determining, with a processor, whether a construction zone is present within the environment and whether the construction zone is a travel-impacting construction zone based on the presence of the construction-related objects in the environment and a number, a position, and a type of the construction-related objects;

determining, with a processor, an impact on a route from the construction zone;

classifying, with a processor, the impact on a route from the construction zone as;

a partial lane blockage when the construction zone causes a partial lane blockage, a lane blockage when the construction zone causes a lane closure, and a road blockage when the construction zone causes a blocked road; and

transmitting a geographical location of the construction zone and the classified impact from the construction zone over a network to an external server.

2. The method of claim 1, further including receiving, at the vehicle, route information associated with a destination, the route information configured to avoid the travel-impacting construction zone.

3. The method of claim 1, further including transmitting information related to the travel-impacting construction zone over a network to a server.

4. The method of claim 1, wherein determining that the construction-related objects are present within the environment includes providing the sensor data to an artificial neural network model.

5. The method of claim 4, wherein the sensor data is provided to a convolutional neural network model.

6. The method of claim 1, wherein determining that construction-related objects are present within the environment includes determining the presence of at least one of:

a traffic cone, a traffic barrier, a traffic barrel, a construction sign, a reflective vest, a construction helmet, an arrow-board trailer, and a piece of construction equipment.

7. The method of claim 1, wherein the sensor data includes optical sensor data.

8. The method of claim 7, wherein the sensor data includes lidar sensor data.

9. A system for controlling a vehicle, comprising:

a construction-related object module, including a processor and machine-readable software instructions stored on non-transitory media that, when executed by the processor, are configured to receive sensor data including optical sensor data relating to an environment associated with the vehicle and determine that construction-related objects are present within the environment based on the sensor data by applying the optical sensor data to a pre-trained machine-learning model stored within the vehicle; and

a construction zone determination module, including a processor and machine-readable software instructions stored on non-transitory media that, when executed by the processor, are configured to;

determine whether a construction zone is present within the environment and whether the construction zone is a travel-impacting construction zone based on the presence of the construction-related objects in the environment and a number, a position, and a type of the construction-related objects;

determine an impact on a route from the construction zone;

classify the impact on a route from the construction zone as;

a partial lane blockage when the construction zone causes a partial lane blockage, a lane blockage when the construction zone causes a lane closure, and a road blockage when the construction zone causes a blocked road; and

transmit a geographical location of the construction zone and the classified impact from the construction zone over a network to an external server.

10. The system of claim 9, further including a communication system configured to transmit information related to the travel-impacting construction zone over a network to a server.

11. The system of claim 10, wherein the construction-related object module includes an artificial neural network model.

12. The system of claim 11, wherein the artificial neural network model is a convolutional neural network.

13. The system of claim 9, wherein the construction-related objects includes at least one of:

a traffic cone, a traffic barrier, a traffic barrel, a construction sign, a reflective vest, a construction helmet, an arrow-board trailer, and a piece of construction equipment.

14. The system of claim 9, wherein the sensor data comprises optical sensor data.

15. An autonomous vehicle, comprising:

at least one sensor that provides sensor data, including optical sensor data; and

a controller that, by a processor and based on the sensor data;

receives sensor data relating to an environment associated with a vehicle;

determines that construction-related objects are present within the environment based on the sensor data by applying the optical sensor data to a pre-trained machine-learning model stored within the vehicle;

determines whether a construction zone is present within the environment and whether the construction zone is a travel-impacting construction zone based on the presence of the construction-related objects in the environment and a number, a position, and a type of the construction-related objects;

determines an impact on a route from the construction zone;

classifies the impact on a route from the construction zone as;

a partial lane blockage when the construction zone causes a partial lane blockage, a lane blockage when the construction zone causes a lane closure, and a road blockage when the construction zone causes a blocked road; and

transmits a geographical location of the construction zone and the classified impact from the construction zone over a network to an external server.

16. The autonomous vehicle of claim 15, further including a communication system configured to transmit information related to the travel-impacting construction zone over a network to a server.

17. The autonomous vehicle of claim 16, further including a navigation system configured to receive, from the server, route information associated with a destination, the route information configured to avoid the travel-impacting construction zone.

18. The autonomous vehicle of claim 15, wherein the controller implements a convolutional neural network model.

19. The autonomous vehicle of claim 15, wherein the at least one sensor includes at least one of an optical sensor and a lidar sensor.

20. The autonomous vehicle of claim 15, wherein the construction-related objects includes at least one of:

a traffic cone, a traffic barrier, a traffic barrel, a construction sign, a reflective vest, a construction helmet, an arrow-board trailer, and a piece of construction equipment.