Providing a GUI to enable analysis of time-synchronized data sets pertaining to a road segment
Techniques for collecting, synchronizing, and displaying various types of data relating to a road segment enable, via one or more local or remote processors, servers, transceivers, and/or sensors, (i) enhanced and contextualized analysis of vehicle events by way of synchronizing different data types, relating to a monitored road segment, collected via various different types of data sources; (ii) enhanced and contextualized analysis of filed insurance claims pertaining to a vehicle incident at a road segment; (iii) advantageous machine learning techniques for predicting a level of risk assumed for a given vehicle event or a given road segment; (iv) techniques for accounting for region-specific driver profiles when controlling autonomous vehicles; and/or (v) improved techniques for providing a GUI to display collected data in a meaningful and contextualized manner.
1. A computer-implemented method providing a graphic user interface to facilitate analyzing vehicle events at a road segment, the method comprising, via one or more local or remote processors, servers, transceivers, and/or sensors:
(A) displaying a graphic user interface (GUI) configured to display at least one of a series of images, the GUI including an image control element interactable to advance forward or backward in time through the series of images;
(B) analyzing a displayed image from the series of images displayed within the GUI to identify a timestamp for the displayed image;
(C) retrieving values for a set of road segment parameters based upon the identified timestamp, such that the values for the set of road segment parameters are relevant-in-time to the displayed image, wherein the set of road segment parameters comprise a parameter collected via one or more vehicle sensors and a parameter collected via one or more infrastructure devices;
(D) dynamically calculating one or more risk indices by analyzing the set of road segment parameters using a machine learning model previously trained on synchronized training data including telematics data, contextual data regarding vehicle operating environments that is synchronized with the telematics data, and vehicle incident data from a plurality of past vehicle events;
(E) analyzing the set of road segment parameters to generate a behavioral characterization of one or more drivers; and
(F) displaying the behavioral characterization of the one or more drivers simultaneous to the displaying of the displayed image, such that the behavioral characterization, relevant-in-time to the displayed image, is simultaneously viewable with the displayed image.
2. The computer-implemented method of claim 1 , wherein the parameter collected via the one or more vehicle sensors represents a speed, position, or heading of a vehicle.
3. The computer-implemented method of claim 1 , wherein the parameter collected via the one or more vehicle sensors represents a proximity between a first vehicle in which the one or more vehicle sensors are disposed and a second vehicle detected via the one or more vehicle sensors.
4. The computer-implemented method of claim 1 , wherein the parameter collected via the one or more infrastructure devices represents an atmospheric condition.
5. The computer-implemented method of claim 1 , wherein the parameter collected via the one or more infrastructure devices is a health status parameter representing a degree of health for an infrastructure component.
6. The computer-implemented method of claim 1 , wherein the parameter collected via the one or more infrastructure devices is an operational status parameter representing an operational status of an infrastructure component.
7. The computer-implemented method of claim 1 , wherein the parameter collected via the one or more infrastructure devices is a speed collected via a radar device.
8. A system for providing a graphic user interface to facilitate analyzing vehicle events at a road segment, the system comprising:
a display;
one or more processors coupled to the display;
a memory coupled to the one or more processors and storing computer-readable instructions that, when executed, cause the one or more processors to:
(A) display a graphic user interface (GUI) configured to display at least one of a series of images, the GUI including an image control element interactable to advance forward or backward in time through the series of images;
(B) analyze a displayed image from the series of images displayed within the GUI to identify a timestamp for the displayed image;
(C) retrieve values for a set of road segment parameters based upon the identified timestamp, such that the values for the set of road segment parameters are relevant-in-time to the displayed image, wherein the set of road segment parameters comprise a parameter collected via one or more vehicle sensors and a parameter collected via one or more infrastructure devices;
(D) dynamically calculate one or more risk indices by analyzing the set of road segment parameters using a machine learning model previously trained on synchronized training data including telematics data, contextual data regarding vehicle operating environments that is synchronized with the telematics data, and vehicle incident data from a plurality of past vehicle events;
(E) analyze the set of road segment parameters to generate a behavioral characterization of one or more drivers; and
(F) display the behavioral characterization of the one or more drivers simultaneous to the displaying of the displayed image, such that the behavioral characterization, relevant-in-time to the displayed image, is simultaneously viewable with the displayed image.
9. The system of claim 8 , wherein the parameter collected via the one or more vehicle sensors represents a speed, position, or heading of a vehicle.
10. The system of claim 8 , wherein the parameter collected via the one or more vehicle sensors represents a proximity between a first vehicle in which the one or more vehicle sensors are disposed and a second vehicle detected via the one or more vehicle sensors.
11. The system of claim 8 , wherein the parameter collected via the one or more infrastructure devices represents an atmospheric condition.
12. The system of claim 8 , wherein the parameter collected via the one or more infrastructure devices is a health status parameter representing a degree of health for an infrastructure component.
13. The system of claim 8 , wherein the parameter collected via the one or more infrastructure devices is an operational status parameter representing an operational status of an infrastructure component.
14. The system of claim 8 , wherein the parameter collected via the one or more infrastructure devices is a speed collected via a radar device.
15. A tangible, non-transitory computer-readable medium storing executable instructions that, when executed by one or more processors of a computer system, cause the computer system to:
(A) display a graphic user interface (GUI) configured to display at least one of a series of images, the GUI including an image control element interactable to advance forward or backward in time through the series of images;
(B) analyze a displayed image from the series of images displayed within the GUI to identify a timestamp for the displayed image;
(C) retrieve values for a set of road segment parameters based upon the identified timestamp, such that the values for the set of road segment parameters are relevant-in-time to the displayed image, wherein the set of road segment parameters comprise a parameter collected via one or more vehicle sensors and a parameter collected via one or more infrastructure devices;
(D) dynamically calculate one or more risk indices by analyzing the set of road segment parameters using a machine learning model previously trained on synchronized training data including telematics data, contextual data regarding vehicle operating environments that is synchronized with the telematics data, and vehicle incident data from a plurality of past vehicle events;
(E) analyze the set of road segment parameters to generate a behavioral characterization of one or more drivers; and
(F) display the behavioral characterization of the one or more drivers simultaneous to the displaying of the displayed image, such that the behavioral characterization, relevant-in-time to the displayed image, is simultaneously viewable with the displayed image.
16. The tangible, non-transitory computer-readable medium of claim 15 , wherein the parameter collected via the one or more vehicle sensors represents a speed, position, or heading of a vehicle.
17. The tangible, non-transitory computer-readable medium of claim 15 , wherein the parameter collected via the one or more vehicle sensors represents a proximity between a first vehicle in which the one or more vehicle sensors are disposed and a second vehicle detected via the one or more vehicle sensors.
18. The tangible, non-transitory computer-readable medium of claim 15 , wherein the parameter collected via the one or more infrastructure devices represents an atmospheric condition.
19. The tangible, non-transitory computer-readable medium of claim 15 , wherein the parameter collected via the one or more infrastructure devices is a health status parameter representing a degree of health for an infrastructure component.
20. The tangible, non-transitory computer-readable medium of claim 15 , wherein the parameter collected via the one or more infrastructure devices is an operational status parameter representing an operational status of an infrastructure component.