| CPC G06F 18/251 (2023.01) [B60W 30/08 (2013.01); B60W 30/182 (2013.01); B60W 40/06 (2013.01); B60W 40/105 (2013.01); G01C 21/3461 (2013.01); G01C 21/3484 (2013.01); G01C 21/3602 (2013.01); G05D 1/0088 (2013.01); G06F 3/0484 (2013.01); G06F 16/29 (2019.01); G06F 16/54 (2019.01); G06F 16/5866 (2019.01); G06F 16/587 (2019.01); G06N 20/00 (2019.01); G06Q 10/20 (2013.01); G06Q 50/26 (2013.01); G06T 7/20 (2013.01); G06T 7/292 (2017.01); G06T 7/70 (2017.01); G06V 20/52 (2022.01); G06V 20/54 (2022.01); G06V 20/56 (2022.01); G06V 20/58 (2022.01); G06V 20/584 (2022.01); G06V 20/588 (2022.01); G07C 5/008 (2013.01); G08G 1/0112 (2013.01); G08G 1/052 (2013.01); G11B 27/34 (2013.01); H04N 7/18 (2013.01); H04W 4/44 (2018.02); B60W 2030/082 (2013.01); B60W 2420/403 (2013.01); B60W 2420/408 (2024.01); B60W 2520/10 (2013.01); B60W 2552/20 (2020.02); B60W 2552/53 (2020.02); B60W 2556/50 (2020.02); B60W 2556/55 (2020.02); G01C 21/3815 (2020.08); G01C 21/3848 (2020.08); G06Q 10/10 (2013.01); G06Q 40/08 (2013.01); G06T 2207/30252 (2013.01); H04L 67/12 (2013.01)] | 20 Claims |

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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.
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