Abstract: A device may receive a ticket identifier associated with a ticket, a vehicle identifier associated with a vehicle, and video data tracking the vehicle to a parking spot in a parking area. The device may process the video data, with models, to identify the parking spot and a parking spot location, and may associate the ticket identifier, the vehicle identifier, and the parking spot location. The device may receive a starting location of a user device associated with a user of the vehicle, and the ticket identifier based on the user scanning the ticket. The device may determine the parking spot location based on the ticket identifier, and may process the starting location and the parking spot location, with a path model, to calculate a navigation path from the starting location to the parking spot location. The device may provide the navigation path to the user device.

Abstract: A device may receive a ticket identifier associated with a ticket, a vehicle identifier associated with a vehicle, and video data tracking the vehicle to a parking spot in a parking area. The device may process the video data, with models, to identify the parking spot and a parking spot location, and may associate the ticket identifier, the vehicle identifier, and the parking spot location. The device may receive a starting location of a user device associated with a user of the vehicle, and the ticket identifier based on the user scanning the ticket. The device may determine the parking spot location based on the ticket identifier, and may process the starting location and the parking spot location, with a path model, to calculate a navigation path from the starting location to the parking spot location. The device may provide the navigation path to the user device.

Abstract: A video summary device may determine, based on video data, a plurality of events that occurred during a period of time. The video summary device may determine first measures of relevance of a plurality of portions of the period of time. A first measure of relevance of a portion of time may be determined based on one or more events of the plurality of events. The video summary device may determine second measures of relevance of a plurality of ranges of time between different portions of time of the plurality of portions of the period of time. The second measures of relevance is determined based on the first measures of relevance determined for the different portions of time. The video summary device may determine particular frames of the video data, as a video summary of the video data, based on the second measures of relevance.

Abstract: In some implementations, a device may receive an image that depicts an environment associated with a vehicle. The device may partition the image into a plurality of subsections. The device may analyze the plurality of subsections to determine respective subsection information, wherein subsection information, for an individual subsection, indicates: a probability score that the subsection includes a line segment associated with an object class, a position of a representative point of the line segment, and a direction of the line segment. The device may identify, based on the respective subsection information of the plurality of subsections, a line associated with the object class that is associated with a set of subsections of the plurality of subsections. The device may perform one or more actions based on identifying the line associated with the object class.

Abstract: A device may receive geographical data identifying a geographical area, and may receive, from vehicle devices of vehicles, first vehicle data identifying engine off conditions, locations during engine off conditions, and durations of the engine off conditions. The device may divide, based on the geographical data, the geographical area into clusters with particular dimensions, and may process data identifying the clusters and the first vehicle data, with a machine learning model, to determine parking data identifying public parking spaces in the geographical area. The device may receive, from a set of the vehicle devices associated with vehicles parked in the public parking spaces, vehicle data identifying engine on conditions and locations during the engine on conditions, and may identify available public parking spaces based on the second vehicle data and the parking data. The device may perform one or more actions based on data identifying the available public parking spaces.

Abstract: In some implementations, a device may receive an image that depicts an environment associated with a vehicle. The device may partition the image into a plurality of subsections. The device may analyze the plurality of subsections to determine respective subsection information, wherein subsection information, for an individual subsection, indicates: a probability score that the subsection includes a line segment associated with an object class, a position of a representative point of the line segment, and a direction of the line segment. The device may identify, based on the respective subsection information of the plurality of subsections, a line associated with the object class that is associated with a set of subsections of the plurality of subsections. The device may perform one or more actions based on identifying the line associated with the object class.

Abstract: A device may obtain video data associated with a driving event involving a first vehicle. The device may determine a vanishing point associated with the video data and may construct a cone of impact of the first vehicle based on the vanishing point. The device may detect a second vehicle within the cone of impact and may analyze the subset of video frames to determine a distance between the first vehicle and the second vehicle. The device may determine a speed of the first vehicle during a time period associated with a subset of video frames. The device may determine a headway score, representative of a severity associated with the first vehicle being within a proximity threshold of the second vehicle during the time period, based on the distance and the speed. The device may determine an occurrence of a tailgating event based on the headway score.

Abstract: A device receives location data that identifies geographic locations of a vehicle at corresponding times, and speed data that identifies respective speeds of the vehicle at the set of corresponding times. The device identifies, based on the location data, speed limit data that identifies respective speed limits at the geographic locations. The device determines, based on the respective speeds and the respective speed limits, a set of respective relative speed values associated with the vehicle. The device identifies respective relative speed values, of the set of respective relative speed values, that exceed a threshold. The device determines, based on the respective relative speed values, a univocal speed indicator that represents an overall degree to which the respective relative speed values exceed the threshold, during a time interval that includes the corresponding times. The device performs a set of actions based on the univocal speed indicator.

Abstract: A device may obtain video data associated with a driving event involving a first vehicle. The device may determine a vanishing point associated with the video data and may construct a cone of impact of the first vehicle based on the vanishing point. The device may detect a second vehicle within the cone of impact and may analyze the subset of video frames to determine a distance between the first vehicle and the second vehicle. The device may determine a speed of the first vehicle during a time period associated with a subset of video frames. The device may determine a headway score, representative of a severity associated with the first vehicle being within a proximity threshold of the second vehicle during the time period, based on the distance and the speed. The device may determine an occurrence of a tailgating event based on the headway score.

Abstract: Embodiments described herein provide for the generation of models, which may predict or otherwise model road safety conditions. For example, embodiments may determine attributes of roads and/or segments of roads based on sensor data, statistical data, and/or other suitable data to determine road safety models associated with the roads or road segments. Such models may be used when determining navigation routes, controlling autonomous vehicles, city planning, and/or other suitable operations that may take road safety into account.

Abstract: In some implementations, a device may receive an image that depicts an environment associated with a vehicle. The device may partition the image into a plurality of subsections. The device may analyze the plurality of subsections to determine respective subsection information, wherein subsection information, for an individual subsection, indicates: a probability score that the subsection includes a line segment associated with an object class, a position of a representative point of the line segment, and a direction of the line segment. The device may identify, based on the respective subsection information of the plurality of subsections, a line associated with the object class that is associated with a set of subsections of the plurality of subsections. The device may perform one or more actions based on identifying the line associated with the object class.

Abstract: A device may obtain video data associated with a driving event involving a first vehicle. The device may determine a vanishing point associated with the video data and may construct a cone of impact of the first vehicle based on the vanishing point. The device may detect a second vehicle within the cone of impact and may analyze the subset of video frames to determine a distance between the first vehicle and the second vehicle. The device may determine a speed of the first vehicle during a time period associated with a subset of video frames. The device may determine a headway score, representative of a severity associated with the first vehicle being within a proximity threshold of the second vehicle during the time period, based on the distance and the speed. The device may determine an occurrence of a tailgating event based on the headway score.

Abstract: A device may receive geographical data identifying a geographical area, and may receive, from vehicle devices of vehicles, first vehicle data identifying engine off conditions, locations during engine off conditions, and durations of the engine off conditions. The device may divide, based on the geographical data, the geographical area into clusters with particular dimensions, and may process data identifying the clusters and the first vehicle data, with a machine learning model, to determine parking data identifying public parking spaces in the geographical area. The device may receive, from a set of the vehicle devices associated with vehicles parked in the public parking spaces, vehicle data identifying engine on conditions and locations during the engine on conditions, and may identify available public parking spaces based on the second vehicle data and the parking data. The device may perform one or more actions based on data identifying the available public parking spaces.

Abstract: A device may receive geographical data identifying a geographical area, and may receive, from vehicle devices of vehicles, first vehicle data identifying engine off conditions, locations during engine off conditions, and durations of the engine off conditions. The device may divide, based on the geographical data, the geographical area into clusters with particular dimensions, and may process data identifying the clusters and the first vehicle data, with a machine learning model, to determine parking data identifying public parking spaces in the geographical area. The device may receive, from a set of the vehicle devices associated with vehicles parked in the public parking spaces, vehicle data identifying engine on conditions and locations during the engine on conditions, and may identify available public parking spaces based on the second vehicle data and the parking data. The device may perform one or more actions based on data identifying the available public parking spaces.

Abstract: A device receives location data that identifies geographic locations of a vehicle at corresponding times, and speed data that identifies respective speeds of the vehicle at the set of corresponding times. The device identifies, based on the location data, speed limit data that identifies respective speed limits at the geographic locations. The device determines, based on the respective speeds and the respective speed limits, a set of respective relative speed values associated with the vehicle. The device identifies respective relative speed values, of the set of respective relative speed values, that exceed a threshold. The device determines, based on the respective relative speed values, a univocal speed indicator that represents an overall degree to which the respective relative speed values exceed the threshold, during a time interval that includes the corresponding times. The device performs a set of actions based on the univocal speed indicator.

Abstract: A device receives location data that identifies geographic locations of a vehicle at corresponding times, and speed data that identifies respective speeds of the vehicle at the set of corresponding times. The device identifies, based on the location data, speed limit data that identifies respective speed limits at the geographic locations. The device determines, based on the respective speeds and the respective speed limits, a set of respective relative speed values associated with the vehicle. The device identifies respective relative speed values, of the set of respective relative speed values, that exceed a threshold. The device determines, based on the respective relative speed values, a univocal speed indicator that represents an overall degree to which the respective relative speed values exceed the threshold, during a time interval that includes the corresponding times. The device performs a set of actions based on the univocal speed indicator.

Abstract: A device receives location data that identifies geographic locations of a vehicle at corresponding times, and speed data that identifies respective speeds of the vehicle at the set of corresponding times. The device identifies, based on the location data, speed limit data that identifies respective speed limits at the geographic locations. The device determines, based on the respective speeds and the respective speed limits, a set of respective relative speed values associated with the vehicle. The device identifies respective relative speed values, of the set of respective relative speed values, that exceed a threshold. The device determines, based on the respective relative speed values, a univocal speed indicator that represents an overall degree to which the respective relative speed values exceed the threshold, during a time interval that includes the corresponding times. The device performs a set of actions based on the univocal speed indicator.

Abstract: A device may receive geographical data identifying a geographical area, and may receive, from vehicle devices of vehicles, first vehicle data identifying engine off conditions, locations during engine off conditions, and durations of the engine off conditions. The device may divide, based on the geographical data, the geographical area into clusters with particular dimensions, and may process data identifying the clusters and the first vehicle data, with a machine learning model, to determine parking data identifying public parking spaces in the geographical area. The device may receive, from a set of the vehicle devices associated with vehicles parked in the public parking spaces, vehicle data identifying engine on conditions and locations during the engine on conditions, and may identify available public parking spaces based on the second vehicle data and the parking data. The device may perform one or more actions based on data identifying the available public parking spaces.