There is provided herein a system for tracking a user's use of available modes of transportation during a trip, the system comprising: a GNSS receiver configured; an IMU; and one or more processors that operates in accordance with a set of instructions stored in a memory to: generate a geotemporal record of a travel route of the trip based on location data provided by the GNSS and inertial data provided by the IMU; and determine, based on the geotemporal record, at least one vehicular trip segment. There is also provided herein a system for tracking a user's use of available modes of transportation during a trip, the system comprising: a remote hub comprising a transportation data repository; and one or more processors to determine, based on a user record of the trip and the transportation data repository, at least one vehicular trip segment.

An upper parking meter outer housing component that includes a parking meter cap is provided. The parking meter cap includes an outer surface, an inner surface defining an interior cavity, and a lower edge portion defining an opening into the interior cavity. The lower edge portion is configured to be coupled to a lower housing component of single space meter such that a meter mechanism may be enclosed within the interior cavity of the upper meter dome. The meter cap includes a vehicle sensor coupled to the inner surface of the upper meter dome. The meter cap includes a solar panel coupled to the inner surface of the upper meter dome and configured to supply power to the vehicle sensor.

An upper parking meter outer housing component that includes a parking meter cap is provided. The parking meter cap includes an outer surface, an inner surface defining an interior cavity, and a lower edge portion defining an opening into the interior cavity. The lower edge portion is configured to be coupled to a lower housing component of single space meter such that a meter mechanism may be enclosed within the interior cavity of the upper meter dome. The meter cap includes a vehicle sensor coupled to the inner surface of the upper meter dome. The meter cap includes a solar panel coupled to the inner surface of the upper meter dome and configured to supply power to the vehicle sensor.

A state estimation method executed by a state estimation apparatus including a processor and a memory storing program instructions that cause the processor to estimate a state related to congestion on a subject lane, includes acquiring a state related to congestion of a monitoring vehicle traveling on a non-subject lane, counting a number of vehicles traveling on the subject lane, the vehicles being overtaken by the monitoring vehicle or being oncoming vehicles passed by the monitoring vehicle, and estimating the state related to the congestion on the subject lane, using the state related to the congestion of the monitoring vehicle and the number of vehicles. In the estimating, when the number of the vehicles is equal to or greater than a threshold value according to the state related to the congestion of the monitoring vehicle, the state related to the congestion on the subject lane is estimated as traffic congestion.

A method for determining a transportation mode acquires magnetometer and speed data from a mobile device, correlates the magnetometer to the speed data in groupings, and performs spectral analysis on the groups of magnetometer data. Energy calculated for each of a set of frequency components obtained from the spectral analysis is compared to a baseline value to generate a difference, and a transportation mode type is assigned to the vehicle based on the difference.

Provided is an apparatus for providing obstacle information and reliability information to vehicle based on information received from roadside units. The apparatus includes a communication module that receives obstacle information from multiple roadside units, each roadside unit including multiple sensors for detecting obstacles within a predetermined field of view. A reliability judgement unit in the apparatus determines a reliability of the received obstacle information to output a reliability value based on a number of roadside units detecting a same obstacle, a number of sensor of one roadside unit detecting a same obstacle, and a difference value of detection of the same obstacle between different roadside units or different sensors.

Systems and methods for verifying stopped traffic and/or fallen object alarms include an image sensor configured to capture a stream of images of a traffic scene, a stopped object component configured to identify a stopped object in the captured images, define an associated object location in the captured images, and classify the detected object. A background modeling system models at least two background images, a slow background image and a fast background image. A DNN is trained to receive the object information and background images to verify the alarm condition.

The present disclosure provides an automated driving vehicle management system, a management method, an automated driving vehicle, and a program that are capable of appropriately managing the automated driving vehicle. The automated driving vehicle management system including: a map information storage unit that stores map information; an imaginary line management unit that manages an imaginary line imaginarily generated for a road included in the map information; and a communication unit that transmits imaginary line information about the imaginary line to a plurality of automated driving vehicles traveling along the imaginary line.

A system and a method for generating traffic information are provided. The system for generating traffic information includes a vehicle that obtains a location and a surrounding image in real time, and a server that receives the location and the surrounding image from the vehicle, and calculates a time required to pass through a specified section and an average speed passing through the specified section based on the location and the surrounding image from the vehicle.

A method for predicting traffic light information by using a LIDAR is provided. The method includes steps of: (a) on condition that each of metadata has been allocated for each of virtual boxes included in a region covered by the LIDAR, obtaining, by a server, at least part of start timing information and stop timing information of a plurality of vehicles for each of the virtual boxes; and (b) predicting, by the server, each of pieces of the traffic light information respectively corresponding to each of the virtual boxes by referring to at least part of the start timing information and the stop timing information of the vehicles for each of the virtual boxes.