The object is to provide an information processing apparatus, an information processing method, and a program capable of appropriately handling a difference between detection results of sensors. The solution is an information processing apparatus including: a detection section configured to detect first information regarding a mobile object; an acquisition section configured to acquire second information regarding the mobile object, the second information being detected by an external apparatus; and a control section configured to control processing based on a comparison result of the first information and the second information detected for the same mobile object.

The invention relates to a method for adjusting a display view of a vision aid system (1) of a vehicle (10), the method comprising the following steps: —(S1) identifying a specific situation when the vision aid system should be utilized; —(S2) receiving a display view setting for the specific situation, wherein the display view setting is based on at least one previous display view setting from at least one other vision aid system of another vehicle which has experienced the specific situation, wherein the at least one previous display view setting relates to the specific situation; and —(S3) adjusting the display view of the vision aid system (1) in accordance with the received display view setting. The present invention also relates to a system, to a method for receiving and transmitting display view settings to a fleet of vehicles (100) having vision aid systems, to a system (20) for receiving and transmitting display view settings to a fleet of vehicles (100), to a computer program and/or to a computer readable medium carrying the computer program

A method of determining a user-centric traffic estimation error parameter associated with an estimated road traffic condition that is electronically provided to a user of a device. the method comprises: at a first moment in time, acquiring an estimated travel time for a road segment, the estimated travel time having been computed for the first moment in time; responsive to the device approaching the road segment, displaying an application-generated estimated travel time for the road segment, the application-generated estimated travel time being based on a most recently acquired estimated travel time from the server; responsive to the device departing from the road segment, determining an actual travel time for the road segment; computing the user-centric traffic estimation error parameter based on the application-generated estimated travel time and the actual travel time; and transmitting the user-centric traffic estimation error parameter to the server for adjusting a traffic prediction algorithm.

Techniques are providing for situationally-dependent service prioritization in a CV2X network. An example method of prioritizing data packets with a mobile device includes determining a range to a waypoint, determining an estimated time of arrival at the waypoint, computing a priority value based at least in part on the estimated time of arrival at the waypoint, and generating a data packet based on the priority value.

Methods and systems are disclosed for predicting an outcome associated with a driver of a vehicle using a machine learning statistical model. The disclosed techniques include obtaining a plurality of input vectors for plurality of points in time, wherein each input vector includes a plurality of variables with a weight vector. Each variable represents data captured from a sensor or a data source. A training dataset for the machine learning model is created by capturing the values of outcome of interest for various values of each input vector for each point in time. The outcome of interest is the predicted by utilizing the machine learning model. In various embodiments, the predicted outcome of interest may be a risk or an energy consumption level associated with the driver.

A precipitation index estimation apparatus includes an operation mode data collection unit and an estimation processing unit. The data collection unit is configured to collect operation mode data indicating an operation mode of a windshield wiper, acquired in one or more vehicles positioned in a predetermined area within a predetermined period. The estimation processing unit is configured to estimate a precipitation index indicating an intensity of precipitation in the predetermined area within the predetermined period, based on a proportion of each of a plurality of kinds of operation modes, which is derived from the collected operation mode data.

In some implementations, a mobile device can transmit traffic information to a server for analysis. The traffic information can include movement information including detected stops and durations of detected stops. The traffic information can be analyzed to detect traffic patterns that indicate locations of stop signs and/or stop lights. The traffic information can be analyzed to determine durations of stops at stop signs and/or stop lights. The durations of stops can be associated with a time of day and/or day of the week. In some implementations, navigational routes can be determined based stop sign and/or stop light information, including the delays attributable to detected stop signs and/or stop lights.

Systems and methods are provided for generating a rear view image display for a motor vehicle. A rear view system includes an optical sensor disposed at the motor vehicle and configured to capture image data, a computational unit coupled to the optical sensor by a cable connection and configured to execute program instructions stored on a computer-readable medium to modify the image data for presentation, and a display device coupled to the computational unit and configured to receive the modified image data from the computational unit and display the modified image data to a driver of the motor vehicle. The computational unit is further configured to receive software calibration to optimize modification of the image data.

A method for determining a location of a vehicle is provided. The method includes receiving, at data processing hardware, a first set of vehicle system data from one or more vehicles. The method also includes determining, at the data processing hardware, a data model based on the first set of vehicle system data. Additionally, the method includes receiving, at the data processing hardware, a second set of vehicle system data associated with the vehicle. The vehicle being different than the one or more vehicles. The method includes determining, using the data processing hardware, a vehicle location based on the second set of vehicle system data and the data model. The method includes displaying, on a user interface of the vehicle in communication with the data processing hardware, the vehicle location.

Traffic signal timing plans are derived from vehicle trajectory or probe data. The probe data is collected and archived in a datastore over a sample time on the order of weeks or longer. Probe data is corrected for clock drift, geo-fence filtered to a selected intersection, and then stop line crossings in the intersection are identified and analyzed along with related data to determine the timing plans and schedule for the intersection. In this way, access to government agency timing plans is obviated so as to save time and expense.