A method, apparatus and computer program product are provided to determine lane status confidence indicators of lane status predictions such as closures and/or shifting. Lane statuses and corresponding confidence indicators are determined based on probe data, such as probe data collected from vehicle and/or mobile devices traveling along a road segment. Probe data may be partitioned into clusters and compared to partitioned subsets of the probe data. Cluster stability for the segment and corresponding lane status confidence indicators can be determined based on the comparison. Accordingly, determinations of whether to transmit predicted lane statuses to another system, service, and/or user device may be made.

A method may include obtaining input information relating to an environment in which an autonomous vehicle (AV) operates, the input information describing at least one of: a state of the AV, an operation of the AV within the environment, a property of the environment, or an object included in the environment. The method may include identifying a first object in the vicinity of the AV based on the obtained input information. The method may include determining a first object rule corresponding to the first object, the first object rule indicating suggested driving behavior for interacting with the first object. The method may include determining a first decision that follows the first object rule and sending an instruction to a control system of the AV, the instruction describing a given operation of the AV responsive to the first object rule according to the first decision.

There is provided an information processing apparatus including: a receiving control unit configured to perform a control to receive information indicating a risk area ahead in a movement direction of a moving object, from a server configured to retain information relating to a risk area; a prediction unit configured to predict a change in the movement direction of the moving object; and a determination unit configured to determine whether the movement direction of the moving object has been changed to the direction predicted by the prediction unit, in which the receiving control unit is configured to perform a control to receive the information indicating the risk area, when the determination unit determines that the movement direction of the moving object has been changed to the direction predicted by the prediction unit.

A system configured to, and method of, generating and providing a data product using data supplied by a multitude of vehicles. The system/method includes carrying out a re-segmentation process in which an initial set of road segments are processed so as to obtain a re-segmented set of road segments; attributing traffic data to at least a subset of the re-segmented set of road segments, wherein, for each road segment of the subset of road segments, a portion of the traffic data is attributed to the road segment based on geographical proximity; carrying out a road segment merging process on the re-segmented set of road segments in order to obtain a merged set of road segments; aggregating metrics associated with the merged set of road segments to obtain aggregated road segment data; generating the data product using the aggregated road segment data; and providing the data product to a third party.

This document describes techniques and systems for improving accuracy of predictions on radar data using vehicle-to-vehicle (V2V) technology. V2V communications data and the matching sensor data related to one or more vehicles in the vicinity of a host vehicle are collected. The V2V data is used as label data and the radar data is used as the input data for training the model. The training may either occur onboard the host vehicle or remotely. Further, multiple host vehicles may contribute data to train the model. Once the model has been updated with the included training, the updated model is deployed to the sensor tracking system of the host vehicle. By using the dataset that includes the V2V communications data and the matching sensor data, the updated model may accurately track other vehicles and enable the host vehicle to utilize advanced driver-assistance systems safely and reliably.

Disclosed herein is a technique for obtaining information relating to a navigable network from devices (12) that are associated with users travelling within the navigable network. For example, a central server can issue requests to the devices (12) for automatically obtaining sensor data, with a request including a set of instructions for obtaining sensor data from one or more sensor(s) (13) accessible by the device (12). The request also includes a location-specific trigger. Thus, when it is determined that the device (12) has reached the location associated with the trigger, the device is able to automatically action the instructions in order to obtain the requested sensor data, which can then be reported back to the server.

A driver alert system for an automobile includes a global positioning system adapted to monitor a location of an automobile, and a processor adapted to receive data from the global positioning system, receive data of historical wildlife position and migration habits within a pre-determined range from the automobile, calculate a distance from the automobile to an area of wildlife activity as indicated by the data of historical wildlife position and migration habits within the pre-determined range from the automobile, and provide an alert to a driver of the automobile when the data of historical wildlife position and migration habits indicates wildlife movement within the pre-determined range of the automobile and the automobile is within a triggering distance of such wildlife movement.

A method of predicting evolution of simulation results for an Internet of Things (IoT) network comprising creating a source digital twin outputting a state of object(s). A main digital twin sequence is formed by creating clone digital twin(s), connecting an input of one clone digital twin with an output of the source digital twin where a time increment is added to the output of the source digital twin and connecting an input of any further clone digital twin with an output of a preceding clone digital twin where a further time increment is added to the output of the preceding clone digital twin. An evolved modified state of the object(s) is provided at additionally incremented time as an output of an exploratory digital twin which has an input connected with an output of one of the source digital twin, the one clone digital twin, and any further clone digital twin.

An on-board unit (OBU) of a vehicle receives one or more data messages indicative of intersection geometry for an upcoming intersection along a roadway being traversed by the vehicle and traffic control status of a traffic control of the intersection. An outbound direction for the vehicle through the intersection is identified. A first traffic message is sent to preempt the traffic control to allow the vehicle to perform a maneuver to traverse the intersection in the outbound direction. The maneuver is indicated as complete and a second traffic message is sent to discontinue the preempt of the traffic control.

An IoV message exchange method performed by the server includes: obtaining position information of a first roadside unit, position information of a second roadside unit, and a roadside unit density reference value, the first roadside unit and the second roadside unit are on a first road section and are two adjacent roadside units, and the first road section is a road section between the first roadside unit and the second roadside unit; determining a density of roadside units on the first road section based on the position information and the roadside unit density reference value; determining a downlink message sending policy based on the density of the roadside units on the first road section; and sending the first IoV message to a first vehicle-mounted device according to the downlink message sending policy, the first vehicle-mounted device is a vehicle-mounted device on the first road section.