Systems and methods are provided for predicting blind spot incursions for a host vehicle. In one implementation, a navigation system for a host vehicle may comprise a processor. The processor may be programmed to receive, from an image capture device located on a rear of the host vehicle, at least one image representative of an environment of the host vehicle. The processor may be programmed to analyze the at least one image to identify an object in the environment of the host vehicle and to determine kinematic information associated with the object. The processor may further be programmed to predict, based on the kinematic information, that the object will travel in a region outside of a field of view of the image capture device and perform a control action based on the prediction.

The present disclosure provides a method and apparatus for detecting a traffic anomaly, a device, a storage medium and a computer program product, relates to the field of artificial intelligence, and specifically to computer vision and deep learning technologies, and can be applied to intelligent transportation scenarios. A specific implementation of the method comprises: acquiring a traffic video stream; performing vehicle detection tracking on the traffic video stream to determine whether there is an abnormally stopped vehicle, wherein a stop with a time length exceeding a preset time length belongs to an abnormal stop; and performing a traffic anomaly classification on a video frame corresponding to the abnormal stop using a decision tree to obtain a traffic anomaly type, if there is the abnormally stopped vehicle, wherein the decision tree is generated based on features for a traffic anomaly detection.

An information processing apparatus includes a controller configured to: acquire mobile body information including information that indicates the respective positions of a plurality of mobile bodies that are usable for machine learning; and select two or more mobile bodies to be used for machine learning based on the mobile body information.

The present invention relates to a method for alerting drivers and/or autonomous vehicles of high risk scenarios. The method includes obtaining positional data of a vehicle, where the positional data is indicative of geographical position and heading of the vehicle. The method further includes obtaining environmental data of the vehicle, where the environmental data is indicative of state of the surrounding environment of the vehicle. The method includes determining, by means of trained model, accident intensity for upcoming road portion for the vehicle, the trained model being configured to determine accident intensity associated with the upcoming road portion based on the obtained environmental data and the obtained positional data. Then, if the determined accident intensity exceeds threshold, the method comprises transmitting signal indicating approaching high risk scenario to a Human-Machine-Interface, HMI, of the vehicle and/or to a control system of the vehicle.

Implementations for an intelligent road barrier (IRB) system can include a plurality of road barriers located along a roadway, and a plurality of IRB kits, each IRB kit being including a set of sensors configured to obtain sensor data representative of at least a portion of a road state of the roadway proximate to the respective road barrier, a data processing unit configured to process at least a portion of the sensor data, a communication unit configured to send at least a portion of the sensor data and to receive sensor data from the one or more other IRB kits, and a state conveyance system configured to convey at least a portion of road state data to one or more vehicles associated with the roadway, the at least a portion of road state data being determined based on the at least a portion of the sensor data.

An apparatus comprising a processor and memory including computer program code, the memory and computer program code configured to, with the processor, enable the apparatus at least to: generate an indication of one or more communication network receiver upload zones for a road data gatherer along a planned route to be travelled by the road data gatherer, the indication determined according to a predetermined upload criterion, which takes account of the bandwidth capacities of communication network receivers along the planned route for the road data gatherer, to specify communication network receivers to be used by the road data gatherer for uploading of road data; and provide the indication to guide uploading of the road data gathered by the road data gatherer.

A vehicle control device includes a communication unit configured to communicate with a plurality of autonomous driving vehicles configured to perform autonomous traveling, and a processor. The processor is configured to, when an abnormality occurs in or around at least one first autonomous driving vehicle among the plurality of autonomous driving vehicles, determine a travel instruction for controlling traveling of the first autonomous driving vehicle, transmit the travel instruction to the first autonomous driving vehicle via the communication unit, set a priority representing the degree of the priority in which an instruction operator is notified of the travel instruction in the order determined according to the content of the abnormality, notify any one of at least one instruction terminal of the determined travel instruction in the order of highest priority, and receive a result of checking the determined travel instruction from the instruction terminal.

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.

Systems and methods for managing speed thresholds for a fleet of vehicles are disclosed. Input is used to provide associations between particular weather-relation conditions (such as rain) and arithmetic operations, that may be used to determine a current speed threshold as a function of a local posted speed limit at the current location of a vehicle. The current speed threshold is subsequently used to detect whether vehicles are exceeding the current speed threshold.

An approach is provided for generating/breaking vehicle paths on a limited graph area. The approach, for example, determining an expected frequency of location data collected from a sensor of a vehicle traveling on a roadway, wherein the location data include a plurality of probe points that are time-sequenced. The approach also involves detecting an exit or an entry of the vehicle on the roadway based on comparing the expected frequency to an observed frequency of the location data on at least one portion of the roadway. The approach further involves initiating an identification, a creation, a breaking, or a combination thereof of a path constructed from the location data based on the detecting of the exit or the entry of the vehicle. The approach further involves providing the path as an output.