Among other things, equipment is located at an intersection of a transportation network. The equipment includes an input to receive data from a sensor oriented to monitor ground transportation entities at or near the intersection. A wireless communication device sends to a device of one of the ground transportation entities, a warning about a dangerous situation at or near the intersection, there is a processor and a storage for instructions executable by the processor to perform actions including the following. A machine learning model is stored that can predict behavior of ground transportation entities at or near the intersection at a current time. The machine learning model is based on training data about previous motion and related behavior of ground transportation entities at or near the intersection. Current motion data received from the sensor about ground transportation entities at or near the intersection is applied to the machine learning model to predict imminent behaviors of the ground transportation entities. An imminent dangerous situation for one or more of the ground transportation entities at or near the intersection is inferred from the predicted imminent behaviors. The wireless communication device sends the warning about the dangerous situation to the device of one of the ground transportation entities.

The invention relates to a computer-aided method for generating a drive cycle for a vehicle which is suitable for simulating a driving operation, in particular a real driving operation. The computer-aided method comprises establishing a state vector of the drive cycle for a current time interval from a past speed curve, providing an acceleration prediction model, determining an acceleration value in consideration of probabilities resulting from the acceleration prediction model and the state vector, integrating the determined acceleration value over the current time interval in order to obtain a predicted speed value for a next future time interval, and appending the predicted speed value to the past speed curve in order to generate the drive cycle. Furthermore, the invention relates to a device for generating a drive cycle for a vehicle which is suitable for simulating a driving operation, in particular a real driving operation, and means for establishing a state vector of the drive cycle for a current time interval from a past speed curve, means for determining an acceleration value in consideration of probabilities resulting from the acceleration prediction model and the state vector, means for integrating the determined acceleration value over the current time interval in order to obtain a predicted speed value for a next future time interval, and means for appending the predicted speed value to the past speed curve in order to generate the drive cycle.

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.

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.

A method of performing management in a luminaire network comprising a plurality of luminaires and control system, each luminaire being configured to operate in accordance with at least one profile which defines one or more settings of luminaire in function of time, said method comprising performing by said control system: retrieving traffic data from traffic database and weather data; predicting for each luminaire motion related conditions for a future period of time based on the retrieved traffic and weather data, preferably motion related conditions influencing the motion behavior of vehicles and/or pedestrians in the vicinity of the luminaire; based on predicted conditions, determining whether the at least one profile needs to be changed for said future period and/or at least one profile to be used for said future period and/or at least one value representative for at least one profile to be used for said future period.

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.

At least a method for determining risk behavior of a driver is described. While a vehicle is being driven, data is obtained related to the position and movement of a wireless communications device. The data may indicate the type of behavior exhibited by the driver while the vehicle is being driven.

An apparatus for recording license plates of vehicles travelling on a road having several adjacent lanes comprises a vehicle classification sensor configured to detect a predetermined shape characteristic of a vehicle or group of vehicles. The apparatus further comprises at least one camera mounted at an elevated point beside one of the lanes and having an angle of aperture covering at least one of said lanes, each lane covered by at least one camera. The vehicle classification sensor is configured to, upon detecting the predetermined shape characteristic on a lane, trigger the camera that covers the lane the predetermined shape characteristic is detected on to record an image of a license plate on the back of the vehicle or group's leading vehicle, respectively, for which the predetermined shape characteristic is detected. The triggered camera is of a lane either adjacent to or at least one lane apart from the lane.