An approach is provided for determining road work zone travel time reliability based on vehicle sensor data. The approach involves, for instance, aggregating telematic data generated by vehicles traveling within a geographic area, to detect a road work zone and/or active time period(s) thereof. The approach also involves identifying a start location and an end location of the road work zone. The approach further involves identifying a starting time point when one of the vehicles approaching the start location and an end time point when the vehicle passes the end location. The approach further involves calculating a time difference between the start and end time points as a travel time through the road work zone. The approach further involves aggregating the vehicle travel times, to compute work zone travel time reliability index(es) for the road work zone. The approach further involves providing the travel time reliability index(es) as an output.

A road lamp control method is disclosed, comprising steps of determining whether a road lamp lighting condition is satisfied, and controlling the road lamp lighting time period. The method can control ON and OFF and the brightness of the road lamps in real time based on the real traffic flow at night, and thereby ensuring safety of pedestrians and vehicles and saving electrical energy.

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 present invention relates to a system and a method for correcting position information of a surrounding vehicle, which provide accurate position information of a surrounding vehicle by correcting the position information of the surrounding vehicle received through vehicle-to-vehicle communication, and identifies a license-plate number of a front vehicle through a sensor mounted in a vehicle, calculates a position of the front vehicle, and compare position information, which is included in information including the identified number of the front vehicle in information received from the surrounding vehicle, with the calculated position of the front vehicle to correct the position information of the surrounding vehicle.

The invention relates to a lane change assistant for a vehicle comprising a sensor arrangement for calculating information relating to lanes and other road users. Provided is an arithmetic and logic unit for calculating movement paths for a first lane change and a subsequent second lane change to the original lane. The vehicle driver can accept or initiate the calculated lane changes. An actuator unit can then execute the lane changes calculated by the arithmetic and logic unit and initiated by the vehicle driver upon receipt of a signal from the arithmetic and logic unit.

A system for communicating information indicative of driving conditions, to a driver, using a smart ring are disclosed. An exemplary system includes a smart ring with a ring band having a plurality of surfaces including an inner surface, an outer surface, a first side surface, and a second side surface. The system further includes a processor, configured to obtain data from a communication module within the ring band, or from one or more sensors disposed within the ring band. The obtained data is representative of information indicative of one or more driving conditions to be communicated to the driver. The smart ring also includes a haptic module disposed at least partially within the ring band, and the module being configured to communicate information indicative of the one or more driving conditions.

Methods and systems are described for generating a vehicle-to-vehicle traffic alert. Various aspects may include detecting that an abnormal traffic condition exists in an operating environment of a vehicle and generating a related electronic message. The electronic message may be transmitted via the vehicle's transceiver using a wireless communication to a nearby vehicle to alert the nearby vehicle of the abnormal traffic condition and to allow the nearby vehicle to avoid the abnormal traffic condition.

A method and apparatus for evaluating the driving of a vehicle performing a journey on a road network is disclosed. The method comprises determining a fuel usage rate and an engine speed of a vehicle, and determining when the vehicle is coasting based upon at least one of the fuel usage rate and the engine speed of the vehicle. An acceleration of the vehicle, when the vehicle is determined to be coasting, is compared with a predetermined reference acceleration, and an application of braking during the coasting is determined based on the result of the comparison. A method and apparatus is also disclosed for determining a score indicative of the relative amount of time for which the vehicle is coasting without braking during a journey.

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.

Provided is a system configured to determine and push adjustments to vehicle operations using machine-learning systems across multiple computing layers.