A traffic light management system that manages display of traffic lights includes a communication device, a control device, and a map database. The control device performs emergency control of the display when abnormal traveling information indicating the information on an abnormally traveling vehicle is included in the information received from a communication vehicle. In the emergency control, the control device predicts a future trajectory of the abnormally traveling vehicle based on the abnormal traveling information. Next, the control device generates the emergency control signal for temporarily prohibiting passage at an intersection and a crosswalk on a road along the future trajectory based on the map information and the future trajectory. After that, the control device sends the emergency control signal to controlled traffic lights. The controlled traffic lights are each a traffic light at the intersection and the crosswalk on a road along the future trajectory.

A method comprises determining a vectorized representation of positions of road agents and road geometry based on sensor data from a vehicle, inputting the vectorized representation of the positions of the road agents and the road geometry into a trained transformer network, predicting one or more road agent trajectories at one or more future time steps based on an output of the transformer network, predicting an acceleration of the vehicle at the one or more future time steps based on the predicted one or more road agent trajectories at the one or more future time steps, and causing the vehicle to perform the predicted acceleration at the one or more future time steps.

In an example, a method determines a first controllable vehicle traveling along a mitigation road segment of a roadway and determines a control lane in the mitigation road segment. The control lane includes the first controllable vehicle and is impedible by the first controllable vehicle. The method determines a first open lane in the mitigation road segment and applies a target mitigation speed to the first controllable vehicle in the control lane. The first open lane is adjacent to the control lane in the mitigation road segment and the target mitigation speed is based on a traffic state of the first open lane. The target mitigation speed adjusts a traffic stream that flows through the first open lane to mitigate traffic congestion located downstream of the mitigation road segment.

A method, system and apparatus for accessing an internal database that includes at least one payroll database, identifying an individual, a first location, a second location, a future date, and a time of day. Responsive to identifying the individual, the first location, the second location, the date, and the time of day, determining a predicted travel time for the individual between the first location and the second location at the time of day on the future date.

Devices and methods for improving the flow of traffic at an intersection are disclosed, such as a traffic light assistance system comprising a light emitter, a light-directing device, a vehicle sensor, a traffic signal detector, and a control unit. The traffic light assistance system can be configured to detect a vehicle and direct projected light toward said vehicle to indicate a change of status of a traffic light. Such devices and methods can alert an operator of a vehicle to a change of status of a traffic light, such as a traffic signal turning green, and indicating that the operator should accelerate the vehicle. By providing means to alert otherwise distracted or inattentive drivers, time spent in traffic can be reduced.

A method can be used for adaptive parking of a vehicle. A parking area is determined around a programmed destination of the vehicle. The parking area has more than one available parking spot for the vehicle. Parking data is acquired via a wireless communication network. The parking data for each parked vehicle includes a parking position and an individual sensing coverage of an environment sensor system of the respective parked vehicle scanning the traffic environment within the parking area. Available parking spots are ranked based on a calculated overall sensing coverage and a recommended parking spot is determined among the available parking spots based on overall sensing coverage of the traffic environment in the parking area.

This application discloses a vehicle driving exit prediction method includes: obtaining a first location of the target vehicle at the target intersection and a driving direction of the target vehicle; obtaining N reference points respectively associated with N driving exits of the target intersection, where N is a positive integer; obtaining, based on the first location, the driving direction of the target vehicle, and the N reference points, likelihoods corresponding to the N driving exits respectively, where the likelihood indicates a probability that the target vehicle travels out of the target intersection from a corresponding driving exit; and obtaining a driving exit with a largest likelihood in the N driving exits as the driving exit of the target vehicle.

According to an embodiment, an information processing device includes one or more processors. The one or more processors are configured to: build a prediction model for predicting prediction data for a second region included in a plurality of regions based on acquired data acquired for one or more first regions included in the plurality of regions; and predict the prediction data based on the acquired data by using the prediction model.

In accordance with an embodiment, a method for processing information associated with vehicular traffic includes monitoring two signals that include information associated with vehicular traffic. A comparison signal is generated by comparing the two signals. The two signals may be wireless signals, or one of the two signals may be a hardwired signal.

In one embodiment, example systems and methods related to a manner of unifying heterogeneous datasets are provided. Multiple heterogeneous datasets containing traffic or driving data are collected. The records of the datasets are combined, and the records in the combined dataset are ordered into a plurality of time series based on timestamps associated with each record. A Bayesian learning method, such as hidden Markov models, is used to identify traffic primitives in the datasets. Each traffic primitive may include several consecutive records in the combined dataset and may correspond to particular driving actions such as turning left or right, stopping, accelerating, etc. The traffic primitives are used to create a traffic primitive index that can be queried by users or researchers for specific records. These records can be used to train or test one or more learning-based algorithms. In addition, the combined dataset can be further divided into tables corresponding to particular sensors, allowing the users or researchers to query for specific traffic primitive and sensor combinations.