The described technology is generally directed towards combining user identity information, corresponding to user profile data, with vehicle information, such as for use by a traffic zone management system that charges a fee for vehicle usage in a traffic zone. A user device, such as a mobile communications device, can be coupled to vehicle information such as via a transponder that is detected by roadside units, or by uploading the combined information to a wireless communications system. Based on the combination of information, different billing rates or the like can be applied to different users and vehicle types. The technology can work with various communication systems, including roadside units and wireless communication networks.

A network system provides interventions to providers to reduce the likelihood that its users will experience safety incidents. The providers provide service to the users such as transportation. Providers who are safe and have positive interpersonal behavior may be perceived by users as high quality providers. However, other providers may be more prone to cause safety incidents. A machine learning model is trained using features derived from service received by users of the network system. Randomized experiments and trained models predict the effectiveness of various interventions on a provider based on characteristics of the provider and the feedback received for the provider. As interventions are sent to providers, the change in feedback can indicate whether the intervention was effective. By providing messages proactively, the network system may prevent future safety incidents from occurring.

A traffic flow estimation apparatus includes: a vehicle number detector which detects a number of preceding vehicles in front of the traffic flow estimation apparatus and; a traffic flow estimator which estimates a traffic flow from the number of preceding vehicles, and the traffic flow estimator includes: an acquisition unit which acquires a time series of the number of preceding vehicles in a first predetermined period as a vehicle number time series; an evaluation index calculation unit which calculates an evaluation index of the vehicle number time series in the first predetermined period; a congestion state determination unit which determines the traffic flow of the preceding vehicles on the basis of the evaluation index; and a traffic flow controller which notifies a following vehicle behind the traffic flow estimation apparatus of an indication with respect to travel on the basis of the traffic flow of the preceding vehicles.

Disclosed is a method for a network to communicate with a vehicle in a wireless communication system. The method may comprise: receiving a location message from a vehicle stopped in a predetermined area; receiving a message about a traffic light system from a traffic light in the predetermined area; and acquiring the vehicle registration number of the stopped vehicle on the basis of a predetermined condition being satisfied. In particular, the predetermined condition may be satisfied when the stopped vehicle is determined to have committed a parking violation on the basis of the location message of the stopped vehicle and the message about the traffic light system.

A method for detecting violation of a driving-related law includes producing, by at least one camera, at least one image of a vehicle. The method includes analyzing, by an analysis engine, the at least one image. The analysis engine identifies, based on analyzing the at least one image, a physical position of a driver of the vehicle within the vehicle and identifies a direction of a gaze of the driver. The analysis engine determines that at least one of the physical position of the driver within the vehicle and the direction of the gaze of the driver are associated with a violation of a driving-related law. The method includes transmitting, by a citation mananagement agement component, a notification of the association between the at least one of the physical position of the driver and the direction of the gaze of the driver and the violation, based upon the determination.

Methods and systems for autonomous vehicle recharging or refueling are disclosed. Autonomous vehicles may be automatically refueled by routing the vehicles to available fueling stations when not in operation, according to methods described herein. A fuel level within a tank of an autonomous vehicle may be monitored until it reaches a refueling threshold, at which point an on-board computer may generate a predicted use profile for the vehicle. Based upon the predicted use profile, a time and location for the vehicle to refuel the vehicle may be determined. In some embodiments, the vehicle may be controlled to automatically travel to a fueling station, refill a fuel tank, and return to its starting location in order to refuel when not in use.

System and methods are disclosed for capturing license plate (LP) information of a vehicle in relative motion to a camera device. In one example, the camera system detects the LP in multiple frames, then aligns and geometrically rectifies the image of the LP by scaling, warping, rotating, and/or performing other functions on the images. The camera system may optimize capturing of the LP information by executing a temporal noise filter on the aligned, geometrically rectified images to generate a composite image of the LP for optical character recognition. In some examples, the camera device may include an image sensor, such as a high dynamic range (HDR) sensor, modified to set long and short exposures of the HDR sensor to capture frames of a vehicle's LP, but without consolidating the images into a composite image. The camera system may set optimal exposure settings based on detected relative speed of the vehicle.

An automated vehicle control distributed network node, that includes at least two modems for communicating with two neighboring roadside nodes on the same side of the roadway; at least one antenna for communicating with vehicles via a wireless connection; pattern recognition processing operative to detect patterns using image data from a plurality of high speed, high resolution video cameras that include night vision; vehicle prediction processing, operatively coupled to the pattern recognition processing, operative to predict vehicle location, velocity and direction using the pattern recognition processing; and a vehicle controller, operatively coupled to the vehicle prediction processing to receive vehicle prediction data, and to the at least one antenna, operative to send acceleration, deceleration and steering control signals to a plurality of vehicles in response to vehicle prediction data received from the vehicle prediction processing.

Techniques for detecting and responding to an emergency vehicle are discussed. A vehicle computing system may determine that an emergency vehicle based on sensor data, such as audio and visual data. In some examples, the vehicle computing system may determine aggregate actions of objects (e.g., other vehicles yielding) proximate the vehicle based on the sensor data. In such examples, a determination that the emergency vehicle is operating may be based on the actions of the objects. The vehicle computing system may, in turn, identify a location to move out of a path of the emergency vehicle (e.g., yield) and may control the vehicle to the location. The vehicle computing system may determine that the emergency vehicle is no longer relevant to the vehicle and may control the vehicle along a route to a destination. Determining to yield and/or returning to a mission may be confirmed by a remote operator.

Techniques for detecting and responding to an emergency vehicle are discussed. A vehicle computing system may determine that an emergency vehicle based on sensor data, such as audio and visual data. In some examples, the vehicle computing system may determine aggregate actions of objects (e.g., other vehicles yielding) proximate the vehicle based on the sensor data. In such examples, a determination that the emergency vehicle is operating may be based on the actions of the objects. The vehicle computing system may, in turn, identify a location to move out of a path of the emergency vehicle (e.g., yield) and may control the vehicle to the location. The vehicle computing system may determine that the emergency vehicle is no longer relevant to the vehicle and may control the vehicle along a route to a destination. Determining to yield and/or returning to a mission may be confirmed by a remote operator.