Methods and devices for using a relationship between activities of different traffic signals in a network to improve traffic signal state estimation are disclosed. An example method includes determining that a vehicle is approaching an upcoming traffic signal. The method may further include determining a state of one or more traffic signals other than the upcoming traffic signal. Additionally, the method may also include determining an estimate of a state of the upcoming traffic signal based on a relationship between the state of the one or more traffic signals other than the upcoming traffic signal and the state of the upcoming traffic signal.

Disclosed is a method for joint parking, the method including selecting a number of target vehicles participating in the joint parking by a reference vehicle, generating joint parking information based on the selected number of the joint parking vehicles by the reference vehicle, transmitting the generated joint parking information to the target vehicle, and controlling the joint parking to be performed based on the joint parking information.

A communication device includes a communication unit configured to receive first path information from a first vehicle and to receive second path information from a second vehicle, and a processor configured to determine a possibility of the first and second vehicles entering a predetermined area based on the first and second path information, and based on the possibility being greater than a reference, transmit a message for controlling at least one of the first vehicle or the second vehicle through the communication unit to allow the first and second vehicles to enter the predetermined area at different time points.

A travelling support system includes a receiving unit configured to receive parked/stopped vehicle information indicating a detection of a parked/stopped vehicle on a travelling road from a preceding vehicle which is travelling or a sensor on the travelling road; and a predicting unit that predicts, based on the parked/stopped vehicle information and a duration predicting model that predicts a parked/stopped duration as a duration in which the parked/stopped vehicle continues to park or stop, the parked/stopped duration.

A system and method for determining a predicted trajectory of a human-driven host vehicle as the human-driven host vehicle approaches a traffic signal. The method includes: obtaining a host vehicle-traffic light distance d.sub.x and a longitudinal host vehicle speed v.sub.x that are each taken when the human-driven host vehicle approaches the traffic signal; obtaining a traffic light signal phase P.sub.t and an traffic light signal timing T.sub.t; obtaining a time of day TOD; providing the host vehicle-traffic light distance d.sub.x, the longitudinal host vehicle speed v.sub.x, the traffic light signal phase P.sub.t, the traffic light signal timing T.sub.t, and the time of day TOD as input into an artificial intelligence (AI) vehicle trajectory prediction application, wherein the AI vehicle trajectory prediction application implements an AI vehicle trajectory prediction model; and determining the predicted trajectory of the human-driven host vehicle using the AI vehicle trajectory prediction application.

Provided herein is a system and method of a vehicle. The system comprises one or more sensors, processors, maps, and a memory storing instructions that, when executed by the one or more processors, causes the system to perform: monitoring a location of the vehicle while driving; detecting a sign while the vehicle is driving; capturing, frame-by-frame, data of the sign until the sign disappears from a field of view of the sensor; synchronizing each frame of the data with the location of the vehicle; determining a location of the sign based on the frame-by-frame data; in response to determining, at a frame immediately before the sign disappears from the field of view of the sensor, that the vehicle is driving towards the sign, uploading the detected sign and the location of the sign onto the one or more maps; and implementing a driving action based on the sign.

Techniques for determining a location and type of traffic-related features and using such features in route planning are discussed herein. The techniques may include determining that sensor data represents a feature such as a traffic light, road segment, building type, and the like. The techniques further include determining a cost of a feature, whereby the cost is associated with the effect of a feature on a vehicle traversing an environment. A feature database can be updated based on features in the environment. A cost of the feature can be used to update costs of routes associated with the location of the feature.

An autonomous driving method, an autonomous driving apparatus, a computer-readable storage medium, and a computer program product are provided. The method includes: receiving first driving-related information of a first road section ahead of a road on which a first vehicle currently drives and information about a parking waiting area that are sent by a network side device, where the parking waiting area is used to park the first vehicle before the first vehicle drives into the first road section (S304); then determining, based on the first driving-related information, that the first road section does not meet an autonomous driving condition requirement of the first vehicle (S305); finally, controlling the first vehicle to drive into the parking waiting area (S306).

A computing framework for addressing a variety of vehicle conditions includes receiving, from a first set of sensors by an edge compute node, first transportation network data associated with a transportation network region, receiving, from a second set of sensors by a cloud computing node, second transportation network data associated multiple transportation network regions, providing, by the edge compute node to one or more autonomous vehicles at the transportation network region, real-time transportation network region information based on at least the first transportation network data to facilitate control decisions by the one or more autonomous vehicles, and providing, by the cloud computing node to at least the one or more autonomous vehicles, non-real-time transportation network region information based on at least the second transportation network data to facilitate the control decisions by the at least one or more autonomous vehicles.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for monitoring a dedicated roadway the runs in parallel to a railroad. In some implementations, a system includes a central server, an interface, and sensors. The interface receives data from a railroad system that manages the railroad parallel to the dedicated roadway. The sensors are positioned in a fixed location relative to the dedicated roadway. Each sensor can detect vehicles in a first field of view on the dedicated roadway. For each detected vehicle, each sensor can generate sensor data based on the detected vehicle in the dedicated roadway and the data received at the interface. Each sensor can generate observational data and instruct the detected vehicle to switch to an enhanced processing mode. Each sensor can determine an action for the detected vehicle to take based on the generated observational data.