Systems and methods for managing speed thresholds for a fleet of vehicles are disclosed. Input is used to provide associations between particular weather-relation conditions (such as rain) and arithmetic operations, that may be used to determine a current speed threshold as a function of a local posted speed limit at the current location of a vehicle. The current speed threshold is subsequently used to detect whether vehicles are exceeding the current speed threshold.

The present disclosure provides a method for managing traffic congestion in a smart city. The method includes predicting, based on a trained traffic state prediction model, one or more target areas where the traffic congestion is likely to occur from the preset area during a next time period by processing the traffic data information during the current time period, the traffic state prediction model being a Graph Neural Network (GNN) model and a predicted result being output by at least one node of a traffic state prediction model; determining whether a traffic scheduling strategy is needed to be switched based on traffic data information in the one or more target areas during the next time period; and in response to determining that the traffic scheduling strategy is needed to be switched, switching a first traffic scheduling strategy to a second traffic scheduling strategy.

The present disclosure provides a method for managing traffic congestion in a smart city. The method includes predicting, based on a trained traffic state prediction model, one or more target areas where the traffic congestion is likely to occur from the preset area during a next time period by processing the traffic data information during the current time period, the traffic state prediction model being a Graph Neural Network (GNN) model and a predicted result being output by at least one node of a traffic state prediction model; determining whether a traffic scheduling strategy is needed to be switched based on traffic data information in the one or more target areas during the next time period; and in response to determining that the traffic scheduling strategy is needed to be switched, switching a first traffic scheduling strategy to a second traffic scheduling strategy.

Techniques for determining predictions on a top-down representation of an environment based on vehicle action(s) are discussed herein. Sensors of a first vehicle (such as an autonomous vehicle) can capture sensor data of an environment, which may include object(s) separate from the first vehicle (e.g., a vehicle or a pedestrian). A multi-channel image representing a top-down view of the object(s) and the environment can be generated based on the sensor data, map data, and/or action data. Environmental data (object extents, velocities, lane positions, crosswalks, etc.) can be encoded in the image. Action data can represent a target lane, trajectory, etc. of the first vehicle. Multiple images can be generated representing the environment over time and input into a prediction system configured to output prediction probabilities associated with possible locations of the object(s) in the future, which may be based on the actions of the autonomous vehicle.

Techniques for determining predictions on a top-down representation of an environment based on vehicle action(s) are discussed herein. Sensors of a first vehicle (such as an autonomous vehicle) can capture sensor data of an environment, which may include object(s) separate from the first vehicle (e.g., a vehicle or a pedestrian). A multi-channel image representing a top-down view of the object(s) and the environment can be generated based on the sensor data, map data, and/or action data. Environmental data (object extents, velocities, lane positions, crosswalks, etc.) can be encoded in the image. Action data can represent a target lane, trajectory, etc. of the first vehicle. Multiple images can be generated representing the environment over time and input into a prediction system configured to output prediction probabilities associated with possible locations of the object(s) in the future, which may be based on the actions of the autonomous vehicle.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a vulnerable road unit (VRU) device may determine that a parameter associated with movement of the VRU device satisfies one or more thresholds indicated in an event reporting configuration. The VRU device may transmit, to a vehicle user equipment device, an indication of an event associated with the VRU device based at least in part on determining that the parameter satisfies the one or more thresholds. Numerous other aspects are provided.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a vulnerable road unit (VRU) device may determine that a parameter associated with movement of the VRU device satisfies one or more thresholds indicated in an event reporting configuration. The VRU device may transmit, to a vehicle user equipment device, an indication of an event associated with the VRU device based at least in part on determining that the parameter satisfies the one or more thresholds. Numerous other aspects are provided.

A collaborative control system for a vehicle has a cloud server, a collaboration device, and an edge computing device. The cloud server stores road coordinates and traffic data corresponding to the road coordinates. The collaboration device is for being mounted in the vehicle. The edge computing device communicates with the cloud server and the collaboration device and is for being mounted in the vehicle. The edge computing device downloads one of the traffic data from the cloud server as an instant traffic datum according to a vehicle positioning coordinate corresponding to one of the road coordinates, and transmits the instant traffic datum to the collaboration device. The collaboration device displays the instant traffic datum.

A collaborative control system for a vehicle has a cloud server, a collaboration device, and an edge computing device. The cloud server stores road coordinates and traffic data corresponding to the road coordinates. The collaboration device is for being mounted in the vehicle. The edge computing device communicates with the cloud server and the collaboration device and is for being mounted in the vehicle. The edge computing device downloads one of the traffic data from the cloud server as an instant traffic datum according to a vehicle positioning coordinate corresponding to one of the road coordinates, and transmits the instant traffic datum to the collaboration device. The collaboration device displays the instant traffic datum.

Provided is an intersection deadlock identification method for a mixed flow of autonomous vehicles. This method considers the reality that the intersection traffic flow is composed of human driven vehicles and connected autonomous vehicles. Firstly, the two-dimensional coordinates, speed and front wheel steering angle information of all vehicles in the intersection are obtained, and the blockage graph of vehicles is constructed on the assumption that the front wheel steering angles of all vehicles are fixed. If there is no ring structure in the blockage graph, there is no deadlock; if there is a ring structure, the evasion distance propagation algorithm is used to calculate the evasion requirement distance of a vehicle in the ring. When the evasion requirement distance is greater than the permitted travelling distance of the vehicle itself, a weak traffic deadlock exists.