A traffic warning method and apparatus includes: obtaining driving status information of a dangerous vehicle on a target road and pavement status information of the target road; determining potential collision strength of the dangerous vehicle against a first vehicle according to driving status information of the first vehicle on the target road, the driving status information of the dangerous vehicle, and the pavement status information of the target road; correcting the potential collision strength of the dangerous vehicle against the first vehicle according to a difference between a performance parameter of the dangerous vehicle and a performance parameter of a non-dangerous vehicle on the target road; and performing traffic warning according to the corrected potential collision strength of the dangerous vehicle against the first vehicle.

Connected and automated vehicles (CAVs) have shown the potential to improve safety, increase road throughput, and optimize energy efficiency and emissions in several complicated traffic scenarios. This invention describes a mixed-integer programming (MIP) optimization method for global multi-vehicle decision making and motion planning of CAVs in a highly dynamic environment that consists of multiple human-driven, i.e., conventional or manual, vehicles and multiple conflict zones, such as merging points and intersections. The proposed approach ensures safety, high throughput and energy efficiency by solving a global multi-vehicle constrained optimization problem. The solution provides a feasible and optimal time schedule through road segments and conflict zones for the automated vehicles, by using information from the position, velocity, and destination of the manual vehicles, which cannot be directly controlled. Despite MIP having combinatorial complexity, the proposed formulation remains feasible for real-time implementation in the infrastructure, such as in mobile edge computers (MECs).

The present disclosure provides an intelligent roadside unit. The intelligent roadside unit includes: a radar configured to detect an obstacle within a first preset range of the intelligent roadside unit; a camera configured to capture an image of a second preset range of the intelligent roadside unit; a master processor coupled to the radar and the camera, and configured to generate a point cloud image according to information on the obstacle detected by the radar and the image detected by the camera; and a slave processor coupled to the radar and the camera, and configured to generate a point cloud image according to the information on the obstacle detected by the radar and the image detected by the camera, in which the slave processor checks the master processor, and when the original master processor breaks down, it is switched from the master processor to the slave processor.

Systems and methods are disclosed for monitoring or affecting movement of a vehicle using a traffic device. An event data source may have a processor and/or a transceiver. The event data source may transmit, via the transceiver and to a vehicle and infrastructure computing device, information indicative of an event affecting a portion of road. The vehicle and infrastructure computing device may comprise a vehicle and infrastructure control computer. The vehicle and infrastructure computing device may receive, from the event data source, the information indicative of the event affecting the portion of road. The computing device may determine one or more traffic devices associated with the portion of road and configured to control traffic for the portion of road. Based on the information indicative of the event affecting the portion of road, the computing device may send, to the one or more traffic devices associated with the portion of road, instructions to change one or more characteristics of the one or more traffic devices.

Mobile wireless terminals, such as vehicles in traffic, can determine the relative positions of other vehicles with improved precision by arranging to acquire GNSS (global navigational satellite system) signals simultaneously, and then analyzing the various data sets differentially. Simultaneous acquisition can cancel many important errors such as motional errors of the vehicles, atmospheric distortions, and satellite timebase errors. Differential analysis to determine the relative positions of vehicles (as opposed to their overall geographical coordinates) can reduce errors related to satellite ephemeris and velocity, as well as roundoff errors. Localization with a precision of less than 1 meter can greatly improve collision avoidance while discriminating near-miss scenarios from imminent collisions, according to some embodiments. Messaging examples, in 5G and 6G, to manage the simultaneous acquisition and differential analysis, are provided in examples. Many other aspects are disclosed.

System for automatically recognising anomalous situations along roads travelled by motor-vehicles for intelligent motor-vehicle driving speed control along roads.

The motor-vehicles are configured to transmit data allowing anomalous situations to be recognised along roads travelled by the motor-vehicles.

The system comprises data processing resources configured to:

receive and process data transmitted by the motor-vehicles to recognise anomalous situations along the roads travelled by the motor-vehicles based on a recognition algorithm,

when anomalous situations are recognised along roads travelled by the motor-vehicles, generate associated alert events and compute reference driving speeds along the roads recognised to be affected by anomalous situations, and

transmit data representative of the alert events and of the reference driving speeds along the roads recognised to be affected by anomalous situations.

The motor-vehicles are further configured to:

receive data representative of alert events and reference driving speeds, and

use the received data to implement one or both of the following actions: inform the drivers of motor-vehicles, through automotive user interfaces of motor-vehicles of the anomalous situations recognised along roads travelled by motor-vehicles, and cause current driving speeds of the motor-vehicles to be adjusted to the reference driving speeds along roads recognised to be affected by anomalous situations.

The traffic flow control method includes: receiving, by a traffic flow control device, traffic control request signaling sent by an in-vehicle device of a first vehicle, where the traffic control request signaling includes travel information of the first vehicle and a travel intention of the first vehicle; determining, by the traffic flow control device, traffic command signaling based on the traffic control request signaling and traffic control phase information of a target intersection, where the target intersection is an intersection through which the first vehicle is to pass; and sending, by the traffic flow control device, the traffic command signaling to the in-vehicle device of the first vehicle. The traffic flow control method, the traffic flow control device, the in-vehicle device, and the computer-readable storage medium in the internet of vehicles can help a vehicle in the internet of vehicles travel safely and efficiently at an intersection.

A method for adjusting control parameters of a traffic management system in a presence of one or more incidents on a network includes junctions in a tree format, and at a lowest level of a tree comprise a group of associated junctions using coordinated timing, wherein each junction is associated with a weight as a function of its height within its tree, and wherein a junction with a highest weight in each tree comprises a lead junction for receiving timing information for its group of associated junctions.

The present invention relates to a detection area setting method for detecting passing vehicles, and a traffic signal control method using the same and, more particularly, to a detection area setting method for detecting passing vehicles, and a traffic signal control method using the same, the detection area setting method being capable of enabling smooth traffic operation at a crossroad, for example preventing a spillback phenomenon, minimizing green time (green light display time) during which there are no passing vehicles, and extending the green time, if needed, by setting one or a plurality of detection areas at a crossroad so as to detect the traffic volume in respective moving directions of vehicles at a signalized intersection, determining the traffic state in the moving directions of the vehicles according to vehicle information in respective detection areas, and automatically controlling the crossroad signals accordingly.

Synchronized work zone traffic management systems and methods are disclosed herein. An example method includes synchronizing, by a first vehicle, communication with a second vehicle over a wireless link, and displaying alternatingly, by the first vehicle, one of two messages on a first external display according to an alternating schema. A first message indicates to drivers to drive slowly and a second message indicates to the drivers to stop. The first message being displayed on the first external display when the second vehicle is displaying the second message on a second external display. The first vehicle displaying the second message on the first external display when the second vehicle is displaying the first message on the second external display. The alternating displaying of the first and second messages being used to control flow of traffic on a one-way street.