A method may include obtaining input information relating to an environment in which an autonomous vehicle (AV) operates. The input information may describe a state of the AV, an operation of the AV within the environment, a property of the environment, or an object included in the environment. The method may include setting up a traffic rule profile for the AV that specifies societal traffic practices corresponding to a location of the environment. The method may include identifying a first traffic rule relevant to the AV based on the obtained input information and determining a first decision corresponding to the traffic rule profile and the first traffic rule. The method may include sending an instruction to a control system of the AV, the instruction describing a given operation of the AV responsive to the traffic rule profile and the first traffic rule according to the first decision.

An on-board unit (OBU) of a vehicle receives one or more data messages indicative of intersection geometry for an upcoming intersection along a roadway being traversed by the vehicle and traffic control status of a traffic control of the intersection. An outbound direction for the vehicle through the intersection is identified. A first traffic message is sent to preempt the traffic control to allow the vehicle to perform a maneuver to traverse the intersection in the outbound direction. The maneuver is indicated as complete and a second traffic message is sent to discontinue the preempt of the traffic control.

An information presentation device includes: a communication unit that receives the degree of congestion at each position on each of a plurality of routes from each of human flow sensors installed at predetermined intervals on each of the plurality of routes to a specific point; a calculation unit that calculates the required time for each time series of the route on the basis of the degree of congestion at each position in each of the routes; a prediction unit that inputs the time-series required time calculated for each of the routes to a predetermined device for predicting The required time of each of the routes learned in advance, and predicts the timing when the required time approaches the threshold for each of the routes as an output of the predetermined device; and an information management unit that identifies a route that requires inducement from the timing predicted for each of the routes, selects an incentive for prompting a change in the identified route and an inducement time for the incentive, and instructs the information presentation terminal installed at least at a starting point of the identified route to present information corresponding to the selected incentive and inducement time.

A vehicle identification method and system are provided. The method includes: identifying a first vehicle to be a second vehicle based on a first group of data and a second group of data, the first group of data representing information of the first vehicle obtained through at least one sensor, and the second group of data representing information received from the second vehicle through an inter-vehicle communication network. By the method, an identity of the first vehicle in the inter-vehicle communication network can be known, the first group of data may be used to check the accuracy of the second group of data, and the second vehicle may be still tracked even if it is out of sight of the at least one sensor.

A road traffic jam early warning method includes: performing characteristic classification according to acquired multi-source traffic data, and constructing a corresponding characteristic membership function, to obtain a first fuzzy weight; applying an expert evaluation method to the multi-source data to construct an artificial membership function, and calculating a second fuzzy weight; performing fuzzy weighted average on the characteristic membership function according to a fused fuzzy weight obtained by fusing the first fuzzy weight and the second fuzzy weight, and performing defuzzification on obtained weighted average membership functions having different characteristic quantities, to obtain fused multi-source traffic data; constructing a road traffic congestion model, and calculating an optimal road traffic congestion index; and acquiring current multi-source traffic data, predicting a current congestion index, and providing, by comparing the current congestion index with the optimal road traffic congestion index, a warning about whether a current road is congested.

A system and a method for cooperative maneuvering and cooperative risk warning of vehicles. The system includes monocular surveillance cameras, local computing devices, and a master server. Each local computing device receives video frames from the camera, detects and tracks vehicles from the video frames, and converts the video frames to bird-view. Each detected vehicle is represented by a detection vector having first dimensions representing two dimensional (2D) parameters of the vehicle and second dimensions representing three dimensional (3D) parameters of the vehicle. Tracking of the vehicles is performed by minimizing loss calculated based on the first dimensions and the second dimensions of the detection vectors. The master server receives bird-views from different computing devices and combines the bird-views into a global bird-view, and performs cooperative maneuvering and cooperative risk warning of the vehicles using the global bird-view.

Provided is a system for properly avoiding a waiting-for-entrance congestion caused by vehicles that stop on a road to enter a facility along the road. A map server 3 is configured to: acquire probe information for identifying a traveling lane in which each vehicle is traveling; acquire, based on the probe information, a congestion occurrence condition when traffic congestion repeatedly occurs in a specific lane at a specific location with a predetermined regularity; and generate, based on the congestion occurrence condition, specific lane congestion information about a congestion status in the specific lane. A control device of a vehicle acquires the specific lane congestion information delivered from the map server, and controls, based on the specific lane congestion information, traveling of the vehicle so as to avoid traffic congestion that is predicted to occur in the specific lane.

Systems and methods for generating driving recommendations are disclosed herein. One embodiment divides automatically a roadway into a plurality of lane-level cells; generates a graph network that represents the plurality of lane-level cells; gathers information pertaining to one or more detected road agents; projects onto the graph network the gathered information pertaining to the one or more detected road agents to update a current status of the plurality of lane-level cells; processes the graph network based on the updated current status of the plurality of lane-level cells to predict a future status of the plurality of lane-level cells, the predicted future status including at least occupancy, by a detected road agent, of the respective lane-level cells in the plurality of lane-level cells; and generates a driving recommendation based, at least in part, on the predicted future status of the plurality of lane-level cells.

A parking recognition server of a personal mobility device and a method thereof are provided. The parking recognition server includes a processor configured to determine whether a personal mobility device is parked in a parking area, and a storage configured to store data and an algorithm run by the processor and a usage history for each user who uses the personal mobility device. The processor may be further configured to predict a destination and a movement path of a user for the usage history for each user, and determine a device candidate group having a high probability of being parked in the predicted destination based on a degree of overlap between the predicted movement path and an actual movement path of the user.

The present invention is a system and method for improving the accuracy with which human behavioral insights regarding visitor volume to places can be predicted. The process aligns at least two different data sets and normalizes the information by removing statistical bias. In a particular implementation motor vehicle data is collected from a known subset of road-borne vehicles and compared to data collected from unknown vehicles. The data is then increased or discounted to remove biases present in the known subset. The system analyzes the normalized data and returns a report including predictions to a human user.