The present invention is a method for determining the amount of pollutant emissions (Q) from at least one vehicle over a road network section, wherein a pollutant emission model (MFE) is constructed by machine learning (APP) using macroscopic data (MAC) of a learning road network and traffic data (TRA). This model (MFE) is then applied to a road network section.

A vehicle running control method includes: when a junction section lane is present adjacent to a traveling lane of a host vehicle, collecting, by a processor, vehicle information of at least one vehicle traveling in the junction section lane; determining, by the processor, the possibility of cut-in of junction section lane vehicles based on the collected vehicle information and whether the traveling lane is congested; and controlling, by the processor, at least one of the traveling path or the traveling velocity of the host vehicle based on the result of determination in order to display an intention to yield.

Systems, methods, and computer-readable media are described for performing real-time vehicular incident risk prediction using real-time vehicle-to-everything (V2X) data. A vehicular incident risk prediction machine learning model is trained using historical V2X data such as historical incident data and historical vehicle operator driving pattern behavior data as well as third-party data such as environmental condition data and infrastructure condition data. The trained machine learning model is then used to predict the risk of an incident for a vehicle on a roadway segment based on real-time V2X data relating to the roadway segment and/or vehicle operators on the roadway segment. A notification of a high risk of incident can then be sent to a V2X communication device of the vehicle to inform an operator of the vehicle.

A traffic data analysis method, an electronic device, a vehicle and a storage medium are provided, and relate to the technical field of artificial intelligence, in particular to the fields of large data processing, automatic driving and vehicle networking, and can be applied to AI navigation. The method includes: acquiring a plurality of initial traffic data; determining a category of each of the plurality of initial traffic data; receiving a search instruction from an AI analysis model, wherein the search instruction includes target category information; determining target traffic data corresponding to the target category information from the respective initial traffic data according to categories of the respective initial traffic data; and sending the target traffic data to the AI analysis model so that the AI analysis model performs an AI analysis according to the target traffic data.

A system to mitigate side-to-side movement of a vehicle induced by passing traffic includes a controller configured to receive vehicle speed information, a traffic sensing system in communication with the controller, a damping system in communication with the controller, and at least one controllable damper in communication with the damping system. The controller determines if the vehicle speed is less than a predetermined minimum vehicle speed threshold, determines if the vehicle is in the proximity of nearby traffic, determines if the nearby traffic is traveling at a speed above a predetermined traffic speed threshold, and commands increased damping at the at least one controllable damper.

The invention relates to a method for displaying lane information in a vehicle. In the method, the present position of the vehicle on a route is captured. A number of existing traffic lanes at the present position in the direction of travel of the vehicle on the route is determined. Furthermore, the lane course of the existing traffic lanes that lies ahead of the vehicle in the direction of travel of the vehicle is determined, wherein a first traffic lane is determined from the lane course, which is a target traffic lane for the vehicle. In a first display region, a first display is generated, which shows a schematic illustration of the existing traffic lanes at the present position of the vehicle, which schematic illustration comprises schematic traffic lanes, wherein the number of the schematic traffic lanes corresponds to the number of the existing traffic lanes, such that an existing traffic lane is assigned a schematic traffic lane in each case. Moreover, for at least one first schematic traffic lane, a graphic object is generated which indicates whether the traffic lane assigned to the first schematic traffic lane is the target traffic lane, A first change position on the route is determined, as of which a second and no longer the first traffic lane is the target traffic lane for the vehicle and a second display is generated in a second display region, which shows a schematic illustration of the existing traffic lanes at the first change position on the route.

In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersections in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as bounding box coordinates for intersections, intersection coverage maps corresponding to the bounding boxes, intersection attributes, distances to intersections, and/or distance coverage maps associated with the intersections. The outputs may be decoded and/or post-processed to determine final locations of, distances to, and/or attributes of the detected intersections.

A method for determining road loads includes preparing a road map (15) that contains information about the local configuration of a plurality of roads. For each of a plurality of vehicles (1) a vehicle location is determined and at least one vehicle location signal (So) that characterizes the location of the vehicle concerned is generated. Using the vehicle location signal (So) and the road map, the vehicles (1) are assigned to the roads. For each vehicle (1) a vehicle load mass is determined and at least one vehicle load mass signal (Sm) that characterizes the vehicle load mass is generated. At least one road loading signal (Sb) that characterizes a road load is generated for each road using the vehicle load mass signals (Sm) of the vehicles (1) assigned to it.

The disclosure provides a method for determining an intersection missing traffic restriction information and an electronic device. The method includes: obtaining trajectory information corresponding to the intersection; determining a traffic anomaly occurring at the intersection based on the trajectory information; obtaining lane line information of a road section connected by the intersection; and determining the intersection missing traffic restriction information based on the lane line information.

A monitoring system and method may receive detection times at which passage of one or more vehicle systems by a detection device disposed along a route is detected. Locations of the one or more vehicle systems at different times may be received from one or more location sensors disposed onboard the one or more vehicle systems. The locations of the one or more vehicle systems when the one or more vehicle systems were detected by the detection device may be determined by comparing the detection times with the different times associated with the locations. A condition or state of the detection device can be determined by comparing the locations that are determined by the controller with a device location of the detection device.