A method is provided. The method includes: obtaining first environmental information and second environmental information, where the first environmental information and the second environmental information are acquired by different sensors; determining, based on the first environmental information, information about a first lane of a first traffic object in the first environmental information, and determining; and determining whether the first traffic object and the second traffic object have an association relationship.

A target dangerous travel scene existing on a target road section is acquired. Reference information corresponding to the target road section is acquired. The reference information is determined according to a detection record corresponding to each of N dangerous travel scenes detected on the target road section within a target time period. N is an integer greater than 1. The detection record corresponds to each dangerous travel scene being used for reflecting time at which the corresponding dangerous travel scene is detected. An associated dangerous travel scene correlated to the target dangerous travel scene from the N dangerous travel scenes is determined according to the reference information. A prompt operation according to the target dangerous travel scene and the associated dangerous travel scene is performed.

The present disclosure relates to a method for predicting a behavior of a target vehicle in a maneuvering space, in which a plurality of maneuvers which can be executed in the future are provided for the target vehicle, in the maneuvering space, the target vehicle located in the maneuvering space being observed by a plurality of observation vehicles, for each maneuver of the target vehicle that can be executed in the future, a single probability distribution for the relevant maneuver that can be executed in the future being determined, on the basis of an observation performed by a relevant observation vehicle, for each maneuver of the target vehicle that can be executed in the future, a total probability distribution for the relevant maneuver of the target vehicle that can be executed in the future being established from a plurality of single probability distributions.

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.

A behavior prediction system predicts human behaviors based on environment-aware information such as camera movement data and geospatial data. The system receives sensor data of a vehicle reflecting a state of the vehicle at a given time and a given location. The system determines a field of concern in images of a video stream and determines one or more portions of images of the video stream that correspond to the field of concern. The system may apply different levels of processing powers to objects in the images based on whether an object is in the field of concern. The system then generates features of objects and identify VRUs from the objects of the video stream. For the identified VRUs, the system inputs a representation of the VRUs and the features into a machine learning model, and outputs from the machine learning model a behavioral risk assessment of the VRUs.

An advice target location at which a user had a predetermined emotion, for example, is determined based on location information, user biological information, and user transportation means information, which have been acquired by a terminal device (20) being used by the user. Advice information containing information indicating an advice presentation region set by a server device (50) is generated based on the advice target location. This advice information is supplied from the server device (50) to the terminal device (20), so that the terminal device (20) presents advice. With this, advice as to locations pedestrians find dangerous can be presented to drivers, and advice as to locations drivers find dangerous can be presented to pedestrians. Accordingly, accidents and the like can be prevented.

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.

A parking control system for an autonomous vehicle is provided. The parking control system includes a parking control device configured to monitor a location and movement of an autonomous vehicle which enters a parking lot, based on a 3D electronic map, calculate a driving trajectory to a parking space selected by a driver of the autonomous vehicle based on sensor data collected from various sensor in the parking lot and vehicle information received from the autonomous vehicle, and provide information about the calculated driving trajectory to the autonomous vehicle. The autonomous vehicle travels to the parking space based on the driving trajectory received from the parking control device and parks in the parking space.

Example aspects of the present disclosure describe a scene generator for simulating scenes in an environment. For example, snapshots of simulated traffic scenes can be generated by sampling a joint probability distribution trained on real-world traffic scenes. In some implementations, samples of the joint probability distribution can be obtained by sampling a plurality of factorized probability distributions for a plurality of objects for sequential insertion into the scene.

A method, apparatus and computer program product are provided to determine lane status confidence indicators of lane status predictions such as closures and/or shifting. Lane statuses and corresponding confidence indicators are determined based on probe data, such as probe data collected from vehicle and/or mobile devices traveling along a road segment. Probe data may be partitioned into clusters and compared to partitioned subsets of the probe data. Cluster stability for the segment and corresponding lane status confidence indicators can be determined based on the comparison. Accordingly, determinations of whether to transmit predicted lane statuses to another system, service, and/or user device may be made.