An electronic device automatically selects a surveillance configuration for a road traffic area by sensor(s) among a set of configurations of at least one sensor. The set includes at least two separate configurations. The road traffic area is segmented into M different regions with M≥1. At least one coverage area is being associated with each sensor. The device includes a determining module configured to determine intersections between each region and each coverage area, respectively, and an optimization computing module configured to determine, from the intersections, at least one optimal surveillance configuration corresponding to a sensor configuration or a combination of sensor configurations providing at least one maximal coverage.

An information processing system is configured to include a target vehicle data acquisition unit configured to acquire target vehicle data including a travel state of a target vehicle, driving operation information of the target vehicle, and position information of the target vehicle on a map, a behavior occurrence position recognition unit configured to recognize a behavior occurrence position that is a position where the target vehicle performs an unstable behavior, based on the target vehicle data, and a cause determination unit configured to determine whether or not the unstable behavior at the behavior occurrence position is caused by the driver, based on at least one of the travel state of the target vehicle and the driving operation information of the target vehicle.

An information processing system is configured to include a target vehicle data acquisition unit configured to acquire target vehicle data including a travel state of a target vehicle, driving operation information of the target vehicle, and position information of the target vehicle on a map, a behavior occurrence position recognition unit configured to recognize a behavior occurrence position that is a position where the target vehicle performs an unstable behavior, based on the target vehicle data, and a cause determination unit configured to determine whether or not the unstable behavior at the behavior occurrence position is caused by the driver, based on at least one of the travel state of the target vehicle and the driving operation information of the target vehicle.

Provided are a driving early warning method and apparatus, an electronic device, and a computer storage medium. The method comprises: acquiring real-time position information of a vehicle (101); predicting future position information according to the real-time position information (102); according to a first mapping relationship between a geographical location and the dangerous driving historical data and which is stored in a database, determining, in the database, first dangerous driving historical data corresponding to the future position information (103); generating first driving early warning information according to the determined first dangerous driving historical data (104); and sending the first driving early warning information to the vehicle (105).

Provided are a driving early warning method and apparatus, an electronic device, and a computer storage medium. The method comprises: acquiring real-time position information of a vehicle (101); predicting future position information according to the real-time position information (102); according to a first mapping relationship between a geographical location and the dangerous driving historical data and which is stored in a database, determining, in the database, first dangerous driving historical data corresponding to the future position information (103); generating first driving early warning information according to the determined first dangerous driving historical data (104); and sending the first driving early warning information to the vehicle (105).

A virtual image is superimposed on a foreground scenery of an occupant. It is determined whether a road condition, for associating a display position of the virtual image with a position of an object in the foreground scenery with respect to a road on which the vehicle travels, is satisfied. The virtual image is generated as a superposing virtual image that presents information by associating the display position with the position of the object when the road condition is satisfied. At least a part of the virtual image is generated as a non-superimposing virtual image that presents the information without associating the display position with the position of the object when the road condition is not satisfied.

The subject matter described herein includes a roadway stud control system including a local stud control system positioned along a first section of a roadway, a plurality of roadway studs, each roadway stud disposed on a surface of the first section of the roadway and communicably coupled to the local stud control system, wherein the local stud control system is configured to communicate a control signal to control at least one aspect of the plurality of roadway studs.

The subject matter described herein includes a roadway stud control system including a local stud control system positioned along a first section of a roadway, a plurality of roadway studs, each roadway stud disposed on a surface of the first section of the roadway and communicably coupled to the local stud control system, wherein the local stud control system is configured to communicate a control signal to control at least one aspect of the plurality of roadway studs.

A method for estimating a future fall of a cargo, the method may include receiving by a computerized system, sensed information related to driving sessions of multiple vehicles; applying a machine learning process on the sensed information to detect actual or estimated cargo falling events and generate one or more future falling cargo predictors for multiple types of cargo; estimating, from the sensed information, an impact of cargo falling events related to at least some of the types of cargo; and responding to the estimating, wherein the responding comprises at least one out of (a) storing the one or more future falling cargo predictors for the multiple types of cargo, (b) transmitting the one or more future falling cargo predictors for the multiple types of cargo; (c) storing the estimated impact of cargo falling events related to the at least some of the types of cargo, and (d) transmitting the impact of cargo falling events related to the at least some of the types of cargo.

A method for estimating a future fall of a cargo, the method may include receiving by a computerized system, sensed information related to driving sessions of multiple vehicles; applying a machine learning process on the sensed information to detect actual or estimated cargo falling events and generate one or more future falling cargo predictors for multiple types of cargo; estimating, from the sensed information, an impact of cargo falling events related to at least some of the types of cargo; and responding to the estimating, wherein the responding comprises at least one out of (a) storing the one or more future falling cargo predictors for the multiple types of cargo, (b) transmitting the one or more future falling cargo predictors for the multiple types of cargo; (c) storing the estimated impact of cargo falling events related to the at least some of the types of cargo, and (d) transmitting the impact of cargo falling events related to the at least some of the types of cargo.