A method and apparatus for identifying a traffic accident, a device and a computer storage medium are disclosed, which relates to the technical fields of intelligent traffic and big data. An implementation includes: acquiring road features, environmental features and road traffic stream features; inputting the road features, the environmental features and the road traffic stream features into a pre-trained traffic-accident identifying model to obtain an accident-information identifying result of a road which at least includes an accident identifying result. In the present application, the accident road may be automatically identified according to the road features, the environmental features and the road traffic stream features. Compared with a traditional manual reporting way, timeliness is stronger, and a coverage rate is higher.

Specifically programmed, integrated motor vehicle dangerous driving warning and control system and methods comprising at least one specialized communication computer machine including electronic artificial intelligence expert system decision making capability further comprising one or more motor vehicle electronic sensors for monitoring the motor vehicle and for monitoring activities of the driver and/or passengers including activities related to the use of cellular telephones and/or other wireless communication devices and further comprising electronic communications transceiver assemblies for communications with external sensor networks for monitoring dangerous driving situations, weather conditions, roadway conditions, pedestrian congestion and motor vehicle traffic congestion conditions to derive warning and/or control signals for warning the driver of dangerous driving situations and/or for controlling the motor vehicle driver use of a cellular telephone and/or other wireless communication devices.

Specifically programmed, integrated motor vehicle dangerous driving warning and control system and methods comprising at least one specialized communication computer machine including electronic artificial intelligence expert system decision making capability further comprising one or more motor vehicle electronic sensors for monitoring the motor vehicle and for monitoring activities of the driver and/or passengers including activities related to the use of cellular telephones and/or other wireless communication devices and further comprising electronic communications transceiver assemblies for communications with external sensor networks for monitoring dangerous driving situations, weather conditions, roadway conditions, pedestrian congestion and motor vehicle traffic congestion conditions to derive warning and/or control signals for warning the driver of dangerous driving situations and/or for controlling the motor vehicle driver use of a cellular telephone and/or other wireless communication devices.

A transportation vehicle, a method, a computer program and an apparatus for combining object information relating to one or more objects in an environment of a transportation vehicle from different data sources. The method includes capturing a first item of environmental information relating to the environment by at least one sensor arranged on the transportation vehicle, receiving at least one second item of environmental information relating to the environment from a transmitter separated from the transportation vehicle, determining at least one first item of object information from the first environmental information, determining at least one second item of object information from the second environmental information, and assigning the first meta information and the second meta information to one another based on a comparison of a tolerance range with a discrepancy between the first object information and the second object information.

A transportation vehicle, a method, a computer program and an apparatus for combining object information relating to one or more objects in an environment of a transportation vehicle from different data sources. The method includes capturing a first item of environmental information relating to the environment by at least one sensor arranged on the transportation vehicle, receiving at least one second item of environmental information relating to the environment from a transmitter separated from the transportation vehicle, determining at least one first item of object information from the first environmental information, determining at least one second item of object information from the second environmental information, and assigning the first meta information and the second meta information to one another based on a comparison of a tolerance range with a discrepancy between the first object information and the second object information.

Systems and methods for a model for estimation of reliability of a hazard sensor observation at a vehicle are disclosed. An example apparatus may include a hazard observation interface configured to receive one or more hazard observations from the hazard sensor of the vehicle, wherein each of the one or more hazard observations is associated with a hazard location and a hazard timestamp, a ground truth module configured to determine ground truth data from one or more weather records from a historical weather database based on the hazard location and the hazard timestamp, and a model configured to perform a comparison of the one or more hazard observations to the one or more weather records, the model trained for estimation of reliability of the hazard sensor observations based on the comparison of the one or more hazard observations to the one or more weather records.

A vehicle control apparatus acquires a position of an other vehicle that is present ahead of the own vehicle in its traveling direction, and uses the acquired position of the other vehicle to calculate a movement locus, which is a past path of the other vehicle. The vehicle control device calculates a lateral movement amount of the movement locus in a predetermined range in the traveling direction of the own vehicle, the lateral movement amount being an amount of change in position in a lateral direction which is a direction intersecting the traveling direction, and calculates an average value of the lateral movement amounts. The vehicle control apparatus excludes, from the movement loci, a movement locus having a lateral movement amount whose difference from the average value is larger than a predetermined value, and calculates the predicted path based on remaining movement loci.

A vehicle control apparatus acquires a position of an other vehicle that is present ahead of the own vehicle in its traveling direction, and uses the acquired position of the other vehicle to calculate a movement locus, which is a past path of the other vehicle. The vehicle control device calculates a lateral movement amount of the movement locus in a predetermined range in the traveling direction of the own vehicle, the lateral movement amount being an amount of change in position in a lateral direction which is a direction intersecting the traveling direction, and calculates an average value of the lateral movement amounts. The vehicle control apparatus excludes, from the movement loci, a movement locus having a lateral movement amount whose difference from the average value is larger than a predetermined value, and calculates the predicted path based on remaining movement loci.

Embodiments of the disclosed systems and methods provide for systems and methods for collecting and managing vehicle and infrastructure sensor measurement data and building predictive models based on such data. In certain embodiments, operation and/or control of a vehicle may be based on the predictive model. In certain embodiments, the predictive model may be generated and/or otherwise trained based on actual vehicle measurement data and actual infrastructure measurement data that has been correlated by associated time and/or location. In further embodiments, model validation techniques may be used to determine a predictive quality of the model.

Embodiments of the disclosed systems and methods provide for systems and methods for collecting and managing vehicle and infrastructure sensor measurement data and building predictive models based on such data. In certain embodiments, operation and/or control of a vehicle may be based on the predictive model. In certain embodiments, the predictive model may be generated and/or otherwise trained based on actual vehicle measurement data and actual infrastructure measurement data that has been correlated by associated time and/or location. In further embodiments, model validation techniques may be used to determine a predictive quality of the model.