A recording reproduction apparatus according to the present disclosure includes: a captured data acquisition unit that acquires first captured data captured by a first camera that captures an image of an outside of a vehicle; an event detection unit that detects an event regarding the vehicle; a recording controller that stores the first captured data in a period including at least a timing of occurrence of the event as event recording data; a reproduction controller that reproduces the event recording data; a display controller that causes a display unit to display the event recording data reproduced by the reproduction controller; and an attachment/detachment detection unit that detects an attachment/detachment state of the display unit to/from the vehicle, in which the reproduction controller starts, when it has been detected by the attachment/detachment detection unit that the display unit has been detached from the vehicle, reproduction of event recording data.

Each of multiple vehicle data collection devices are configured to collect data streams associated with operation of a vehicle. The data streams include time-stamped speed data. A transmitter is configured to transmit the data streams. An analytics server is configured to receive the data streams transmitted by the transmitter and to process the data. In connection with the processing, data streams with at least one common time stamp are identified. The time-stamped speed for one of the data streams at a first time interval is compared to the time-stamped speed for another of the identified plurality of data streams at a second time interval, where the second time interval comprises the first time interval plus an additional time increment. Based on the comparison, it is determined whether the two of the plurality of data streams are associated with a same trip or a different trip.

An apparatus according to one aspect of the present invention determines whether or not platoon vehicles can pass through an intersection, and includes: a calculation unit that calculates a first distance, a second distance, and a third distance described below; and a determination unit that determines whether or not the platoon vehicles can pass through the intersection, based on a result of comparison of the first distance with the second and third distances. First distance: a distance from a stop line of the intersection to a position of a leading vehicle at the present time. Second distance: a distance obtained by subtracting a platoon length from a distance of traveling for a remaining green interval at a vehicle speed at the present time. Third distance: a distance required for the leading vehicle to safely stop before the stop line of the intersection, with the vehicle speed at the present time.

State data of vehicles in a control area is collected, then time of starting control and selection of controlled vehicles are decided according to a position of a ramp merging vehicle, information is transmitted into a traffic control module by means of a data transmission module, and an artificial intelligence based ramp merging multi-objective control model ensures efficient, safe, and energy-saving operation of overall traffic of a road while completing ramp merging by means of a vehicle traveling track in the cooperative ramp control area. Compared with a traditional method, the present invention greatly promotes merging of the ramp vehicles, and different from other methods having the defect of transforming the problem of ramp merging into the problem of vehicle sequencing, the present invention greatly improves efficiency of ramp merging,

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.

A method for determining a false negative rate of mobile monitoring and requisite number of mobile monitoring vehicles. The method focuses on road network of an urban area, by installing air pollution detection equipment on the mobile monitoring vehicles, to monitor air quality of the urban area. The method includes establishing a curve model of monitoring times for the mobile monitoring vehicles, determining two parameters of expected indicator: covered range, number of scheduled detections; and finding out a requisite number (C.sub.0) of the mobile monitoring vehicles, corresponding to a curve model which meets the two parameters of expected indicator.

A method of transferring data between an autonomous vehicle and a vehicle traffic control infrastructure system. The method includes receiving, by a communication device of a vehicle, a data payload for a smart traffic control infrastructure node. The method includes, by a computing device of the vehicle: determining that the vehicle is or will be within a communication range of the smart traffic control infrastructure node, determining a length of time that the vehicle will be in the communication range of the smart traffic control infrastructure node, and assembling a communication package with at least a portion of the data payload that can be transferred in the determined length of time. The method includes, by a communication device of the vehicle when the vehicle has an ad hoc communication link with the smart traffic control infrastructure node, transmitting the assembled communication package to the smart node.

A method for extracting a get-on-and-off place where a vehicle may stop, which is performed by an operation server, includes, extracting vehicle road sections accessible by foot, filtering the extracted road sections by excluding a road section in which stopping of the vehicle is not permitted under traffic regulations, from the extracted road sections, selecting n quantity of virtual get-on-and-off places allowing getting on-and-off from the filtered roads, determining the walking time from each in all points of the service area to closest get-on-and-off place point, setting a longest time among walking times of all points of the service area, as a maximum walking time, and select a predetermined quantity of virtual get-on-and-off places from among the n quantity of virtual get-on-and-off places such that the selected maximum walking time is minimum, where the quantity k may be a natural number smaller than number n.

Autonomous driving includes identifying a traffic behavior of an object in an environment surrounding a vehicle based on an evaluation of information about the environment surrounding the vehicle while the vehicle is in the midst of manual operation, and operating vehicle systems in the vehicle to perform a driving maneuver according to a driving plan for performing the driving maneuver. The autonomous driving further includes receiving a traffic behavior model that describes a predominating traffic behavior of a like population of reference objects, and operating the vehicle systems to perform the driving maneuver according to the driving plan in response to identifying that the traffic behavior of the object does not match the predominating traffic behavior of the like population of reference objects. Under the driving plan, the traffic behavior of the object is addressed.

Providing user assistance in a vehicle includes identifying a traffic behavior of an object in an environment surrounding the vehicle based on an evaluation of information about the environment surrounding the vehicle while the vehicle is in the midst of manual operation, and issuing an alert to a user prompting the user to implement defensive manual operation. The user assistance further includes receiving a traffic behavior model that describes a predominating traffic behavior of a like population of reference objects, and issuing the alert to a user prompting the user to implement defensive manual operation in response to identifying that the traffic behavior of the object does not match the predominating traffic behavior of the like population of reference objects. Under the defensive manual operation, the traffic behavior of the object is addressed.