Abstract:
The present invention relates to a method and apparatus for providing a driver-customized service. The present invention is configured to collect bio-signal information of a driver from a first sensor, collect current health condition information of the driver based on data stored in a health information DB, collect environmental condition information of an interior of a vehicle from a second sensor, collect environmental condition information of an exterior of the vehicle from a third sensor, generate a driving index based on the pieces of information collected from the first to third sensors and the health information DB, and perform a vehicle control operation corresponding to the driving index. Accordingly, the present invention not only enables the accident risk of drivers to be decreased, but also allows the drivers to drive their vehicles, which were considered to be only transportation means, in a safer and more comfortable environment.
Abstract:
Disclosed herein are a dynamic scheduling method for guaranteeing Quality of Service QoS depending on network transmission traffic and a system using the same. The dynamic scheduling method includes assigning communication channels to respective nodes based on Identifications (IDs) of parent nodes corresponding to the respective nodes, setting priorities for assignment of time slots to the respective nodes in each quarter based on data traffic volumes corresponding to the respective nodes, and assigning time slots to the respective nodes in each quarter depending on the set priorities for assignment of the time slots.
Abstract:
A method of predicting a possibility of an accident is provided. The method includes abstracting surrounding situation data and movement data of an ego-vehicle input from a sensor to generate abstracted driving situation data by using an abstraction module executed by a processor, calculating a digitized score of a possibility of an accident of the ego-vehicle by using a calculation module executed by the processor, based on the abstracted driving situation data, and generating action data of the ego-vehicle for decreasing the possibility of the accident by using an action generating module executed by the processor, based on the score.
Abstract:
The present invention relates to a method and apparatus for providing a driver-customized service. The present invention is configured to collect bio-signal information of a driver from a first sensor, collect current health condition information of the driver based on data stored in a health information DB, collect environmental condition information of an interior of a vehicle from a second sensor, collect environmental condition information of an exterior of the vehicle from a third sensor, generate a driving index based on the pieces of information collected from the first to third sensors and the health information DB, and perform a vehicle control operation corresponding to the driving index. Accordingly, the present invention not only enables the accident risk of drivers to be decreased, but also allows the drivers to drive their vehicles, which were considered to be only transportation means, in a safer and more comfortable environment.
Abstract:
A training data service system for operation scope-oriented autonomous driving shuttle includes a communication module configured to collect an autonomous driving data set from an autonomous driving vehicle to construct training data and distribute the training data to an autonomous driving shuttle, a memory configured to store a program for construction of the training data, and a processor configured to generate query data including road shape information, road attribute information, traffic environment information, and collection sensor information by executing the program stored in the memory, detect data satisfying a condition corresponding to the query data from a previously collected autonomous driving data set, construct training data for the autonomous driving shuttle, based on the detected data, and distribute the constructed training data to a corresponding autonomous driving shuttle.
Abstract:
Disclosed herein is an apparatus for continuous profiling for a multicore embedded system, the apparatus including a profiling data reception unit for receiving one or more pieces of profiling source data, in which events for each core in a multicore embedded system are written, from the multicore embedded system; a profiling data analysis unit for analyzing the profiling source data, determining a time at which each of events included in the profiling source data occurred and a core corresponding to the event, and determining whether each of the events is a past event depending on the time at which the event occurred; and a profiling file management unit for distinguishing each of the events depending on the determination of whether the event is a past event and storing the events in profiling files.
Abstract:
There is provided a network join method including transmitting a long beacon message including transmission timing information of a first short beacon message to a child node network device, transmitting a first short beacon message including information regarding an interval in which it is possible to transmit a message, to the child node network device according to a transmission timing of the first short beacon message, receiving a slot allocation request message from the child node network device according to the interval in which it is possible to transmit a message, and checking whether the child node network device joins a network, and transmitting a slot allocation confirmation message to the child node network device.
Abstract:
The present invention relates to an apparatus and method for monitoring abnormal state of a vehicle. In the method, CAN data collected from an ECU mounted on the vehicle is transformed into coordinates. The coordinates are applied to a distribution map in a specific space, a number of clusters is calculated based on results of the application, and an initial center point corresponding to the number of clusters is selected. Clustering is performed based on the initial center point, and then clusters are generated. At least one piece of data is extracted from each of the clusters, and a state feature of a corresponding cluster is decided on using a difference between maximum and minimum values of attributes constituting the at least one piece of data. A current state of the vehicle is monitored based on current CAN data of the vehicle and state features of the clusters.