Abstract:
A vehicle application enabling system is provided and includes a memory and initialization, latency evaluation, and application enable modules. The initialization module: receives a maximum network latency; sets a percentage of occurrences that the maximum network latency is not satisfied, a maximum false positive rate, and a maximum deviation value; and calculates a weighting factor based on the percentage of occurrences, maximum false positive rate and maximum deviation value. The latency evaluation module implements a latency evaluation algorithm, which includes: comparing one or more latency estimates to the maximum network latency to provide one or more samples; updating confusion matrix statistics based on the one or more samples; updating a probability threshold based on the maximum false positive rate; updating weighted observations based on the weighting factor; and determining a predicted decision based on the probability threshold. The application enable module executes the vehicle application based on the probability threshold.
Abstract:
In a system, a behavior description generator extracts a feature portion from at least one of segmented behavior-data sequences corresponding to at least one target driving situation included in sequential driving situations, and generates behavior-description information indicative of a description of a driver's behavior associated with the at least one target driving situation. An event description generator sequentially detects traffic events in at least one environmental data sequence indicative of time-series driving environments of a vehicle. The event description generator generates event-description information including a description indicative of each of the detected traffic events. A context description generator extracts, from the sequentially detected traffic events, one target traffic event that is detected immediately adjacent to the feature portion. The context description generator generates context information indicative of a context between behavior-description information and the event-description information about the target traffic event.
Abstract:
In an abnormal driving behavior detection system for a vehicle, an obtainer repeatedly obtains an observed value indicative of at least one of a running condition of the vehicle and a driver's driving operation of the vehicle. A mode-probability calculator calculates, each time an observed value is obtained at a given obtaining timing as a target obtained value, a mode probability for each of driving modes as a function of one or more previous observed values. A deviation calculator obtains a predicted observed value for each driving mode using a driver's normal behavior model defined therefor, and calculates a deviation of the target observed value from the predicted observed value for each driving mode. An abnormality determiner determines whether there is at least one driver's abnormal behavior based on the mode probability for each driving mode and the deviation calculated for each driving mode.
Abstract:
A drive assist apparatus includes a data collection part that collects driving behavior data representing driving maneuvers and vehicle behaviors caused by the driving maneuvers for each drivers, a classification part that classifies the driving behavior data into a plurality of clusters each showing a tendency of driving behavior of the drivers by clustering the driving behavior data, a storage part that stores cluster information representing a driving behavior characteristic of each of the clusters, a subject data acquisition part that acquires, as subject data, the driving behavior data for a subject driver to be assisted, an estimation part that estimates, as a corresponding cluster, one of the clusters to which the subject driver is assumed to belong by comparing the subject data with the cluster information stored in the storage part, and an assist providing part that provides a drive assist depending on the estimated corresponding cluster.
Abstract:
A drive data collection system includes an in-vehicle system and an information center. The in-vehicle system includes a data collector that repeatedly collects measurement values indicative of various indexes regarding a state of a subject vehicle, a model memory that memorizes model information regarding discretization rules that are shared by each of participating vehicles in the system, and a data discretizer that discretizes drive data, which includes time-series measurement values collected by the data collector, into multiple data parts according to the model information. The information center includes a data accumulator that accumulates the discretized drive data in a server. The in-vehicle system sends the discretized drive data to the information center through a communicator. Therefore, the drive data collection system efficiently collects the drive data in a versatilely-utilizable manner for the analysis of general driving practices/behaviors.
Abstract:
A central latency system includes a communication device configured to exchange data with an external device. The data includes current latency data, current contextual data associated with the latency data, or a combination thereof. The system further includes a controller configured to aggregate latency data in response to acquiring the current latency data, generate a latency characterization information based on the aggregated latency data, and transmit the latency characterization information to the external device.
Abstract:
A central latency system includes a communication device configured to exchange data with an external device. The data includes current latency data, current contextual data associated with the latency data, or a combination thereof. The system further includes a controller configured to aggregate latency data in response to acquiring the current latency data, generate a latency characterization information based on the aggregated latency data, and transmit the latency characterization information to the external device.
Abstract:
In an abnormal driving behavior detection system for a vehicle, an obtainer repeatedly obtains an observed value indicative of at least one of a running condition of the vehicle and a driver's driving operation of the vehicle. A mode-probability calculator calculates, each time an observed value is obtained at a given obtaining timing as a target obtained value, a mode probability for each of driving modes as a function of one or more previous observed values. A deviation calculator obtains a predicted observed value for each driving mode using a driver's normal behavior model defined therefor, and calculates a deviation of the target observed value from the predicted observed value for each driving mode. An abnormality determiner determines whether there is at least one driver's abnormal behavior based on the mode probability for each driving mode and the deviation calculated for each driving mode.
Abstract:
A system for setting parameters within a first vehicle for a first user of the first vehicle is provided and includes a memory, transceiver, and a processing module. The memory stores normalized parameters, which are specific to the first user. Each of the normalized parameters is a parameter of the vehicle. The transceiver receives the normalized parameters from a mobile device of the first user, a server in a cloud-based network, or a portable memory device. The processing module: determines whether the first user is authorized for parameter translation services; if the first user is authorized for parameter translation services, translates the normalized parameters to resultant parameters; generates parameter recommendations based on the resultant parameters; presents the parameter recommendations to the first user; and adjusts current parameters of the first vehicle based on the resultant parameters and a received response from the first user with regards to the parameter recommendations.
Abstract:
A mobile network device is provided and includes a memory and a control module. The memory is configured to store a driver assistance application. The control module is configured to execute the driver assistance application and when executing the driver assistance application the control module is configured to: receive mobile sensor signals from a plurality of sensors in the mobile network device; normalize the mobile sensor signals to generate normalized signals; translate the normalized signals to a plurality of time sequences representing vehicle behavior; concatenate the plurality of time sequences to generate pseudo-vehicle behavior signals; and generate driver assistance signals based on the pseudo-vehicle behavior signals to assist a driver of a vehicle.