Abstract: A drive assistance apparatus predicts demand for vehicles in each area including a driving route, based on a demand prediction model for predicting the demand in the area. The drive assistance apparatus also predicts the frequency of occurrence of an obstacle in each of the regions obtained by dividing the area, based on a frequency model for predicting the frequency of occurrence of the obstacle in the region, and generates a driving route of the vehicle, based on the predicted demand in the area and the predicted frequency of occurrence of the obstacle.

Abstract: A center device is used in a probe center that collects information from a plurality of vehicles, and functions as an anomaly detector detecting anomaly of travel environment caused on a road. The center device obtains drive data from a vehicle, and determines a probability of anomaly resolution regarding anomaly in a travel environment caused on the road, based on the obtained drive data. The center device, upon determining that anomaly may possibly be resolvable, obtains image data used for a situation determination of an abnormal area, and also obtains a determination result of anomaly resolution based on the image data.

Abstract: In an accident pattern determination apparatus including a storage storing attributes assigned to respective ones of a plurality of predefined traffic situations, an acquirer acquires, for each of vehicle-related accident cases, an accident pattern that is a combination of traffic situations in the accident case, from the plurality of predefined traffic situations. A determiner determines, for each accident pattern acquired by the acquirer, whether the accident pattern is an accident pattern of high accident risk or an accident pattern of low accident risk for specific vehicles, based on the accident patterns acquired for the respective accident cases and the attributes assigned to respective ones of the plurality of predefined traffic situations.

Abstract: A computer calculates, in accordance with a maximum mean discrepancy, a similarity level between a first feature distribution correlating to a first distribution information item stored in a distribution database and a second feature distribution correlating to a second distribution information item stored in the distribution database. The second distribution information item is different from the first distribution information item. The maximum mean discrepancy is a distance measure indicative of the similarity level between the first and second feature distributions. The computer determines whether the calculated similarity level is equal to or higher than a predetermined threshold, and integrates the first feature distribution and the second feature distribution into a common feature distribution upon determining that the calculated similarity level is equal to or higher than the predetermined threshold.

Abstract: In a driving data analyzer, a data collector collects, from at least one vehicle, driving data sequences while each of the driving data sequences is correlated with identification data. Each driving data sequence includes sequential driving data items, and each driving data item represents at least one of a driver's operation of at least one vehicle and a behavior of the at least one vehicle based on the at least one of a driver's operation. The identification data represents a type of at least one external factor that contributes to variations in the driving data items. A feature extractor applies a data compression network model to the driving data sequences to thereby extract, from the driving data sequences, at least one latent feature independently from the type of the at least one external factor.

Abstract: A center device is used in a probe center that collects information from a plurality of vehicles, and functions as an anomaly detector detecting anomaly of travel environment caused on a road. The center device obtains drive data from a vehicle, and determines a probability of anomaly resolution regarding anomaly in a travel environment caused on the road, based on the obtained drive data. The center device, upon determining that anomaly may possibly be resolvable, obtains image data used for a situation determination of an abnormal area, and also obtains a determination result of anomaly resolution based on the image data.

Abstract: In a driving-state data storage apparatus, a collector collects, from each of vehicles on a target travelling road, a value of data indicative of a driving state of the corresponding vehicle to correspondingly obtain driving-state data values for the target road. A data allocator divides, based on similarity among the driving-state data values, the target traveling road into a plurality of traveling segments, and extracts, from the driving-state data values, data values for each of the divided travelling segments. The data values extracted for each of the travelling segments are similar to each other. The data allocator allocates a distribution of the extracted data values for each of the divided travelling segments to the corresponding one of the divided travelling segments as a feature distribution. A storage unit stores the feature distribution allocated for each of the travelling segments.

Abstract: An anomaly estimation apparatus includes a collection section that collects vehicle data, a feature amount calculation section that calculates a feature amount from the vehicle data and stores the feature amount and a place corresponding thereto, an anomaly determination section that determines whether an anomaly occurrence point is present based on the feature amount, an accumulation section that, if the anomaly occurrence point is present, uses the vehicle data at the anomaly occurrence point and an anomaly periphery point to generate estimation data, an information generation section that uses the estimation data to generate causality information representing causality between an anomaly caused at the anomaly occurrence point and an anomaly caused at the anomaly periphery point, and an estimation section that, if the anomaly occurrence point is present, uses the causality information to estimate transition of the anomaly from the anomaly occurrence point to the anomaly periphery point.

Abstract: In a driving assist system, a driving evaluator compares a driving feature data item sampled at each predetermined sampling point and obtained from a target vehicle with historical driving data items for the corresponding sampling point. The driving evaluator obtains, based on a result of the comparison, an evaluation value of the driving feature data item for the target vehicle at each predetermined sampling point. An unusual driving determiner obtains a cumulative sum of selected values in the evaluation values of the driving feature data items for the target vehicle. The unusual driving determiner determines whether the cumulative sum is larger than a predetermined threshold, and determines that driving of a driver of the target vehicle is unusual upon determining that the cumulative sum is larger than the predetermined threshold.

Abstract: In an anomaly detection apparatus mounted in a vehicle, a model storage stores a driving model which is set corresponding to a travel location of the vehicle traveling on a roadway and represents a normal driving state as the vehicle is traveling at the travel location. A data acquirer acquires driving performance data representing the driving state of the vehicle. A degree-of-anomaly calculator calculates a degree of anomaly in the driving state based on the driving model stored in the model storage and the driving performance data acquired by the data acquirer. A vehicle-mounted transmitter, if the degree of anomaly calculated by the degree-of-anomaly calculator exceeds an anomaly determination value for determining the presence of an anomaly in the driving state, transmits at least the degree of anomaly and the travel location corresponding to the degree of anomaly to a monitoring center located external to the vehicle and detects an anomaly in the road condition based on the degree of anomaly.

Abstract: A computer calculates, in accordance with a maximum mean discrepancy, a similarity level between a first feature distribution correlating to a first distribution information item stored in a distribution database and a second feature distribution correlating to a second distribution information item stored in the distribution database. The second distribution information item is different from the first distribution information item. The maximum mean discrepancy is a distance measure indicative of the similarity level between the first and second feature distributions. The computer determines whether the calculated similarity level is equal to or higher than a predetermined threshold, and integrates the first feature distribution and the second feature distribution into a common feature distribution upon determining that the calculated similarity level is equal to or higher than the predetermined threshold.

Abstract: In an apparatus for detecting an anomaly of an evaluation target condition of a road, a storage stores a reference data set correlating to a predetermined attribute and being comprised of reference data samples. An anomaly level calculator obtains, from target vehicles, an evaluation data set correlating to the predetermined attribute and being comprised of evaluation data samples. The evaluation data samples are collected from the respective target vehicles as target driving data items at the predetermined attribute under the evaluation target condition of the road. The target driving data item for each of the target vehicles represents at least one driving operation of the corresponding one of the target vehicles. The anomaly level calculator compares the reference data set with the evaluation data set to thereby calculate an anomaly level of the evaluation target condition of the road.

Abstract: A risk assessment system is provided and includes a behavior description generator, driving behavior comparator, and risk assessment modules and a transceiver. The behavior description generator module receives a set of driving features and topics corresponding to a driving behavior of a first driver in a location and under a set of driving conditions. The driving behavior comparator module: compares the set of driving features and the topics to other sets of features and topics, which correspond to driving behaviors of other drivers for the location and same or similar driving conditions as the set of driving conditions; and generates an anomaly score for the first driver based on results of the comparison. The risk assessment module calculates a risk assessment score for the driver based on the anomaly score, where the risk assessment score is indicative of a risk level of the first driver relative to the other drivers.

Abstract: In a driving data analyzer, a data collector collects, from at least one vehicle, driving data sequences while each of the driving data sequences is correlated with identification data. Each driving data sequence includes sequential driving data items, and each driving data item represents at least one of a driver's operation of at least one vehicle and a behavior of the at least one vehicle based on the at least one of a driver's operation. The identification data represents a type of at least one external factor that contributes to variations in the driving data items. A feature extractor applies a data compression network model to the driving data sequences to thereby extract, from the driving data sequences, at least one latent feature independently from the type of the at least one external factor.

Abstract: A risk assessment system is provided and includes a behavior description generator, driving behavior comparator, and risk assessment modules and a transceiver. The behavior description generator module receives a set of driving features and topics corresponding to a driving behavior of a first driver in a location and under a set of driving conditions. The driving behavior comparator module: compares the set of driving features and the topics to other sets of features and topics, which correspond to driving behaviors of other drivers for the location and same or similar driving conditions as the set of driving conditions; and generates an anomaly score for the first driver based on results of the comparison. The risk assessment module calculates a risk assessment score for the driver based on the anomaly score, where the risk assessment score is indicative of a risk level of the first driver relative to the other drivers.

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: In an anomaly detection apparatus mounted in a vehicle, a model storage stores a driving model which is set corresponding to a travel location of the vehicle traveling on a roadway and represents a normal driving state as the vehicle is traveling at the travel location. A data acquirer acquires driving performance data representing the driving state of the vehicle. A degree-of-anomaly calculator calculates a degree of anomaly in the driving state based on the driving model stored in the model storage and the driving performance data acquired by the data acquirer. A vehicle-mounted transmitter, if the degree of anomaly calculated by the degree-of-anomaly calculator exceeds an anomaly determination value for determining the presence of an anomaly in the driving state, transmits at least the degree of anomaly and the travel location corresponding to the degree of anomaly to a monitoring center located external to the vehicle and detects an anomaly in the road condition based on the degree of anomaly.

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: An anomaly estimation apparatus includes a collection section that collects vehicle data, a feature amount calculation section that calculates a feature amount from the vehicle data and stores the feature amount and a place corresponding thereto, an anomaly determination section that determines whether an anomaly occurrence point is present based on the feature amount, an accumulation section that, if the anomaly occurrence point is present, uses the vehicle data at the anomaly occurrence point and an anomaly periphery point to generate estimation data, an information generation section that uses the estimation data to generate causality information representing causality between an anomaly caused at the anomaly occurrence point and an anomaly caused at the anomaly periphery point, and an estimation section that, if the anomaly occurrence point is present, uses the causality information to estimate transition of the anomaly from the anomaly occurrence point to the anomaly periphery point.

Abstract: In a driving assist system, a driving evaluator compares a driving feature data item sampled at each predetermined sampling point and obtained from a target vehicle with historical driving data items for the corresponding sampling point. The driving evaluator obtains, based on a result of the comparison, an evaluation value of the driving feature data item for the target vehicle at each predetermined sampling point. An unusual driving determiner obtains a cumulative sum of selected values in the evaluation values of the driving feature data items for the target vehicle. The unusual driving determiner determines whether the cumulative sum is larger than a predetermined threshold, and determines that driving of a driver of the target vehicle is unusual upon determining that the cumulative sum is larger than the predetermined threshold.