Denso Corporation

Denson Corporation

Kenji Muto

Kazuoki Matsugatani

Kazuhito Takenaka

Masataka Mori

Masumi Egawa

Hideaki Misawa

Jun Kosai

Naoki Kawasaki

Shunsuke Suzuki

Kazuyoshi Isaji

Tsuneo Nakata

Hiroshi Mizuno

Yusuke Tanaka

Seiichi Furuta

Kojiro Tateishi

Mikio Shimizu

Tsubasa Okuya

Masato Matsumoto

Yuki Shinohara

Kentaro Hitomi

Utsushi Sakai

Shun Shimizu

Daisuke Hirano

Suguru Yamazaki

Yoshinori Takeuchi

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.

Accident pattern determination apparatus and method

In an abnormality detection method for detecting an abnormality in vehicle behaviors based on an abnormality degree indicating a degree of difference between first driving information representing a vehicle behavior undergoing abnormality detection and second driving information obtained when no abnormality is found in a travel environment, an abnormality threshold is estimated using an abnormality degree distribution generated from a set of preaccumulated abnormality degrees. The abnormality threshold is an abnormality degree allowing a probability of erroneous determination as to presence or absence of an abnormality to be a predefined probability. Further, in the abnormality detection method, it is detected whether an abnormality is found in the vehicle behavior by comparing the abnormality degree for the first driving information and the estimated abnormality threshold.

Abnormality detection method, abnormality detection apparatus, and abnormality detection system

A server device includes: a data collection unit that collects vehicle behavior data; a scene extraction unit that extracts, from the collected vehicle behavior data, driving scenes corresponding to the behavior of the vehicle and a scene feature amount of each of the driving scenes; an abnormality detection unit that calculates a degree of abnormality which represents an extent of deviation of the scene feature amount of each of the extracted driving scenes relative to a driving model, and detects a driving scene including a location of an abnormality using the calculated degree of abnormality; a section determination unit that extracts driving scenes satisfying a predetermined condition from among the detected driving scene and a plurality of driving scenes continuous to the driving scene, and determines, as an abnormal behavior section, a time range defined by the total continuation time of the extracted driving scenes; an image request unit that requests, from the vehicle, one or more captured images according to the determined abnormal behavior section; and a display control unit that performs control to display the images acquired from the vehicle according to the request.

Road environment monitoring device, road environment monitoring system, and road environment monitoring program

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.

Driving-state data storage apparatus

In a location estimation device, a change detection unit detects a point of change in the width of a roadside structure as a width change point. A width correction unit calculates a width deviation between a first relative location of the roadside structure on a map data segment and a second relative location of the roadside structure obtained based on shape information of the roadside structure in the width direction of a vehicle. The width correction unit corrects, as a function of the width deviation and a positional accuracy of the vehicle in the travelling direction, an estimated location of the vehicle in the width direction when it is determined that there is the point of change in the width of the roadside structure in the travelling direction of the vehicle.

Location estimation device

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.

Road condition monitoring system

Images are efficiently collected or delivered so that uncomfortable feeling given to users who view picked images picked up at vehicles and delivered by an image server is lessened. An image server is so constructed that the following operation is performed: the picked image of the surroundings of a vehicle, picked up with a camera mounted on the vehicle, and the image pickup information of the picked image, including information about the image pickup point, are stored on a storing medium; and picked images stored on the storing medium are delivered to users who view received picked images. One is selected from among picked images inputted from a vehicle with respect to each of areas to which the image pickup points of those picked images belong based on the image pickup information and a predetermined selection condition. The selected picked image is stored on the storing medium.

Image server, image deliver based on image information and condition, and image display terminal

A wireless position detection apparatus specifies, on a road, a target point corresponding to a current position correspondence point of a moving object and calculates a positioning coordinate of the target point as current position information of the moving object based on (i) position information of the current position correspondence point of the moving object on the coverage map, (ii) the position information of the auxiliary points positioned immediately prior to and immediately posterior to the current position correspondence point on the coverage, and (iii) the position data of the map objects corresponding to the auxiliary points positioned immediately prior to and immediately posterior to the current position correspondence point on the coverage map.

Wireless position detection apparatus and storage medium

A vehicle identification apparatus mounted in a vehicle provided with a detection unit configured to detect a speed of a first other vehicle and a communication unit configured to receive information indicative of a speed of a second other vehicle from the second other vehicle. In the apparatus, a calculation unit calculates an indicator value indicative of a likelihood that the first and second other vehicles are the same, where the indicator value is defined as a function of the speed of the first other vehicle detected by the detection unit and the speed of the second other vehicle indicated by the information received by the communication unit. A determination unit determines whether or not the first and second other vehicles are the same on the basis of the indicator value calculated by the calculation unit.

Vehicle identification apparatus and method

Fragment data is generated with respect to a certain data aggregate that is one of a plurality of data aggregates using a coded matrix. The fragment data is capable of restoring the certain data aggregate based on the coded matrix when the fragment data is collected to a required count. The generated fragment data is provided with an identification code associated with the certain data aggregate restored by the fragment data. The fragment data having the identification code is transmitted to a transmission channel. Further, the same transmission channel can be used to transmit the multiple pieces of the fragment data corresponding to the multiple data aggregates. When a receiving party is previously notified of the identification code, it can receive the fragment data by distinguishing which fragment data should restore which data aggregate.

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Kariya, Japan

Kenji Muto