Denso Corporation

Denso International America, Inc.

The Ritsumeikan Trust

Hideaki Misawa

Kazuhito Takenaka

Yuki Shinohara

Masumi Egawa

Masataka Mori

Kentaro Hitomi

Utsushi Sakai

Kenji Muto

Takashi Bando

Haris Volos

Kenji Konishi

Daisuke Hirano

Tadahiro Taniguchi

An autonomous driving system includes a recognition unit configured to recognize an obstacle based on image data obtained by imaging a predetermined region including a road on which an autonomous vehicle travels, the imaging being performed by an imaging device installed at a specific location in the external environment of the autonomous vehicle, a feature quantity distribution creation unit configured to create a feature quantity distribution expressing a distribution of features related to obstacles recognized in the past, and an erroneous recognition judgement unit configured to compare the created feature quantity distribution with a feature quantity of an obstacle recognized as an evaluation subject, to thereby judge whether the obstacle recognized as the evaluation subject is erroneously recognized.

Autonomous driving system, autonomous driving method, and autonomous driving program

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.

Anomaly detector, anomaly detection program, anomaly detection method, anomaly detection system, and in-vehicle device

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 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.

Driving data analyzer

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

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.

Vehicle behavior and driver assistance modules for a mobile network device implementing pseudo-vehicle behavior signal generation based on mobile sensor signals

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.

Anomaly estimation apparatus and display apparatus

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.

Driving assist system

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.

Growing community of inventors

Kariya, Japan

Hideaki Misawa