Toyota Jidosha Kabushiki Kaisha

Aisin Seiki Kabushiki Kaisha

Denso Corporation

Isahiko Tanaka

Junya Kasugai

Koji Suzuki

Takuhiro Omi

Yoshihiro Adachi

Yoshiki Ninomiya

Masaru Ogawa

Takashi Naito

Atsushi Kawakubo

Nobuyuki Shiraki

Takeshi Hayashi

Makoto Ohkado

Michinori Ando

Shinichi Kojima

Shin-ichi Kojima

Fumio Sugaya

Akira Kadoya

Satoru Nakanishi

Hiroyuki Ishizaka

Takumi Yoda

A radar system includes an arithmetic processing unit, which calculates the second derivative of the intensity of a reflected wave and determines whether a target is an upper structure or a vehicle, based on the second derivative. Alternatively, the arithmetic processing unit decomposes the waveform of the intensity of the reflected wave into frequency components and determines whether the target is an upper structure or a vehicle, based on the intensity of a predetermined frequency component.

Radar system and detection method

A radar system includes an arithmetic processing unit, which sends a warning command to a warning device when a local minimum point of the intensity of the reflected wave from a target within a predetermined detection range is detected. By determining whether it is necessary to send a warning command based on whether there is a local minimum point of the intensity within the detection range, it is made possible to determine whether it is necessary to send a warning command more quickly as compared to the conventional system.

An object determining device includes imaging means for obtaining an image of the object, likelihood value calculating means for calculating a first likelihood value for the object shown in the image by use of the image obtained by the imaging means and a machine learning system and for calculating a second likelihood value for the object shown in the image by use of the image obtained by the imaging means and another machine learning system, the first likelihood value indicating a level of likelihood that the object is wearing the covering and the second likelihood value indicating a level of likelihood that the object is not wearing the covering, and determining means for determining whether or not the object, shown in the image obtained by the imaging means, is wearing a covering, on the basis of a ratio between the first likelihood value and the second likelihood value.

Object determining device and program thereof

Local maxima values and local minima values are derived from eyelid openness time series data in a segment in which a continuous closed eye period of extracted blinks is a specific time duration (for example 1 second) or longer. When plural local minima values are present in the segment of continuous closed eye period of 1 second or longer, blinks are extracted passing over and back through each variable closed eye threshold value of a variable closed eye threshold that is slid in a direction from the derived local maxima value towards the local minima value in set steps to a low value, and a inter-blink interval derived. Determination is made that a blink burst has occurred when the derived inter-blink interval is 1 second or less, and say greater than 0.2 seconds, thereby detecting a blink burst. Blink bursts can be detected with good precision, and the state of drowsiness can be assessed with good precision.

Drowsiness assessment device and program

An image processing apparatus is configured so that a combination of the processes that are performed is set for each of unit cycles (according to the value of a counter) in accordance with priorities individually set for the processes, and image processing is performed in a fundamental cycle that is constituted of six unit cycles. A face direction determination process, a line-of-sight direction determination process, a blink detection process, a dozing determination process, and a consciousness deterioration level determination process are selectively performed depending on the value of the counter, which varies from 1 to 6. According to the image processing apparatus, it is possible to more efficiently perform image processing that involves a plurality of processes.

Image processing apparatus and image processing method

A central location of a face detecting device comprises an image input means for inputting a captured face image, a horizontal edge detecting means for detecting a horizontal edge having a predetermined vertical length based on the face image inputted by the image input means; and a central location of a face detecting means for calculating an average value of a horizontal location data of the horizontal edge detected by the horizontal edge detecting means, and determining the average value as a central location of the face.

Central location of a face detecting device, method and program

A facial parts position detection device which includes an image inputting device for inputting a captured face image, an ends position detecting device for detecting right and left ends position of the face on the basis of the image, a facial parts region setting device for setting facial parts region indicating a region where facial parts exists on the basis of the right and left ends of the face, a horizontal edge detecting device for detecting a horizontal edge on the basis of the image inputted by the image inputting device, a horizontal edge histogram creating device for creating a horizontal edge histogram by projecting the horizontal edge in a horizontal direction, a proposed position setting device for setting a plurality of peaks of the horizontal edge histogram as proposed positions respectively, and an optimum position detecting device for detecting an optimum position of the facial parts region.

Facial parts position detection device, method for detecting facial parts position, and program for detecting facial parts position

In a digital map data processing device and a digital map data processing method which are useful to generate an accurate road map and particularly can be used for navigation and control of traveling vehicles according to the generated road map, a map image processing station acquires image data of the ground surface through a satellite communication station , and an image processing section converts the image data into orthoimage data which is viewed substantially from directly above and free from distortion. A data extraction section extracts a skeleton map (road network data) formed of roads and related stereoscopic information according to the orthoimage data, a data conversion section calculates base road data from the skeleton map, and a map generation section overlays necessary attribute data to generate highly accurate digital orthomap data free from a distortion. The digital orthomap data is sent to a vehicle through a transmission section and used for navigation control and running control.

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Susono, Japan

Isahiko Tanaka