Denso Corporation

Nippon Soken, Inc.

Toyota Jidosha Kabushiki Kaisha

Naoki Kawasaki

Shunsuke Suzuki

Syunya Kumano

Tetsuya Takafuji

Tomohiko Tsuruta

Yuu Maeda

Hiroshi Mizuno

Osamu Shimomura

Kazuhisa Ishimaru

Taku Sakima

Yuusuke Matsumoto

Taiki Kawano

Kazuma Hashimoto

Satoshi Osanai

Kenta Hoki

Kojiro Tateishi

Akihiro Watanabe

Masayoshi Takeda

Akitoshi Minemura

Yusuke Akamine

Takeshi Shikimachi

Yusuke Ueda

Tadashi Sakai

Hirotake Ishigami

Masaya Okada

Naoki Nitanda

Yusuke Kataoka

Yasuyuki Miyake

Masayuki Imanishi

Shotaro Fukuda

Youhei Masui

Masao Oooka

Shunya Kumano

Toyoharu Katsukura

Junichiro Funabashi

Yusuke Tanaka

Junichi Hasegawa

Kenji Muto

Tatsuru Kuwabara

Kiyokazu Takagi

Mikio Shimizu

Hiroki Nakano

Masaki Masuda

Koujirou Tateishi

An in-vehicle radar device includes a transmission section, a reception section, an analysis section, an extraction section, a speed calculation section, a distance calculation section, and a folding detection section. The folding detection section detects occurrence of erroneous calculation of a distance, when reflection intensity at a frequency peak obtained by the extraction section is smaller than a preset intensity threshold for a distance calculated by the distance calculation section and a frequency width in a frequency spectrum calculated by the analysis section is smaller than a preset width threshold.

In-vehicle radar device

A road parameter calculator is provided which is equipped with an edge-point extracting unit, a road parameter calculating unit, a gradient detecting unit, and a modeling unit. The image acquiring unit. The edge-point extracting unit extracts edge points from an image of a frontal view of a vehicle. The parameter calculating unit calculates a road parameter using the edge points through a Kalman filter. The gradient detecting unit detects a change in gradient of the road in front of the vehicle. The modeling unit is responsive to a change in gradient to make a model as extending more straight than when the change in gradient is not detected. This minimizes adverse effects of the change in gradient of the road on the calculation of the road parameter.

Road parameter calculator

In a boundary line recognition apparatus, based on luminance levels of an image captured by a camera, candidate edge points of a boundary line sectioning a travel road are extracted, and a candidate line of the boundary line is extracted. An apparent width of the candidate line on an image is calculated, from a width of the candidate line in a horizontal direction of the image and an angle of the candidate line relative to a vertical direction of the image. A probability of a candidate line being a boundary line is calculated to be higher, as a degree of the candidate line having characteristics as a boundary line is higher. The calculated probabilities are integrated in respect of a plurality of characteristics to recognize a boundary line. The characteristics include a ratio of the calculated apparent width to an image blur degree is larger than a predetermined value.

Boundary line recognition apparatus and branch road determination apparatus

A lane detecting apparatus is provided which obtains at least one type of parameters associated with lane lines on the road and selects one of the lane lines as a reference line using one of the parameters which is most useful in determining the configuration of a lane on a road. The lane detecting apparatus also calculates a lane boundary line using the reference lane line.

Lane detection apparatus

A course estimator has a first estimating means, a second estimating means and a determining means. The first estimating means obtains first information and estimating a first radius of a first forward traveling path on the basis of the obtained first information, the first forward traveling path being a part of a forward traveling path to which a vehicle is going to travel. The second estimating means obtains second information and estimating a second radius of a second forward traveling path on the basis of the obtained second information, the second forward traveling path being a part of the forward traveling path to which a vehicle is going to travel, the second forward traveling path being farther from the vehicle than the first traveling being. The determining means determines whether or not there is a changing point where road shapes change between the first forward traveling path and the second forward traveling path on the basis of comparison of the estimated first radius and the estimated second radius.

Course estimator and method of estimating a state of a course of a vehicle and a non-transitory computer-readable storage medium for the same

A position identifying apparatus includes a position detecting unit; an information acquiring unit; a feature extracting unit; an index value calculating unit; a feature selecting unit; a correlating unit; a weight setting unit; and a correcting unit. The position detecting unit performs a map matching to detect a vehicle position on a map. The information acquiring unit acquires road information from the map. The feature extracting unit extracts features of a ground object. The index value calculating unit calculates an index value representing a likelihood of a stationary object. The feature selecting unit selects feature candidates among the features having the index values larger than or equal to a predetermined likelihood of the stationary object. The correlating unit correlates the feature candidates with the road information. The weight setting unit sets weights of the feature candidates. The correcting unit corrects the position of the vehicle using the weights.

Apparatus for identifying position of own vehicle and method for identifying position of own vehicle

An estimating device includes: a movement amount computing section that, on the basis of a position of a predetermined region within an image picked-up at a first time, and a position of a region that corresponds to the predetermined region and is within an image picked-up in a same direction at a second time that is after the first time, computing a movement amount of the predetermined region in a vertical direction; a movement amount integrating section that computes an integrated value of the movement amounts, each time the movement amount is computed; a correcting section that corrects the integrated value of a current point in time on the basis of time series data of the integrated values of the movement amounts within a past predetermined time period; and an estimating section that estimates a pitch angle of the vehicle on the basis of a corrected integrated value.

Estimating device and estimating program

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

A road shape recognition apparatus is mounted in a vehicle. The road shape recognition apparatus acquires a road image, determines a road shape using the road image, recognizes the road shape based on the road image within a predetermined recognition range, and estimates the road shape outside of the recognition range based on the road shape within the recognition range. The road shape recognition apparatus estimates the road shape outside of the recognition range and within a first distance as a curved line of which a curvature change rate is constant, and the road shape farther than the first distance as a curved line of which a curvature is constant.

Road shape recognition apparatus

In a lane boundary line information acquiring device, a detection unit detects lane boundary lines. A driving environment acquiring unit acquires a driving environment. A probability information acquiring unit acquires probability information containing a probability of presence of a lane boundary line, etc. based on the detected lane boundary lines and the acquired driving environment. A position information acquiring unit acquires position information of the own vehicle. A memory unit associates the probability information with the position information of the own vehicle. Where the position information is acquired by the position information acquiring unit at a time when the probability information acquiring unit acquires the probability information, and stores the probability information associated with the position information into the memory unit. A readout unit reads out the probability information associated with the position information at a location in front of the own vehicle.

Lane boundary line information acquiring device

A road surface information acquisition apparatus a lane line recognizer configured to, based on a road surface image of a carpool lane forward of a vehicle acquired by a camera, recognize lane lines of the carpool lane. The apparatus further includes a determiner configured to, based on a lane partition line recognized by the lane line recognizer, determine a start point and an end point of the entrance and exit, and a lane-width calculator configured to, based on the lane lines recognized by the lane line recognizer, calculate a lane width of the carpool lane. The apparatus further includes a lane-width corrector configured to, based on determinations made by the determiner, correct the lane width calculated by the lane-width calculator. Based on a broken line as the lane partition line, the determiner determines the start point and the end point of the entrance and exit of the carpool lane.

Road surface information acquisition apparatus for entrance/exit lane

In a pitch angle estimation apparatus, an image acquirer sequentially acquires images in a forward or rearward direction of a vehicle. Each image is referred to as a frame. A movement amount calculator extracts a feature point from each of the images acquired by the image acquirer and calculates an amount of movement of the feature point in a vertical direction of the image. A pitch angle calculator calculates a pitch angle of the vehicle based on the amount of movement of the feature point. An output unit outputs the pitch angle calculated by the pitch angle calculator. A low frequency component calculator calculates a low frequency component of the pitch angle calculated by the pitch angle calculator. An output determiner determines whether to output the pitch angle based on the magnitude of the low frequency component calculated by the low frequency component calculator.

Pitch angle estimation apparatus

In an image processing apparatus, an image acquiring unit acquires a first image and a second image that form stereoscopic images. A first sub-image extracting unit extracts first sub-images from the first image. A second sub-image extracting unit extracts second sub-images from the second image. A matching unit matches each pair of the first and second sub-images to determine a degree of similarity therebetween. A similar sub-image setting unit sets the second sub-image having a highest degree of similarity to the first sub-image to be a similar sub-image to the first sub-image. A brightness comparing unit compares in brightness each pair of the first and second sub-images. The matching unit is configured to, if a result of comparison made by the brightness comparing unit between a pair of the first and second sub-images is out of a predetermined brightness range, exclude such a pair of the first and second sub-images.

Image processing apparatus

An in-vehicle system as a road curvature detection device calculates a curvature of a road in front of a vehicle based on an acquired front scene image. The in-vehicle system receives gradient information from the data map. The gradient information corresponds to a current road section on the road on which the vehicle drives. The in-vehicle system detects a gradient accuracy of the received gradient information. When the current road section has a gradient, i.e. the road is an uphill or downhill road, the in-vehicle system selects an appropriate special detection methods based on the gradient accuracy of the received gradient information, and calculates a road curvature by using the selected special detection method. Each of the special detection methods calculates a road curvature while effectively suppressing influence of a road gradient indicated by the received gradient information.

Road curvature detection device

A monitoring apparatus is provided. When a determination process of determining whether or not the part of an object located in a search range is an airborne substance, a rangefinder measures a distance to the object located in the search range for each of a plurality of unit areas forming the search range, a variation measure calculator calculates a variance of the distances (individual distances) measured for the respective unit areas, a variable threshold setter variably sets a variation measure threshold based on the individual distances, and a determiner determines that at least part of the object is an airborne substance if the calculated variance exceeds the variation measure threshold.

Monitoring apparatus and non-transitory computer-readable medium

Growing community of inventors

Kariya, Japan

Naoki Kawasaki