Denso Corporation

So-ken Corporation

Naoki Nitanda

Shinya Taguchi

Kazuma Ishigaki

Naoki Kawasaki

Kentarou Shiota

Kenta Hoki

Taiki Kawano

Akihiro Watanabe

Shunya Kumano

Takayuki Miyahara

Kentaro Shiota

A map system includes a vehicle apparatus that is mounted to a vehicle. The map system includes an imaging apparatus, a server, and an integrating unit. The imaging apparatus captures an image of a surrounding area of the vehicle. The server generates a map using data that corresponds to feature points extracted from the image captured by the imaging apparatus that is transmitted from the vehicle apparatus. The integrating unit weights the pieces of data that are transmitted from a plurality of vehicle apparatuses based on bias in the pieces of data and generates a map by integrating at least a portion of the plurality of pieces of data based on the weighting.

Map system, map generating program, storage medium, on-vehicle apparatus, and server

The travelling route recognition unit recognizes, based on an image captured by an imaging apparatus capturing an area in the vicinity of the vehicle, a travelling route where the vehicle travels. The classifying unit classifies the travelling route into at least one of a predetermined plurality of road models. The edge point extracting unit extracts edge points necessary for expressing the road model classified by the classifying unit among edge points indicating a boundary of the travelling route recognized by the travelling route recognition unit. The parameter generation unit correlates road model information indicating the road model classified by the classifying unit with the edge points extracted by the edge point extracting unit and generates a travelling route parameter indicating the travelling route recognized by the travelling route recognition unit. The transmission unit transmits the travelling route parameter generated by the parameter generation unit to the server.

Vehicle apparatus, vehicle program and recoding media thereof

In an apparatus for estimating a behavior of a vehicle carrying the apparatus based on images of surroundings of the vehicle captured by an imaging device, an information acquirer acquires beforehand specific location information that is information representing a specific location in which a situation around the vehicle is such that the estimation of the own vehicle behavior based on the images is unstable. In the apparatus, a behavior estimator estimates the own vehicle behavior based on the images captured by the imaging device and the specific location information.

Apparatus and method for estimating own vehicle behavior

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

In an apparatus for recognizing a travel lane of a vehicle, a deviation determiner is configured to, if two or more shape change points are extracted by a shape change point extractor and the second derivative value of curvature of an extracted boundary line at at least one of the two or more shape change points is inverted in sign, determine whether or not the extracted boundary line and an estimated boundary line estimated from travel lane parameters estimated by a travel lane parameter estimator are deviating from each other beyond a predetermined allowable range. A driving aid is configured to, if it is determined that the extracted boundary line and the estimated boundary line are deviating from each other beyond the predetermined allowable range, perform control upon deviation to prevent occurrence of undesirable situations that may be caused by deviation between the extracted boundary line and the estimated boundary line.

Apparatus and method for recognizing travel 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

A roadside detection system installed in a vehicle to detect a roadside of a road on which the vehicle travels includes detection results acquiring means, first edge line detecting means and second edge line detecting means. The detection results acquiring means emits light waves or electromagnetic waves to a target detection region in which an object to be measured is detected and acquires an objective distance and a reflection intensity, for each of separate regions obtained by separating the target detection region into a plurality of divisions. The first edge line detecting means detects a first edge line that is a candidate of a roadside based on each objective distance. The second edge line detecting means detects a second edge line that is a candidate of a roadside based on each reflection intensity. A driver assistance system and a roadside detecting method are also provided.

Roadside detection system, driver assistance system and roadside detecting method

In a restoration apparatus, an estimating unit divides a captured original image into a plurality of local pixel blocks, and estimates an luminance level of airlight in each of the plurality of local pixel blocks. A calculating unit directly calculates, from a particle-affected luminance model, a luminance level of each pixel of each of the plurality of local pixel blocks in the original image to thereby generate, based on the luminance level of each pixel of each of the plurality of local pixel blocks, a restored image of the original image. The particle-affected luminance model expresses an intrinsic luminance of a target observed by the image pickup device as a function between the luminance level of airlight and an extinction coefficient. The extinction coefficient represents the concentration of particles in the atmosphere.

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Kariya, Japan

Naoki Nitanda