Denso Corporation

So-Ken Corporation

Shunya Kumano

Shunsuke Suzuki

Takeshi Shikimachi

Masaya Okada

Akihiro Watanabe

Mitsuhiro Tokimasa

Hiroki Ukai

Takumi Uematsu

Taiki Kawano

Syoya Ishida

Hiroaki Niino

Naoki Kawasaki

Tetsuya Takafuji

Keiji Matsuoka

Takaharu Oguri

Kazuhisa Ishimaru

Naoya Inoue

Yu Maeda

Kenta Hoki

Yuusuke Matsumoto

Naoki Nitanda

Hiroyuki Osawa

Ukyo Tanikawa

Satoru Yoshikawa

Ryota Yamamoto

An evacuation running assistance system includes a road shoulder evacuation possibility determiner to determine if an own vehicle can be evacuated to a road shoulder; an own vehicle situation determiner to determine a current situation of an own vehicle in accordance with a time limit and the road shoulder evacuation possibility, a controller to control an own vehicle in accordance with the situation of the own vehicle; and a road shoulder evacuation possibility road determiner to acquire evacuation space information from a past running history of the own vehicle. The own vehicle situation determiner determines that the own vehicle is in the situation to be controlled to perform the on-lane stopping when the road shoulder evacuation possibility road determiner does not determine within the provisional time that the evacuation of the own vehicle to the road shoulder is possible.

Road shoulder evacuation running assistance system

An autonomous driving apparatus, which is used in a subject vehicle capable of performing an autonomous driving, is configured to: determine a deviation value of each of one or more candidate routes, which indicates a possibility of the subject vehicle deviating from a traveling rule when the subject vehicle travels the corresponding candidate route, based on a comparison result between an inter-vehicle distance and a minimum control permission distance; select one candidate route having the deviation value within a control permission range as a target route; output an instruction for controlling the subject vehicle to travel along the selected target route; execute a traveling control of the subject vehicle according to the instruction; and update a determination rule of the deviation value in response to a change in a sensor that detects a behavior of at least one of the subject vehicle or the peripheral vehicle.

Autonomous driving apparatus and rule determination apparatus

An evacuation running assistance system includes a peripheral environment recognizer to recognize at least a space of a road shoulder and a free space as a non-traffic portion, a time limit setter to set a time limit on the own vehicle for continuing evacuation running, and an evacuation place setter to determine at least one of the road shoulder space and the free space recognized by the peripheral environment recognizer as an evacuation place where the own vehicle is evacuated. A situation determiner determines a situation of an own vehicle as being in one of situations in which evacuation running is to be continued, running of an own vehicle is to be stopped on a lane, and road shoulder evacuation is to be performed based on the time limit. A controller controls the own vehicle based on the situation of the own vehicle determined by the situation determiner.

Evacuation running assistance system

A vehicle position estimation device includes an external information acquisition unit for acquiring external information, a vehicle parameter acquisition unit for acquiring a vehicle parameter, a satellite positioning acquisition unit for acquiring a latitude and longitude of a self-position of the vehicle, a map data acquisition unit for acquiring map data, and a position estimation unit. The position estimation unit estimates the self-position, and includes a reliability calculation unit and a lane estimation unit. The reliability calculation unit calculates a reliability of each lane based on the external information and the map data when the vehicle is traveling on a road having multiple lanes. The reliability of each lane indicates a probability of the vehicle being traveling in the lane among the lanes. The lane estimation unit estimates a lane in which the vehicle is located by using the reliability calculated by the reliability calculation unit.

Vehicle position estimation device

The position of the distance measurement point is acquired from the distance measurement sensor, the lateral position of the road boundary is estimated from the most frequent value in the frequency distribution indicating the distribution status of the distance measurement point group. When multiple peaks having a frequency in the frequency distribution equal to or higher than a predetermined value are disposed on one of the left side and the right side of the vehicle, it is determined whether the estimation of the road boundary is in an unstable state. The vehicle position on the map is specified, and the traveling lane of the vehicle is estimated based on the information about the numerical number of traffic lanes and the positional relationship of the road boundary without using the estimation result of the lateral position of the road boundary in the unstable state.

Travel lane estimation device, travel lane estimation method, and computer-readable non-transitory storage medium

A vehicle position estimation device is configured to acquire a distance from a road edge to a subject vehicle using at least one of an imaging device or a distance measuring sensor, acquire position information of lane boundary lines detected by analyzing the images captured by the imaging device; acquire map information including a lane quantity of a traveling road of the subject vehicle from a map storage, calculate, as a roadside area width, a lateral direction distance between an outermost detection line, which is an outermost boundary line among the detected boundary lines, and the road edge, and specify a traveling lane of the subject vehicle based on (i) the distance from the road edge to the subject vehicle, (ii) the roadside area width, and (iii) the lane quantity included in the acquired map information.

Vehicle position estimation device and traveling position estimation method

By a traveling lane estimation apparatus, a traveling lane estimation method, a control program, a computer-readable non-temporary storage medium, a front vehicle is recognized based on a sensing result by a periphery monitoring sensor, a front vehicle traveling trajectory is estimated based on a front vehicle position, map data including lane number information is acquired; a subject vehicle position on a map is identified, an inappropriate traveling trajectory for estimating the subject vehicle traveling lane is determined; and the subject vehicle traveling lane is estimated.

Traveling lane estimation apparatus, traveling lane estimation method, and computer-readable non-temporary storage medium storing control program

A self-position estimation device equipped to a vehicle: captures an image of a periphery of the vehicle; detects a state quantity of the vehicle; acquires position information indicating a position of the vehicle from a satellite system; stores map data that defines a map in which a road is expressed by a link and a node; estimate a self-position of the vehicle on the map, as an estimation position, based on the captured image, the state quantity, the position information, and the map data, respectively; recognizes a road section in which a lane quantity increases or decreases based on the captured image; and sets a weighting of the estimation position of the vehicle estimated based on the map data relatively smaller in response to a recognition of the road section in which the lane quantity increases or decreases.

Self-position estimation device

In a road parameter estimation apparatus, a marker-based estimator extracts, based on markers extracted by a marker extractor, at least one lane line that demarcates a road into plural regions in a width direction of the road, and estimates, based on the extracted at least one lane line, a value of at least one feature parameter of the road as a maker-based estimation result. The at least one feature parameter of the road represents at least one feature of the road. A model-based estimator estimates, based on the at least one model-based demarcation line, a value of the at least one feature parameter of the road parameter as a model-based estimation result. A determiner compares the at least one lane line with the model-based demarcation line to accordingly determine whether to use the marker-based estimation result or the model-based estimation result.

Apparatus for estimating road parameter

In an apparatus for controlling travel of an own vehicle which is a vehicle carrying the apparatus, an information acquirer is configured to acquire information regarding a target around the own vehicle from a target detector. A controller is configured to, if determining, using the target information acquired by the information acquirer, that if travel of the own vehicle is continued in accordance with a collision avoidance trajectory determined to avoid a collision with an object located on a roadway ahead of the own vehicle, the own vehicle is likely to collide with the object or another object, change a setting of a driving state of the own vehicle so as to avoid or reduce a likelihood of the collision.

Apparatus and system for controlling travel of vehicle

A reliability calculation apparatus calculates a reliability of a recognized object that is recognized as being present on a route on which a vehicle travels. The reliability of the recognized object is used for driving control of the vehicle. In the reliability calculation apparatus, a provisional reliability calculating unit determines a feature quantity of each of a plurality of recognized objects, selects a plurality of pairs of recognized objects from the plurality of recognized objects, determines a difference in feature quantity of each pair of recognized objects, and calculates a reliability that decreases as the difference in feature quantity increases as a provisional reliability of each pair of recognized objects. An integrated reliability calculating unit calculates an integrated reliability of each recognized object from the provisional reliabilities of the plurality of pairs of recognized objects.

Reliability calculation apparatus

A roadside object recognition apparatus recognizes a roadside object that is present on a travel route on which an own vehicle travels. The roadside object is used for driving control of the vehicle. In the roadside object recognition apparatus, a reflection point acquiring unit acquires, using a radar that emits electromagnetic waves, a reflection-point group of reflection points of the electromagnetic waves reflected by an object that is present on the travel route. An image acquiring unit acquires an image of the travel route using a camera. A reflection point correcting unit corrects the reflection-point group by removing an erroneous reflection point that is determined to be highly likely not to be a reflection point of a roadside object from the reflection-point group through image processing of the image. A shape recognizing unit recognizes a shape of the roadside object using the corrected reflection-point group.

Roadside object recognition apparatus

A driving support apparatus is provided to improve accuracy of the driving support such as a notification to the driver and the vehicle control. In the apparatus, a common parameter indicating a location of a lane boundary is calculated for the vehicle control, based on an actual boundary line where a location of the lane boundary line existing at both sides of the vehicle is actually measured. A common error is defined as an error between the location of an estimated boundary indicated by the common parameter and a location of the actual boundary line. In the case where the common error is larger than or equal to a common threshold in which an amount of the common error is set in advance, the apparatus determines that the travelling road where the vehicle travels is branched and stops the vehicle control using the common parameter.

Driving support apparatus

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

An apparatus for identifying a line marking on a road surface. In the apparatus, an extractor extracts a paint candidate that is a candidate for road surface paint used to identify a line marking in an image captured by a camera mounted on a vehicle to capture an image of an area including a road surface ahead of the vehicle. A determiner determines whether or not the paint candidate has at least one predefined flare feature. A line marking identifier identifies a line marking using the paint candidate which meets an identification condition used to identify a line marking. The line marking identifier sets the identification condition to be more stringent for a flare paint candidate that is the paint candidate determined by the determiner to have the at least one predefined flare feature than for the paint candidate determined by the determiner to have no predefined flare feature.

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Nisshin, Japan

Shunya Kumano