Nissan Motor Company Limited

Panasonic Corporation

Panasonic Intellectual Property Management Co., Ltd.

Renault S.a.s.

Takuya Nanri

Hirofumi Nishimura

Fang Fang

Kensuke Maruya

Hisashi Kurokawa

Chikao Tsuchiya

Yasuhito Sano

Shotaro Yamaguchi

Hirotoshi Ueda

Shinya Tanaka

Hiroyuki Takano

Junko Ueda

Takeshi Ishihara

Koji Arata

Shoichi Takei

Abdelaziz Khiat

Wenli Zhang

Shoutaro Yamaguchi

A behavior prediction method includes: determining a position of a host vehicle; determining a position of another vehicle in a second travel lane, the second travel lane being an opposite lane to a first travel lane in which the host vehicle travels; detecting an intersecting passage intersecting the second travel lane at a position ahead of the host vehicle; and determining whether or not the another vehicle is located within a predetermined range from an intersection position of the intersecting passage and the second travel lane to a point away from the intersection position by a predetermined distance in an opposite direction to a traveling direction of a vehicle in the second travel lane and the another vehicle is in either state of a stopped state and a decelerated state to predict that there is a probability that a mobile unit enters the first travel lane from the intersecting passage.

Behavior prediction method and behavior prediction device for mobile unit, and vehicle

An other-vehicle behavior prediction method includes calculating a travel path of the host vehicle, detecting a position of an intersection present on the travel path of the host vehicle and within a predetermined distance from the host vehicle, and detecting a position of another vehicle around the host vehicle before the intersection. A host-vehicle passing area through which the host vehicle passes when proceeding through the intersection and an other-vehicle passing area through which the first oncoming vehicle is predicted to pass when proceeding through the intersection are calculated, a space between the host-vehicle passing area and the another-vehicle passing area is calculated, and a distance between the first oncoming vehicle and the intersection is calculated. Based on the space and the distance, a probability that the first oncoming vehicle will proceed through the intersection ahead of the host vehicle is predicted.

Method for predicting behavior of other vehicle, device for predicting behavior of other vehicle, and driving assistance method

A behavior prediction apparatus specifies a first object that affects a behavior of a vehicle from objects present around the vehicle. The behavior prediction apparatus performs a prediction process of extracting a second object that affects a behavior of the first object among a plurality of objects present around the first object and predicting a behavior. The behavior prediction apparatus sets the extracted second object as a new first object, and performs a prediction process of extracting a new second object affecting the behavior of the new first object and predicting the behavior. The behavior prediction apparatus repeats the prediction process by a predetermined number of times. The behavior prediction apparatus predicts the behavior of the first object in the first prediction process based on the behavior of each of the second objects subjected to the prediction process.

Behavior prediction method, behavior prediction apparatus and vehicle control apparatus

A driving assistance method includes: detecting a first other vehicle entering an intersection on a first route where a host vehicle is traveling from a second route; predicting whether or not the first other vehicle will stop in the intersection, and predicting a stop position of the first other vehicle; calculating a minimum distance of a first gap between a vehicle body of the first other vehicle and a surrounding object around the first other vehicle or between the vehicle body of the first other vehicle and a road edge of a travel lane of the first other vehicle when the first other vehicle stops at the predicted stop position; and predicting according to the calculated minimum distance whether or not a second other vehicle, which is a following vehicle behind the first other vehicle, may slip through the first gap from behind the first other vehicle.

Driving assistance method and driving assistance device

A self-position estimation method includes: detecting a relative position between a target present in surroundings of a moving object and the moving object; storing a position where the relative position is moved by the moved amount of the moving object, as target position data; selecting the target position data on the basis of reliability of the relative position of the target position data with respect to the moving object; and comparing the selected target position data with map information including the position information on the target present on a road or around the road, thereby estimating a self-position which is a current position of the moving object.

Self-position estimation method and self-position estimation device

A vehicle behavior prediction device includes an objection detection device for detecting a position of an object, with respect to a host vehicle, located on the front side or the lateral side of the host vehicle, and a moving object traveling further than the object from the host vehicle, and an behavior prediction unit. The behavior prediction unit calculates, based on the position detected by the objection detection device, a blind spot region from the host vehicle caused by the object in which the objection detection device cannot detect. The behavior prediction unit presumes a detection-available period from a point when the moving object is detected to a point when the moving object enters the blind spot region in a case in which the moving object travels in a predetermined course after being detected by the objection detection device.

Vehicle behavior prediction method and vehicle behavior prediction device

A self-position estimation method includes: detecting a relative position of each target existing around a moving body relative to the moving body; estimating a movement amount of the moving body; correcting the relative position on a basis of the movement amount of the moving body and accumulating the corrected relative position as target position data; detecting a slope amount of a traveling road of the moving body; selecting, from among the accumulated target position data, the target position data of one or more targets in one or more sections having a slope amount less than a threshold value; and collating the selected target position data with map information indicating positions of the targets on a two-dimensional map to estimate a present position of the moving body.

A traveling assistance method acquires driving characteristics of another vehicle around a host vehicle, determines whether the other vehicle is in an autonomous driving mode depending on the driving characteristics of the other vehicle, and detects an action of the other vehicle in accordance with the determination result of whether the other vehicle is in the autonomous driving mode.

Traveling assistance method of traveling assistance device and traveling assistance device

A traveling assistance method detects another vehicle and objects around a host vehicle, sets a blind spot area from the other vehicle, specifies an object present in the blind spot area among the detected objects, and predicts an action that the other vehicle takes in accordance with the specified object.

Traveling assistance method and traveling assistance device

A driving assistance method detects a behavior of a moving object causing a blind spot area around a host vehicle, predicts a probability of action that the moving object takes when an obstacle is present in the blind spot area, according to a road structure around the host vehicle, and compares the behavior of the moving object with the probability of action that the moving object takes, so as to predict an action of the moving object.

A self-position estimation method includes: detecting a relative position of a target existing around a moving body relative to the moving body; estimating a movement amount of the moving body; correcting the relative position on a basis of the movement amount of the moving body and accumulating the corrected relative position as target position data; detecting a behavior change amount of the moving body; selecting, from among the accumulated target position data, the target position data of the relative position detected during a period in which the behavior change amount is less than a threshold value; and collating the selected target position data with map information indicating a position on a map of the target to estimate a present position of the moving body.

A step detection device detects the distance and the direction to a road surface around a vehicle, using a distance sensor mounted on the vehicle, and sets a first step determination position and a second step determination position on the road surface. Then, the step detection device calculates the height changes of the road surfaces at the first step determination position and the second step determination position based on the distance and the direction to the road surface, and detects a step on the road surface based on one of the step determination positions, the gradient of the height change of the road surface at the one being larger.

Step detection device and step detection method

Multiple objects detected by multiple sensors are subjected to a determination as to whether or not the objects are identical to each other. If there is an object in the multiple objects, of which object position becomes undetectable after the point when the multiple objects detected by the multiple sensors are determined to be the identical object, a determination is made as to whether or not the continuously detected object is an object identical to the object in a previous processing based on a predicted range calculated from a previously detected object position of the object becoming undetectable, and on a range of presence estimated from an object position of the continuously detected object.

Object detecting method and object detecting device

Device () comprises: a stereo image acquisition unit () for acquiring a standard image and a reference image for a stereo image; an image segmenting unit () for acquiring standard pixel data and reference pixel data from the stereo image; a phase correction unit () for carrying out for each of the pixel data a phase correction process making the values of the standard pixel data more symmetrical; a windowing unit () for applying a windowing function to each of the pixel data that have undergone phase correction; a phase correlation unitfor calculating a phase correlation value between the two pixel data that have undergone windowing; and a peak position detection unit () for calculating, on the basis of the phase correlation value, the parallax of a corresponding point of the reference image with respect to a standard point on the standard image.

Stereo image processing device and stereo image processing method

A disparity calculation apparatus for a stereo camera implements ranging of an object that includes consecutive similar patterns. In stereo matching, if a plurality of corresponding point candidates are present in a sum of absolute differences or similar evaluation value distribution for a target point, an evaluation value map is generated by superimposing an evaluation value distribution of a target point, for which a plurality of corresponding points are determined to be present, and an evaluation value distribution of each other target point present in a peripheral area of that target point. The shape of an object is represented in real space around a target point for which a plurality of corresponding points are determined to be present. The true distance of a railing that extends in a straight line is determined by extracting a line segment with the strongest linearity in the evaluation value map.

Growing community of inventors

Kanagawa, Japan

Takuya Nanri