Abstract: A traveling path setting method for a vehicle has a set traveling path of a host vehicle that includes a traveling path that turns across another lane at an intersection. The traveling path setting method is configured to set a traveling path for turning of the vehicle to an inner side of a turning direction when an adjacent vehicle exists within a predetermined distance outward of the traveling path of the host vehicle in the turning direction, compared to a case where no adjacent vehicle exists.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: An action prediction method for predicting an action of another vehicle around a host vehicle, acquires information on conditions of a road surface around the other vehicle, and predicts the action of the other vehicle in accordance with the information on the conditions of the road surface.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A vehicle behavior prediction apparatus 1 includes: an object detection unit that detects the position of a target vehicle around a host vehicle; and a controller that acquires road structure around the position of the target vehicle including at least a traffic lane. The controller acquires a traffic rule for the road structure and predicts a route on which the target vehicle will travel, based on the acquired traffic rule.