Abstract: A method includes: recognizing a signal candidate position around a vehicle by using a sensor mounted on the vehicle, the signal candidate position being a position a traffic light is possibly present, the vehicle including a light configured in such a manner that a light distribution state of the light changes; and performing a first light control process of controlling the light in response to recognition of the signal candidate position, the first light control process including at least one of: increasing an intensity of light emitted to the signal candidate position to a value higher than before the recognition of the signal candidate position; or making the intensity of the light emitted to the signal candidate position higher than an intensity of light emitted to an area other than the signal candidate position.

Abstract: A stop line detection apparatus recognizes a boundary line of a lane on which the host vehicle travels, and detect a stop line at an intersection in front of the host vehicle. The apparatus sets an intersection area corresponding to the intersection in the road on which the host vehicle travels on the basis of a captured image by a front camera of the host vehicle, detects a crosswalk in the intersection area, a stop line search area setting unit configured to set a stop line search area extending from the crosswalk toward a front side in the lane on which the host vehicle travels, and performs stop line detection in the stop line search area. A depth of the stop line search area in an extending direction of a traveling lane is smaller than twice a depth of the crosswalk.

Abstract: A tracking device includes a processor configured to detect regions each having a confidence score, which indicates how likely a predetermined object is represented, not less than a predetermined detection threshold as candidate regions, determine whether one of the candidate regions can be associated with the predetermined object, based on optical flow between an object region representing the predetermined object in a past image earlier than the image and each candidate region, increment a tracking count of the object by one when the association can be made, set the detection threshold applied to a subsequent image later than the image at a first value when the tracking count is not greater than a predetermined number, and set the detection threshold applied to the subsequent image at a second value lower than the first value when the tracking count is greater than the predetermined number.

Abstract: The control device according to the present disclosure executes a process of acquiring an image captured by a camera provided in a vehicle and estimating an vehicle-to-vehicle distance and a relative speed with the preceding vehicle based on an image, a process of calculating a time to collision with respect to the preceding vehicle from the vehicle-to-vehicle distance and the relative speed, a deceleration process of decelerating the vehicle based on the vehicle-to-vehicle distance, the relative speed, and the time to collision, and a process of performing a warning to the driver of the vehicle upon receiving that the amplitude of the variation of any one physical quantity of the vehicle-to-vehicle distance, the relative speed, or the time to collision has become equal to or greater than a predetermined threshold value.

Abstract: An apparatus for estimating distance inputs an outside image representing the situation around a vehicle and obtained from a camera mounted on the vehicle into a classifier to detect a vehicle region representing a target vehicle and to classify the target vehicle represented in the detected vehicle region as one of preregistered types of vehicles. The apparatus then identifies the position of a virtual object corresponding to the target vehicle represented in the vehicle region, and estimates the distance from the vehicle to the virtual object as the distance from the vehicle to the target vehicle.

Abstract: An object detection device comprising a first detecting part detecting types and positions of objects including a road mirror in an image and a presence of a movable object reflected in the road mirror based on image data of a low resolution image and a second detecting part detecting a type of the movable object in the road mirror and a position of the movable object in the road mirror based on high resolution partial image data obtained by extracting the part of the road mirror from the image if there is a movable object reflected in the road mirror.

Abstract: A proceedable direction detection apparatus includes a processor. The processor inputs an image acquired from an image capturing unit mounted on a vehicle into a classifier that is pre-trained to output a proceedable certainty degree indicating a proceedable probability of each relative bearing with respect to a predetermined direction relative to a vehicle based on a display status of a traffic signal in the image. The processor calculates, using the classifier, the proceedable certainty degree of each relative bearing based on the display status of the traffic signal in the image. The processor determines the direction in which the vehicle can proceed based on a proceedable certainty degree of each relative bearing.

Abstract: An object detection device includes a processor configured to calculate, for each of a plurality of regions in a detection range of the sensor represented in the newest sensor signal among a plurality of sensor signals in time-series acquired by a sensor, a confidence indicating a degree of certainty that an object to be detected is represented in the region; track a first object which has been detected, to detect, in the newest sensor signal, a passed region through which the first object has passed; control, for each of the plurality of regions in the newest sensor signal, a confidence threshold according to whether or not the region is included in the passed region, and detect a second object in a region, among the plurality of regions, with respect to which the confidence for the second object is equal to or higher than the confidence threshold.

Abstract: An image selection device includes a processor configured to: input, for each of a series of images acquired from a camera mounted on a vehicle, the image to a classifier to detect a region including an object represented on the image; track the detected object over the series of images; and select, when a period in which the detected object can be tracked is equal to or more than a predetermined period, and a size of a region including the detected object in any one image during the period in which the object can be tracked is equal to or more than a predetermined size threshold value, among the series of images, an image immediately before the period in which the object can be tracked, or an image in which the tracked object is not represented during the period in which the object can be tracked.

Abstract: An object detection device calculates, for each of images acquired in time-series, a confidence score of each of a plurality of types of an object to be detected for each of a plurality of regions on the image, detects the type of the object corresponding to the confidence score in a region in which the confidence score is equal to or higher than a confidence threshold of the type for each of the images, tracks the detected object, counts a frequency of occurrence for each type of the detected object in a period in which the detected object is tracked, and updates the confidence threshold in such a way that the confidence threshold of a type having a higher frequency of occurrence is lower than the confidence threshold of a type having a lower frequency of occurrence.

Abstract: A training image selection system 1, 1? comprises: an imaging device 21, 22 capturing an outside of a host vehicle to generate images, a storage device 32 storing the images generated by the imaging device, a detecting part 34 configured to detect operation of signaling lights of the host vehicle, and an image selecting part 35 configured to select training images from the images generated by the imaging device. If the detecting part detects operation of signaling lights, the image selecting part is configured to select images generated at timings within a predetermined range from when operation of signaling lights is detected, as training images.

Abstract: A distance estimation device includes: a processor configured to detect the predetermined object represented in an image acquired by a camera, and calculate a confidence score that, for each of the plurality of types of the predetermined object, the detected object belongs to the type, estimate a size in the real space of the detected object by weighted-averaging a reference size in a real space of each of the plurality of types by the confidence score of the type, and estimate a distance to the detected object on the basis of a ratio between the detected size of the predetermined object on the image and an estimated size of the detected object on the image when a distance to the detected object is a predetermined reference distance, which corresponds to the estimated size in the real space of the detected object.

Abstract: An object detection device includes a processor configured to calculate, for each of a plurality of regions in an input image, a confidence indicating a degree of certainty that an object to be detected is represented; determine that the object is represented in a first region for which the confidence is equal to or higher than a first confidence threshold; calculate an occlusion ratio of an occluded region, in which the object is occluded in the first region by another object, to the first region; and determine that the object to be detected is represented in a second region which at least partially overlaps with the first region, when the occlusion ratio is equal to or higher than an occlusion ratio threshold and the confidence for the second region is equal to or higher than a second confidence threshold which is lower than the first confidence threshold.

Abstract: An object detection device 30 comprises a position region detecting part 31 using a first neural network to detect a position region of an object in the image, a large attribute identification part 32 configured using a second neural network to identify a large attribute of the object, a small attribute identification part 33 using a third neural network to identify a small attribute of the object, and an object judging part 34 judging a result of detection of the object. The object judging part is configured to judge that a result of identification of the small attribute is the result of detection if a confidence of the result of identification of the small attribute is equal to or more than a threshold value, and judge the result of detection based on a result of identification of the large attribute if the confidence is less than the threshold value.

Abstract: An object recognition apparatus is provided with: a first output device configured to output a provisional position of a first object and a tracking priority by using a neural network; and a second output device configured to receive entries of the first image data, the provisional position and the tracking priority, and configured (i) to perform a time-series tracking process and output a position and a speed, for the first object in which the tracking priority satisfies a predetermined condition, and (ii) to output a position without performing the time-series tracking process, for the first object in which the tracking priority does not satisfy the predetermined condition. The neural network is a learned neural network that has learned by using teacher data, wherein the teacher data includes second image data, and correct answer data.

Abstract: An object detection device calculates, for each of images acquired in time-series, a confidence score of each of a plurality of types of an object to be detected for each of a plurality of regions on the image, detects the type of the object corresponding to the confidence score in a region in which the confidence score is equal to or higher than a confidence threshold of the type for each of the images, tracks the detected object, counts a frequency of occurrence for each type of the detected object in a period in which the detected object is tracked, and updates the confidence threshold in such a way that the confidence threshold of a type having a higher frequency of occurrence is lower than the confidence threshold of a type having a lower frequency of occurrence.

Abstract: A distance estimation device includes: a processor configured to detect the predetermined object represented in an image acquired by a camera, and calculate a confidence score that, for each of the plurality of types of the predetermined object, the detected object belongs to the type, estimate a size in the real space of the detected object by weighted-averaging a reference size in a real space of each of the plurality of types by the confidence score of the type, and estimate a distance to the detected object on the basis of a ratio between the detected size of the predetermined object on the image and an estimated size of the detected object on the image when a distance to the detected object is a predetermined reference distance, which corresponds to the estimated size in the real space of the detected object.

Abstract: An object detection device is configured to: detect from an acquired image whether a predetermined part of an outer shape of an object in question shown in that image is hidden by another object shown in that image; and detect a position and/or size of the object in question based on a position of the part of the outer shape of the object in question hidden by the other object, when it is detected that the predetermined part of the outer shape of the object in question is hidden.

Abstract: An image selection device includes a processor configured to: input, for each of a series of images acquired from a camera mounted on a vehicle, the image to a classifier to detect a region including an object represented on the image; track the detected object over the series of images; and select, when a period in which the detected object can be tracked is equal to or more than a predetermined period, and a size of a region including the detected object in any one image during the period in which the object can be tracked is equal to or more than a predetermined size threshold value, among the series of images, an image immediately before the period in which the object can be tracked, or an image in which the tracked object is not represented during the period in which the object can be tracked.

Abstract: A proceedable direction detection apparatus includes a processor. The processor inputs an image acquired from an image capturing unit mounted on a vehicle into a classifier that is pre-trained to output a proceedable certainty degree indicating a proceedable probability of each relative bearing with respect to a predetermined direction relative to a vehicle based on a display status of a traffic signal in the image. The processor calculates, using the classifier, the proceedable certainty degree of each relative bearing based on the display status of the traffic signal in the image. The processor determines the direction in which the vehicle can proceed based on a proceedable certainty degree of each relative bearing.