Nissan Motor Company Limited

Renault S.a.s.

Akinobu Ikeda

Susumu Fujita

Reo Sakakura

An object recognition method includes generating a cluster at a fixed period by clustering distance measurement points acquired from an object detection sensor, mapping the cluster to an image captured by an imaging means, setting a region on the image to which the cluster is mapped as a processing region, extracting a feature point from the image in the set processing region, calculating a movement speed of the extracted feature point, and determining whether clusters at different time points are clusters of an identical object based on a difference between positions of two feature points corresponding to the clusters at the different time points and a difference between movement speeds of the two feature points corresponding to the clusters at the different time points.

Object recognition method and object recognition device

A map information correction method for correcting map information includes information of a lane boundary line, the method further including: detecting a position with respect to an own vehicle of a lane boundary line set in place on a road surface around the own vehicle; estimating an own position on a map of the own vehicle; and correcting, depending on the estimated own position and the detected position of the lane boundary line, a position of the lane boundary line included in the map information by, in a first region comparatively close to the own vehicle, a larger rotational correction amount than in a second region comparatively far from the own vehicle and, in the second region, a larger translational correction amount than in the first region.

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Kanagawa, Japan

Akinobu Ikeda