CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Korean Patent Application No. 10-2019-0128056, filed in the Korean Intellectual Property Office on Oct. 15, 2019, which application is hereby incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to a technology of determining a lane change path of an autonomous vehicle based on deep learning.

BACKGROUND

In general, deep learning (or a deep neural network), which is a type of machine learning, may be composed of artificial neural networks (ANNs) of several layers between an input and an output. Such an artificial neural network may include a convolutional neural network (CNN) or a recurrent neural network (RNN) corresponding to a structure, a problem and an object to be solved.

The deep learning is used to solve various problems such as classification, regression, localization, detection, segmentation, and the like. In particular, in an autonomous driving system, semantic segmentation and object detection, which can distinguish the location and type of a dynamic and static obstacle, are important.

The semantic segmentation performs segmentation prediction in units of pixels and dividing the image in units of pixels of identical meaning in order to detect an object in an image, and thus, it is possible to not only identify which object is present in the image but also exactly pinpoint the positions of pixels having the same meaning (the same object).

Object detection means classifying and predicting the type of object in an image to find location information of the object by regression prediction of a bounding box, and it is possible to understand not only what type of object is in the image differently from simple classification but also location information of the object.

There has not been proposed a technology of determining a lane change path of an autonomous vehicle based on such deep learning.

SUMMARY

The present disclosure has been made to solve problems occurring in the prior art while advantages achieved by the prior art are maintained intact.

An embodiment of the present disclosure provides an apparatus for determining a lane change path of an autonomous vehicle and a method thereof which can extract at least two lane change paths corresponding to a lane change strategy among a plurality of lane change paths based on deep learning and determine a final lane change path based on properties of the extracted lane change paths, so that it is possible to allow the autonomous vehicle to be driven along the lane change path optimized for a driving situation changing in real time, thereby improving the driving stability.

Technical problems to be solved by the present inventive concept are not limited to the aforementioned problems, and any other technical problems not mentioned herein will be clearly understood from the following description by those skilled in the art to which the present disclosure pertains.

According to an embodiment of the present disclosure, an apparatus for determining a lane change path of an autonomous vehicle includes a learning device that learns lane change paths corresponding to a lane change strategy of the autonomous vehicle, and a controller that interworks with the learning device to extract at least two lane change paths corresponding to the lane change strategy among a plurality of lane change paths in a drivable area of the autonomous vehicle and determine a final lane change path based on properties of the extracted lane change paths.

The controller may generate the plurality of lane change paths based on a drivable area of the autonomous vehicle corresponding to a driving path of a dynamic object and a drivable area of the autonomous vehicle corresponding to a distribution of static objects.

The controller may generate a plurality of lane change paths proceeding to a left side of an obstacle and a plurality of lane change paths proceeding to a right side of the obstacle when the obstacle is located on a lane boundary within the drivable area of the autonomous vehicle. In this case, the controller may extract a lane change path corresponding to the lane change strategy from the plurality of lane change paths proceeding to the right side of the obstacle.

The controller may determine one of a normal lane change, a return from a current lane during lane change, and a return from a target lane during the lane change as the lane change strategy. In this case, the controller may periodically determine a new lane change strategy suitable to a current situation while the autonomous vehicle changes lanes.

The controller may determine a final lane change path based on a collision risk, a path curvature and a path length of each extracted lane change path. In this case, the controller may determine a lane change path having a minimum sum of the collision risk, the path curvature and the path length of each extracted lane change path as the final lane change path, or may determine a lane change path having a minimum average of the collision risk, the path curvature and the path length of each extracted lane change path as the final lane change path.

According to another embodiment of the present disclosure, a method of determining a lane change path of an autonomous vehicle includes learning, by a learning device, lane change paths corresponding to a lane change strategy of the autonomous vehicle, extracting, by a controller interworking with the learning device, at least two lane change paths corresponding to the lane change strategy among a plurality of lane change paths in a drivable area of the autonomous vehicle, and determining, by the controller, a final lane change path based on properties of the extracted lane change paths.

The extracting of the lane change path may include generating the plurality of lane change paths based on a drivable area of the autonomous vehicle corresponding to a driving path of a dynamic object and a drivable area of the autonomous vehicle corresponding to a distribution of static objects.

The extracting of the lane change path may include generating a plurality of lane change paths proceeding to a left side of an obstacle and a plurality of lane change paths proceeding to a right side of the obstacle when the obstacle is located on a lane boundary within the drivable area of the autonomous vehicle, and extracting a lane change path corresponding to the lane change strategy from the plurality of lane change paths proceeding to the right side of the obstacle.

The extracting of the lane change path may include determining one of a normal lane change, a return from a current lane during lane change, and a return from a target lane during the lane change as the lane change strategy. In this case, the determining of the lane change strategy may include periodically determining a new lane change strategy suitable to a current situation while the autonomous vehicle changes lanes.

The determining of the lane change strategy may include determining a lane change path having a minimum sum of a collision risk, a path curvature and a path length of each extracted lane change path as the final lane change path.

The determining of the lane change strategy may include determining a lane change path having a minimum average of a collision risk, a path curvature and a path length of each extracted lane change path as the final lane change path.

