Abstract: This disclosure relates generally to vehicle navigation maps, and more particularly to method and system for generating and updating vehicle navigation maps with features of navigation paths. In one embodiment, a method may include receiving a position of a vehicle and an environmental field of view (FOV) of the vehicle along a navigation path of the vehicle on a navigation map, extracting features of the navigation path from the environmental FOV, correlating the features with the navigation path on the navigation map based on the position, generating a features based navigation map based on the correlation, and transmitting the features based navigation map to a server of a navigation map service provider for storage and for subsequent use. The features based navigation map, when required to assist a navigation of another vehicle, may be accessed to assess the features of the navigation path and to provide the assessment to the other vehicle.

Abstract: The present disclosure relates to detection of obstacles by an autonomous vehicle in real-time. An obstacle detection system of an autonomous vehicle obtains point cloud data from single frame Light Detection and Ranging (LIDAR) data and camera data, of the surroundings of the vehicle. Further, the system processes the camera data to identify and extract regions comprising the obstacles. Further, the system extracts point cloud data corresponding to the obstacles and enhances the point cloud data, with the detailed information of the obstacles provided by the camera data. Further, the system processes the enhanced point cloud data to determine the obstacles along with the structure and orientation of obstacles. Upon determining the details of the obstacles, the system provides instructions to the vehicle to manoeuvre the obstacle. The disclosed obstacle detection system provides accurate data about a structure, an orientation and a location of the obstacles.

Abstract: A method and system for guiding an autonomous vehicle to extract drivable road region is provided. The method involves capturing the road region ahead of the autonomous vehicle using a plurality of sensors. The road region is captured as a three-dimensional point cloud. Thereafter, a plurality of images of the road ahead the autonomous vehicle is captured using a camera. The captured road region and the plurality of images are mapped and compared. The mapping involves comparing the point cloud with plurality of pixels in the images. Based on the mapping, a training data is dynamically updated to incorporate current road conditions and a drivable road region is predicted. Finally, based on the drivable region, the autonomous vehicle is controlled and guided through the road.

Abstract: The present disclosure relates to detection of obstacles by an autonomous vehicle in real-time. An obstacle detection system of an autonomous vehicle obtains point cloud data from single frame Light Detection and Ranging (LIDAR) data and camera data, of the surroundings of the vehicle. Further, the system processes the camera data to identify and extract regions comprising the obstacles. Further, the system extracts point cloud data corresponding to the obstacles and enhances the point cloud data, with the detailed information of the obstacles provided by the camera data. Further, the system processes the enhanced point cloud data to determine the obstacles along with the structure and orientation of obstacles. Upon determining the details of the obstacles, the system provides instructions to the vehicle to manoeuvre the obstacle. The disclosed obstacle detection system provides accurate data about a structure, an orientation and a location of the obstacles.

Abstract: This disclosure relates generally to vehicle navigation maps, and more particularly to method and system for generating and updating vehicle navigation maps with features of navigation paths. In one embodiment, a method may include receiving a position of a vehicle and an environmental field of view (FOV) of the vehicle along a navigation path of the vehicle on a navigation map, extracting features of the navigation path from the environmental FOV, correlating the features with the navigation path on the navigation map based on the position, generating a features based navigation map based on the correlation, and transmitting the features based navigation map to a server of a navigation map service provider for storage and for subsequent use. The features based navigation map, when required to assist a navigation of another vehicle, may be accessed to assess the features of the navigation path and to provide the assessment to the other vehicle.

Abstract: A method and system for guiding an autonomous vehicle to extract drivable road region is provided. The method involves capturing the road region ahead of the autonomous vehicle using a plurality of sensors. The road region is captured as a three-dimensional point cloud. Thereafter, a plurality of images of the road ahead the autonomous vehicle is captured using a camera. The captured road region and the plurality of images are mapped and compared. The mapping involves comparing the point cloud with plurality of pixels in the images. Based on the mapping, a training data is dynamically updated to incorporate current road conditions and a drivable road region is predicted. Finally, based on the drivable region, the autonomous vehicle is controlled and guided through the road.

Abstract: Method and device for identifying path boundary for vehicle navigation are disclosed. The method includes capturing a plurality of images of a path being traversed by a vehicle, through a plurality of cameras placed to meet predefined placement criteria. The method further includes processing shadowed regions within each of the plurality of images based on an associated Hue Saturation and Value (HSV) color space to generate a plurality of shadow processed images. The method includes identifying boundaries of the path within each of the plurality of shadow processed images based on a histogram of each of the plurality of shadow processed images. The method further includes estimating a distance between the boundaries of the path identified in the plurality of shadow processed images, based on a disparity map created for the plurality of shadow processed images and parameters associated with the plurality of cameras.