Abstract: A system is provided that includes a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: receive an image depicting a parking spot, determine a length of the parking spot based on a classified endpoint of the parking spot, compare the length to an average length, and determine an endpoint of the parking spot when the length is less than the average length, wherein the determined endpoint is distal to the classified endpoint.

Abstract: A method of predicting lane line types utilizing a heterogeneous convolutional neural network (HCNN) includes capturing an input image with one or more optical sensors disposed on a host member, passing the input image through the HCNN, the HCNN having at least three distinct sub-networks, the three distinct sub-networks: predicting object locations in the input image with a first sub-network; predicting lane line locations in the input image with a second sub-network; and predicting lane line types for each predicted lane line in the input image with a third sub-network.

Abstract: A method of localizing a host member through sensor fusion includes capturing an input image with one or more optical sensors disposed on the host member and determining a location of the host member through a global positioning system (GPS) input. The method tracks movement of the host member through an inertial measurement unit (IMU) input, generates coordinates for the host member from the GPS input and the IMU input. The method compares the input image and a high definition (HD) map input to verify distances from the host member to predetermined objects within the input image and within the HD map input. The method continuously localizes the host member by fusing the GPS input, the IMU input, the input image, and the HD map input.