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.

Abstract: A parking detection method includes: accessing data corresponding to an image from a camera on a vehicle; computing a confidence score for a possible vacant parking location from the data corresponding to the image from the camera on the vehicle; and when the confidence score is greater than a threshold confidence, identifying the possible vacant parking location as an actual vacant parking location from the data corresponding to the image from the camera on the vehicle.

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.

Abstract: A system and method for predicting object detection and lane detection for a motor vehicle includes a convolution neural network (CNN) that receives an input image and a lane line module. The CNN includes a set of convolution and pooling layers (CPL's) trained to detect objects and lane markings from the input image, the objects categorized into object classes and the lane markings categorized into lane marking classes to generate a number of feature maps, a fully connected layer that receives the feature maps, the fully connected layer generating multiple object bounding box predictions for each of the object classes and multiple lane bounding box predictions for each of the lane marking classes from the feature maps, and a non-maximum suppression layer generating a final object bounding box prediction for each of the object classes and generating multiple final lane bounding box predictions for each of the lane marking classes.

Abstract: A system and method for predicting object detection and lane detection for a motor vehicle includes a convolution neural network (CNN) that receives an input image and a lane line module. The CNN includes a set of convolution and pooling layers (CPL's) trained to detect objects and lane markings from the input image, the objects categorized into object classes and the lane markings categorized into lane marking classes to generate a number of feature maps, a fully connected layer that receives the feature maps, the fully connected layer generating multiple object bounding box predictions for each of the object classes and multiple lane bounding box predictions for each of the lane marking classes from the feature maps, and a non-maximum suppression layer generating a final object bounding box prediction for each of the object classes and generating multiple final lane bounding box predictions for each of the lane marking classes.