Abstract: The present invention extends to methods, systems, and computer program products for changing vehicle configuration based on vehicle storage compartment contents. At an autonomous vehicle, a camera is mounted inside a storage compartment. The camera monitors the interior of the storage compartment. The camera can confirm that the storage compartment is empty when it is supposed to be empty and contains an object when it is supposed to contain an object. Any discrepancies can be reported to a human operator. The human operator can instruct the autonomous vehicle to change configuration to address discrepancies. In one aspect, a machine-learning camera memorizes a background pattern permeated to a surface of the storage compartment. The machine-learning camera detects objects in the storage compartment based on disturbances to the background pattern.

Abstract: Systems and methods for mitigating wind throb in vehicles are disclosed herein. An example method includes detecting wind throb in a cabin of a vehicle due to opening of a first window of the vehicle, determining when a seat that is adjacent to a second window of the vehicle is unoccupied, opening the second window when the seat is determined to be unoccupied so as to compensate for the wind throb, determining that the first window has been closed, and closing the second window in response to closing of the first window.

Abstract: Systems and methods for mitigating wind throb in vehicles are disclosed herein. An example method includes detecting wind throb in a cabin of a vehicle due to opening of a first window of the vehicle, determining when a seat that is adjacent to a second window of the vehicle is unoccupied, opening the second window when the seat is determined to be unoccupied so as to compensate for the wind throb, determining that the first window has been closed, and closing the second window in response to closing of the first window.

Abstract: The present invention extends to methods, systems, and computer program products for changing vehicle configuration based on vehicle storage compartment contents. At an autonomous vehicle, a camera is mounted inside a storage compartment. The camera monitors the interior of the storage compartment. The camera can confirm that the storage compartment is empty when it is supposed to be empty and contains an object when it is supposed to contain an object. Any discrepancies can be reported to a human operator. The human operator can instruct the autonomous vehicle to change configuration to address discrepancies. In one aspect, a machine-learning camera memorizes a background pattern permeated to a surface of the storage compartment. The machine-learning camera detects objects in the storage compartment based on disturbances to the background pattern.

Abstract: A rearward viewing system for an automotive vehicle comprises an image display mounted proximate to a front windshield of the vehicle. An exterior image sensor captures a first image directed externally of the vehicle with a first field of view. An internal image sensor captures a second image directed internally of the vehicle with a second field of view. An image integrator merges the first and second images according to a predetermined alignment of the first and second fields of view, wherein the second image is muted in the merged image so that the first image is emphasized when displayed on the image display.

Abstract: A rearward viewing system for an automotive vehicle comprises an image display mounted proximate to a front windshield of the vehicle. An exterior image sensor captures a first image directed externally of the vehicle with a first field of view. An internal image sensor captures a second image directed internally of the vehicle with a second field of view. An image integrator merges the first and second images according to a predetermined alignment of the first and second fields of view, wherein the second image is muted in the merged image so that the first image is emphasized when displayed on the image display.