Abstract: Example systems and methods for detecting an animal proximate a vehicle are described. In one implementation, a wearable device carried by an animal is activated when the wearable device is within a predetermined distance of a vehicle. The vehicle receives a signal from the wearable device and determines an approximate distance between the wearable device and the vehicle. An alert is generated to warn a driver that an animal is near the vehicle. The alert has an intensity level that corresponds to the approximate distance between the wearable device and the vehicle.

Abstract: Example systems and methods for communicating animal proximity to a vehicle are described. In one implementation, a device implanted in an animal is activated when the device is within a predetermined distance of a vehicle. The vehicle receives a signal from the device and determines an approximate distance between the device and the vehicle. A symbol is flashed to a driver of the vehicle at a frequency that corresponds to the approximate distance between the device and the vehicle.

Abstract: Example systems and methods for communicating animal proximity to a vehicle are described. In one implementation, a device implanted in an animal is activated when the device is within a predetermined distance of a vehicle. The vehicle receives a signal from the device and determines an approximate distance between the device and the vehicle. A symbol is flashed to a driver of the vehicle at a frequency that corresponds to the approximate distance between the device and the vehicle.

Abstract: Example systems and methods for detecting an animal proximate a vehicle are described. In one implementation, a wearable device carried by an animal is activated when the wearable device is within a predetermined distance of a vehicle. The vehicle receives a signal from the wearable device and determines an approximate distance between the wearable device and the vehicle. An alert is generated to warn a driver that an animal is near the vehicle. The alert has an intensity level that corresponds to the approximate distance between the wearable device and the vehicle.

Abstract: A controller for an autonomous vehicle receives audio signals from one or more microphones and identifies sounds. The controller further identifies an estimated location of the sound origin and the type of sound, i.e. whether the sound is a vehicle and/or the type of vehicle. The controller analyzes map data and attempts to identify a landmark within a tolerance from the estimated location. If a landmark is found corresponding to the estimated location and type of the sound origin, then the certainty is increased that the source of the sound is at that location and is that type of sound source. Collision avoidance is then performed with respect to the location of the sound origin and its type with the certainty as augmented using the map data. Collision avoidance may include automatically actuating brake, steering, and accelerator actuators in order to avoid the location of the sound origin.

Abstract: A controller for an autonomous vehicle receives audio signals from one or more microphones and identifies sounds. The controller further identifies an estimated location of the sound origin and the type of sound, i.e. whether the sound is a vehicle and/or the type of vehicle. The controller analyzes map data and attempts to identify a landmark within a tolerance from the estimated location. If a landmark is found corresponding to the estimated location and type of the sound origin, then the certainty is increased that the source of the sound is at that location and is that type of sound source. Collision avoidance is then performed with respect to the location of the sound origin and its type with the certainty as augmented using the map data. Collision avoidance may include automatically actuating brake, steering, and accelerator actuators in order to avoid the location of the sound origin.

Abstract: A controller for an autonomous vehicle receives an image stream from one or more imaging devices. The controller identifies vehicle images in the image stream. Vehicle images are compared to the color, shape, badging, markings, license plate, and driver of the autonomous vehicle. If the vehicle image is determined to match the autonomous vehicle, then the vehicle image is ignored as a potential obstacle. The location of a reflective surface that generated the vehicle image may be determined and added to a set of potential obstacles. The color and shape of a vehicle in a vehicle image may be evaluated first. Only if the color and shape in the vehicle image match the autonomous vehicle are other factors such as badging, markings, license plate, and driver considered. Vehicle images not matching the autonomous vehicle are included in a set of potential obstacles.

Abstract: A controller for an autonomous vehicle receives an image stream from one or more imaging devices. The controller identifies vehicle images in the image stream. Vehicle images are compared to the color, shape, badging, markings, license plate, and driver of the autonomous vehicle. If the vehicle image is determined to match the autonomous vehicle, then the vehicle image is ignored as a potential obstacle. The location of a reflective surface that generated the vehicle image may be determined and added to a set of potential obstacles. The color and shape of a vehicle in a vehicle image may be evaluated first. Only if the color and shape in the vehicle image match the autonomous vehicle are other factors such as badging, markings, license plate, and driver considered. Vehicle images not matching the autonomous vehicle are included in a set of potential obstacles.

Abstract: A controller for an autonomous vehicle receives audio signals from one or more microphones and identifies sounds. The controller further identifies an estimated location of the sound origin and the type of sound, i.e. whether the sound is a vehicle and/or the type of vehicle. The controller analyzes map data and attempts to identify a landmark within a tolerance from the estimated location. If a landmark is found corresponding to the estimated location and type of the sound origin, then the certainty is increased that the source of the sound is at that location and is that type of sound source. Collision avoidance is then performed with respect to the location of the sound origin and its type with the certainty as augmented using the map data. Collision avoidance may include automatically actuating brake, steering, and accelerator actuators in order to avoid the location of the sound origin.