Nissan North America, Inc.

Kevin Kochever

Stefan Witwicki

Erik St Gray

Mark Bailey

A system may detect a sound associated with a vehicle. The sound may be detected using a set of one or more microphones. The system may use a machine learning model to detect at least one of a squeak or a rattle based on the sound. The machine learning model may be trained using a plurality of sounds emitted in relation to one or more vehicles. The system may determine, based on detecting at least one of the squeak or the rattle, a location of the vehicle associated with at least one of the squeak or the rattle. In some implementations, the location may be determined based on detecting different sound levels between a first microphone and a second microphone, or detecting a time difference between the first and the second microphone, or by correlating the sound to a sound signature associated with a part of the vehicle.

Determining a location of a squeak or rattle associated with a vehicle

Vehicle control using map-based braking includes receiving, while a driver is operating the vehicle to traverse a vehicle transportation network, vehicle operation information including at least a current speed of the vehicle, retrieving, from a planned path of an in-vehicle navigation system, an upcoming turn in a current road when the driver is using the in-vehicle navigation system, and retrieving from map data, the upcoming turn in the current road when the driver is not using tin-vehicle navigation system. Thereafter, it is determined whether, during the upcoming turn, wheels of the vehicle will maintain contact with a road surface at the current speed. A braking instruction is issued to a control system of the vehicle responsive to whether the wheels of the vehicle will maintain contact with the road surface at the current speed.

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San Bruno, CA, United States of America

Kevin Kochever