A machine learning model is trained by defining a scenario including models of vehicles and a typical driving environment. A model of a subject vehicle is added to the scenario and sensor locations are defined on the subject vehicle. A perception of the scenario by sensors at the sensor locations is simulated. The scenario further includes a model of a lane-splitting vehicle. The location of the lane-splitting vehicle and the simulated outputs of the sensors perceiving the scenario are input to a machine learning algorithm that trains a model to detect the location of a lane-splitting vehicle based on the sensor outputs. A vehicle controller then incorporates the machine learning model and estimates the presence and/or location of a lane-splitting vehicle based on actual sensor outputs input to the machine learning model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: defining, by a computer system, a three dimensional (3D) model; simulating, by the computer system, two or more sensor outputs from sound from a lane-splitting vehicle incident on two or more sensor locations of a subject vehicle in the 3D model; and training, by the computer system, a machine-learning model using a location of the lane-splitting vehicle in the 3D model over time and the two or more sensor outputs. 2. The method of claim 1, further comprising: defining on the subject vehicle one or more camera locations; simulating detection of images at the one or more camera locations; and training the machine-learning model using both the images and the two or more sensor outputs. 3. The method of claim 2 further comprising: defining on the subject vehicle a RADAR sensor location; simulating a RADAR sensor output according to the 3D model; and training the machine learning model using all of the images, the RADAR sensor output, and the two or more sensor outputs. 4. The method of claim 3, further comprising: defining on the subject vehicle a LIDAR sensor location; simulating a sequence of point clouds detected from the LIDAR sensor location of the 3D model; and training the machine learning model using all of the images, the RADAR sensor output, the sequence of point clouds, and the two or more sensor outputs. 5. The method of claim 1, wherein defining the 3D model further includes defining velocities for the plurality of vehicles, the lane-splitting vehicle and the subject vehicle. 6. The method of claim 1, wherein the machine-learning model is a deep neural network. 7. The method of claim 1, wherein simulating the two or more sensor outputs from the sound of the plurality of vehicles incident on the two or more sensor locations comprises simulating Doppler effects and propagation distances of the sound. 8. The method of claim 1, where the lane-splitting vehicle is modeled as a lane-splitting motorcycle emitting engine noises characteristic of a motorcycle. 9. The method of claim 1, further comprising: providing a vehicle including a vehicle controller and two or more microphones; programming the vehicle controller with the machine-learning model; receiving, by the vehicle controller, two or more audio streams from the two or more microphones; inputting, by the vehicle controller, the two or more audio streams to the machine-learning model; (a) determining, by the vehicle controller, that the machine-learning model indicates that the two or more audio streams currently indicate a lane-splitting vehicle is present; in response to (a) at least one of outputting an alert and refraining from causing the vehicle to enter an inter-lane region. 10. The method of claim 9, further comprising: (b) determining, by the vehicle controller, subsequent to (a) that the machine-learning model indicates that the two or more audio streams do not currently indicate a lane-splitting vehicle is present; in response to (b), discontinuing outputting the alert by the vehicle controller and permitting, by the vehicle controller, movement into the inter-lane region. 11. A system comprising one or more processors and one or more memory devices coupled to the one or more processors, the one or more memory devices storing executable code effective to: define a three dimensional model including a road and a plurality of vehicles, a lane-splitting vehicle, and a subject vehicle including two or more sensor locations positioned on the road; simulate two or more sensor outputs from sound from the plurality of vehicles incident on the two or more sensor locations; and train a machine-learning model using a location of the lane-splitting vehicle over time and the two or more sensor outputs. 12. The system of claim 11, wherein the executable code is further effective to cause the one or more processors to: define on the subject vehicle one or more camera locations; simulate detection of images at the one or more camera locations; and train the machine-learning model using both the images and the two or more sensor outputs. 13. The system of claim 12, wherein the executable code is further effective to cause the one or more processors to: define on the subject vehicle a RADAR sensor location; simulate a RADAR sensor output according to the 3D model; and train the machine learning model using all of the images, the RADAR sensor output, and the two or more sensor outputs. 14. The system of claim 13, wherein the executable code is further effective to cause the one or more processors to: define on the subject vehicle a LIDAR sensor location; simulate a sequence of point clouds detected from the LIDAR sensor location of the 3D model; and train the machine learning model using all of the images, the RADAR sensor output, the sequence of point clouds, and the two or more sensor outputs. 15. The system of claim 11, wherein the executable code is further effective to cause the one or more processors to define the 3D model by defining velocities for the plurality of vehicles, the lane-splitting vehicle and the subject vehicle. 16. The system of claim 11, wherein the machine-learning model is a deep neural network. 17. The system of claim 11, wherein the executable code is further effective to cause the one or more processors to simulate the two or more sensor outputs from the sound of the plurality of vehicles incident on the two or more sensor locations by simulating Doppler effects and propagation distances of the sound. 18. The system of claim 11, wherein the executable code is further effective to cause the one or more processors to model the lane-splitting vehicle as a lane-splitting motorcycle emitting engine noises characteristic of a motorcycle. 19. The system of claim 11, further comprising a vehicle including a vehicle controller and two or more microphones; wherein the vehicle controller is programmed with the machine-learning model; wherein the vehicle controller is further programmed to: receive two or more audio streams from the two or more microphones; input the two or more audio streams to the machine-learning model; if the machine-learning model indicates that the two or more audio streams currently indicate a lane-splitting vehicle is present, at least one of output an alert by the vehicle controller and refrain from moving, by the vehicle controller, the vehicle into an inter-lane region. 20. The method of claim 9, further comprising: if the machine-learning model indicates that the two or more audio streams currently indicate a lane-splitting vehicle is present, at least one of refrain from outputting the alert by the vehicle controller and permit, by the vehicle controller, movement into the inter-lane region.