Training An Automatic Traffic Light Detection Model Using Simulated Images
A scenario is defined that including models of vehicles and a typical driving environment as well as a traffic light having a state (red, green, amber). A model of a subject vehicle is added to the scenario and camera location is defined on the subject vehicle. Perception of the scenario by a camera is simulated to obtain an image. The image is annotated with a location and state of the traffic light. Various annotated images may be generated for difference scenarios, including scenarios lacking a traffic light or having traffic lights that do not govern the subject vehicle. A machine learning model is then trained using the annotated images to identify the location and state of traffic lights that govern the subject vehicle.
Much More than Average Length Specification
- Claim CLM-00001. 1. A method comprising, by a computer system:
simulating perception of a 3D model having a traffic light model as a light source to obtain an image; annotating the image with a location and state of the traffic light model to obtain an annotated image; and training a model according to the annotated image.
- Claim CLM-00011. 11. A system comprising one or more processing devices and one or more memory devices operably coupled to the one or more processing devices, the one or more processing devices storing executable code effective to cause the one or more processing devices to:
simulate perception of a 3D model having a traffic light model as a light source to obtain an image; annotate the image with a location and state of the traffic light model to obtain an annotated image; and train a model according to the annotated image.
