Abstract: Described herein are systems and methods for determining if a vehicle is parked. In one example, a system includes a processor, a sensor system, and a memory. Both the sensor system and the memory are in communication with the processor. The memory includes a parking determination module having instructions that, when executed by the processor, cause the processor to determine, using a random forest model, when the vehicle is parked based on vehicle estimated features, vehicle learned features, and vehicle taillight features of the vehicle that are based on sensor data from the sensor system.

Abstract: A traffic light classification system for a vehicle includes an image capture device to capture an image of a scene that includes a traffic light with multiple light signals, a processor, and a memory communicably coupled to the processor and storing a first neural network module including instructions that when executed by the processor cause the processor to determine, based on inputting the image into a neural network, a semantic keypoint for each light signal in the traffic light, and determine, based on each semantic keypoint, a classification state of each light signal.

Abstract: Described herein are systems and methods for determining if a vehicle is parked. In one example, a system includes a processor, a sensor system, and a memory. Both the sensor system and the memory are in communication with the processor. The memory includes a parking determination module having instructions that, when executed by the processor, cause the processor to determine, using a random forest model, when the vehicle is parked based on vehicle estimated features, vehicle learned features, and vehicle taillight features of the vehicle that are based on sensor data from the sensor system.

Abstract: In one embodiment, a vehicle light classification system captures a sequence of images of a scene that includes a front/rear view of a vehicle with front/rear-side lights, determines semantic keypoints, in the images and associated with the front/rear-side lights, based on inputting the images into a first neural network, obtains multiple difference images that are each a difference between successive images from among the sequence of images, the successive images being aligned based on their respective semantic keypoints, and determines a classification of the front/rear-side lights based at least in part on the difference images by inputting the difference images into a second neural network.

Abstract: A bird's eye view feature map, augmented with semantic information, can be used to detect an object in an environment. A point cloud data set augmented with the semantic information that is associated with identities of classes of objects can be obtained. Features can be extracted from the point cloud data set. Based on the features, an initial bird's eye view feature map can be produced. Because operations performed on the point cloud data set to extract the features or to produce the initial bird's eye view feature map can have an effect of diminishing an ability to distinguish the semantic information in the initial bird's eye view feature map, the initial bird's eye view feature map can be augmented with the semantic information to produce an augmented bird's eye view feature map. Based on the augmented bird's eye view feature map, the object in the environment can be detected.

Abstract: A bird's eye view feature map, augmented with semantic information, can be used to detect an object in an environment. A point cloud data set augmented with the semantic information that is associated with identities of classes of objects can be obtained. Features can be extracted from the point cloud data set. Based on the features, an initial bird's eye view feature map can be produced. Because operations performed on the point cloud data set to extract the features or to produce the initial bird's eye view feature map can have an effect of diminishing an ability to distinguish the semantic information in the initial bird's eye view feature map, the initial bird's eye view feature map can be augmented with the semantic information to produce an augmented bird's eye view feature map. Based on the augmented bird's eye view feature map, the object in the environment can be detected.

Abstract: In one embodiment, a traffic light classification system for a vehicle, includes an image capture device to capture an image of a scene that includes a traffic light with multiple light signals, a processor, and a memory communicably coupled to the processor and storing a first neural network module including instructions that when executed by the processor cause the processor to determine, based on inputting the image into a neural network, a semantic keypoint for each light signal in the traffic light, and determine, based on each semantic keypoint, a classification state of each light signal.