Abstract: A method for performing vehicle taillight recognition is described. The method includes extracting spatial features from a sequence of images of a real-world traffic scene during operation of an ego vehicle. The method includes selectively focusing a convolutional neural network (CNN) of a CNN-long short-term memory (CNN-LSTM) framework on a selected region of the sequence of images according to a spatial attention model for a vehicle taillight recognition task. The method includes selecting, by an LSTM network of the CNN-LSTM framework, frames within the selected region of the sequence of images according to a temporal attention model for the vehicle taillight recognition task. The method includes inferring, according to the selected frames within the selected region of the sequence of images, an intent of an ado vehicle according to a taillight state. The method includes planning a trajectory of the ego vehicle from the intent inferred from the ado vehicle.

Abstract: A method for performing vehicle taillight recognition is described. The method includes extracting spatial features from a sequence of images of a real-world traffic scene during operation of an ego vehicle. The method includes selectively focusing a convolutional neural network (CNN) of a CNN-long short-term memory (CNN-LSTM) framework on a selected region of the sequence of images according to a spatial attention model for a vehicle taillight recognition task. The method includes selecting, by an LSTM network of the CNN-LSTM framework, frames within the selected region of the sequence of images according to a temporal attention model for the vehicle taillight recognition task. The method includes inferring, according to the selected frames within the selected region of the sequence of images, an intent of an ado vehicle according to a taillight state. The method includes planning a trajectory of the ego vehicle from the intent inferred from the ado vehicle.

Abstract: A method and apparatus for processing images, the method comprising: using a radar (4), generating a first image of an area of terrain (8); using a sensor (5), generating a second image of the area of terrain (8); performing an image segmentation process on the first image to identify a point in the first image as corresponding to a ground surface of the area of terrain (8); and projecting the identified point in the first image from the first image into the second image to identify a point in the second image as corresponding to the ground surface of the area of terrain (8). The method may further comprise: for the identified point in the second image, defining a sub-image of the second image containing that point; and performing a feature extraction process on the sub-image to identify points in the sub-image that correspond to the ground surface.