Abstract: Methods and systems for training a model include pre-training a backbone model with a pre-training decoder, using an unlabeled dataset with multiple distinct sensor data modalities that derive from different sensor types. The backbone model is fine-tuned with an output decoder after pre-training, using a labeled dataset with the multiple modalities.

Abstract: Methods and systems identifying road hazards include capturing an image of a road scene using a camera. The image is embedded using a segmentation model that includes an image branch having an image embedding layer that embeds images into a joint latent space and a text branch having a text embedding layer that embeds text into the joint latent space. A mask is generated for an object within the image using the segmentation model. A probability is determined that the object matches a road hazard using the segmentation mode. A signal is generated responsive to the probability to ameliorate a danger posed by the road hazard.

Abstract: Methods and systems for detecting faults include capturing an image of a scene using a camera. The image is embedded using a segmentation model that includes an image branch having an image embedding layer that embeds images into a joint latent space and a text branch having a text embedding layer that embeds text into the joint latent space. Semantic information is generated for a region of the image corresponding to a predetermined static object using the embedded image. A fault of the camera is identified based on a discrepancy between the semantic information and semantic information of the predetermined static image. The fault of the camera is corrected.

Abstract: Systems and methods is provided for road hazard analysis. The method includes obtaining sensor data of a road environment including a road and observable surroundings, and applying labels to the sensor data. The method further includes training a first neural network model to identify road hazards, training a second neural network model to identify faded lane markings, and training a third neural network model to identify overhanging trees and blocking foliage. The method further includes implementing the trained neural network models to detect road hazards in a real road setting.

Abstract: Systems and methods are provided for multi-modal test-time adaptation. The method includes inputting a digital image into a pre-trained Camera Intra-modal Pseudo-label Generator, and inputting a point cloud set into a pre-trained Lidar Intra-modal Pseudo-label Generator. The method further includes applying a fast 2-dimension (2D) model, and a slow 2D model, to the inputted digital image to apply pseudo-labels, and applying a fast 3-dimension (3D) model, and a slow 3D model, to the inputted point cloud set to apply pseudo-labels. The method further includes fusing pseudo-label predictions from the fast models and the slow models through an Inter-modal Pseudo-label Refinement module to obtain robust pseudo labels, and measuring a prediction consistency for the pseudo-labels.

Abstract: A computer-implemented method for rut detection is provided. The method includes detecting, by a rut detection system, areas in a road-scene image that include ruts with pixel-wise probability values, wherein a higher value indicates a better chance of being a rut. The method further includes performing at least one of rut repair and vehicle rut avoidance responsive to the pixel-wise probability values. The detecting step includes performing neural network-based, pixel-wise semantic segmentation with context information on the road-scene image to distinguish rut pixels from non-rut pixels on a road depicted in the road-scene image.

Abstract: Methods and systems for training a model include combining data from multiple datasets, the datasets having different respective label spaces. Relationships between labels in the different label spaces are identified. A unified neural network model is trained, using the combined data and the identified relationships to generate a unified model, with a class relational binary cross-entropy loss.