Abstract: A navigation system for a host vehicle may generate an image input based on at least one image captured by the camera onboard the host vehicle; provide the image input to a trained model configured to generate, based on the image input, an output identifying a left side boundary of a lane of travel for the host vehicle relative to a road surface and a right side boundary of the lane of travel for the host vehicle relative to the road surface; determine, based on the output generated by the trained model, a first location information for the left side boundary and a second location information for the right side boundary; determine at least one navigational action for the host vehicle based on at least one of the first location information or the second location information; and cause the host vehicle to implement the at least one navigational action.

Abstract: Systems and methods are disclosed for mapping lanes for use in vehicle navigation. In one implementation, at least one processing device may be programmed to receive navigational information from a first vehicle and a second vehicle that have navigated along a road segment including a lane split feature; receive at least one image associated with the road segment; determine, from the first navigational information, a first actual trajectory of the first vehicle and a second actual trajectory of the second vehicle; determine a divergence between the first actual trajectory and the second actual trajectory; determine, based on analysis of the at least one image, that the divergence between the first actual trajectory and the second actual trajectory is indicative of the lane split feature; and update a vehicle road navigation model to include a first target trajectory and a second target trajectory that branches from the first target trajectory after the lane split feature.

Abstract: Systems and methods for identifying lane marks in an environment of a host vehicle are disclosed. In one implementation, a system includes a processor configured to receive an image acquired by a camera onboard the host vehicle and analyze the image using a trained model. The trained model is configured to identify a plurality of candidate regions, each of the plurality of candidate regions corresponding to a representation of an object in the image; determine a plurality of scores, each of the plurality of scores indicating a degree to which a corresponding one of the plurality of candidate regions corresponds to a representation of a lane marking in the image; analyze the plurality of scores to select one of the plurality of candidate regions; and output an indicator of the selected candidate region.

Abstract: Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

Abstract: Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

Abstract: Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

Abstract: Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

Abstract: Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

Abstract: Systems and methods are disclosed for mapping lanes for use in vehicle navigation. In one implementation, at least one processing device may be programmed to receive navigational information from a first vehicle and a second vehicle that have navigated along a road segment including a lane split feature; receive at least one image associated with the road segment; determine, from the first navigational information, a first actual trajectory of the first vehicle and a second actual trajectory of the second vehicle; determine a divergence between the first actual trajectory and the second actual trajectory; determine, based on analysis of the at least one image, that the divergence between the first actual trajectory and the second actual trajectory is indicative of the lane split feature; and update a vehicle road navigation model to include a first target trajectory and a second target trajectory that branches from the first target trajectory after the lane split feature.

Abstract: Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

Abstract: Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

Abstract: Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.