Abstract: An autonomous system may selectively displace human driver control of a host vehicle. The system may receive an image representative of an environment of the host vehicle and detect an obstacle in the environment of the host vehicle based on analysis of the image. The system may monitor a driver input to a throttle, brake, and/or steering control associated with the host vehicle. The system may determine whether the driver input would result in the host vehicle navigating within a proximity buffer relative to the obstacle. If the driver input would not result in the host vehicle navigating within the proximity buffer, the system may allow the driver input to cause a corresponding change in one or more host vehicle motion control systems. If the driver input would result in the host vehicle navigating within the proximity buffer, the system may prevent the driver input from causing the corresponding change.

Abstract: Systems and methods are disclosed for navigating a host vehicle. In one implementation, at least one processing device may be programmed to receive an image representative of an environment of the host vehicle, determine a planned navigational action for the host vehicle, analyze the image to identify a target vehicle with a direction of travel toward the host vehicle, and determine a next-state distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken.

Abstract: Systems and methods are provided for navigating a host vehicle. In one implementation, a system may include a processing device configured to receive an image acquired by an image capture device; determine a planned navigational action for accomplishing a navigational goal of the host vehicle; analyze the at least one image to identify a first target vehicle ahead of the host vehicle and a second target vehicle ahead of the first target vehicle; determine a next-state distance between the host vehicle and the second target vehicle that would result if the planned navigational action was taken; determine a stopping distance for the host vehicle based on a maximum braking capability of the host vehicle and a current speed of the host vehicle; and cause the vehicle to implement the planned navigational action if the stopping distance is less than the determined next-state distance.

Abstract: The presently disclosed embodiments may include a computer readable medium including instructions that when executed by one or more processing devices cause the one or more processing devices to perform a method. The method may include receiving a user input, wherein the user input includes a natural language question, using a trained language model to decompose the natural language question into two or more information requests, routing each of the two or more information requests to at least one information resource, receiving two or more information responses from the at least one information resource, wherein the two or more information responses correspond to the two or more information requests, generating a natural language response to the user input based on the two or more information responses and providing the natural language response to the user.

Abstract: For example, a Machine-Learning (ML) model training system may be configured to shuffle a plurality of input examples in plurality of input blocks to provide a plurality of first-shuffled examples in a plurality of shuffled blocks; and to provide the plurality of first-shuffled examples in the plurality of shuffled blocks as an input to a model training procedure to train an ML model. For example, the model training procedure may include a plurality of epoch iterations applied to a plurality of block groups. For example, an epoch iteration may include determining a block group for the epoch iteration by randomly selecting a group of shuffled blocks from the plurality of shuffled blocks; shuffling first-shuffled examples in the block group to provide a plurality of second-shuffled examples; and updating the ML model according to a plurality of update iterations applied to the plurality of second-shuffled examples.

Abstract: Systems and methods are provided for vehicle navigation. In one implementation, at least one processor may receive, from a camera of a vehicle, at least one image captured from an environment of the vehicle. The processor may analyze the at least one image to identify a road topology feature in the environment of the vehicle represented in the at least one image and at least one point associated with the at least one image. Based on the identified road topology feature, the processor may determine an estimated path in the environment of the vehicle associated with the at least one point. The processor may further cause the vehicle to implement a navigational action based on the estimated path.

Abstract: A navigational system for a host vehicle may comprise at least one processor. The processor may be programmed to receive an image representative of an environment of the host vehicle; analyze the image to identify a navigational state associated with the host vehicle; and determine, based on the navigational state, a navigational action for the host vehicle based on a policy that maps possible navigational actions to sensed states. The navigational action may be based on a safety constraint applicable to the navigational state, the safety constraint including a safety distance constraint associated with the host vehicle, wherein the safety distance constraint is based on a determined speed of the host vehicle and a determined speed of a detected target object. The processor may cause an adjustment of a navigational actuator of the host vehicle to implement the determined navigational action.

Abstract: The present disclosure relates to navigational systems for vehicles. In one implementation, such a navigational system may a first output from a first sensor and a second output from a mapping system; identify a target object in the first output; and determine, based on the first output, a detected driving condition associated with the target object and whether the condition triggers a navigational constraint. If the navigational constraint is triggered, the system may cause a first navigational adjustment. If the navigational constraint is not triggered, the system may determine whether a representation of the target object is included in the second output. If the representation of the target object is included in the second output, the system may cause a second navigational adjustment. If the representation of the target object is not included in the second output, the system may forego any navigational adjustments.

Abstract: Systems and methods are provided for navigating based on behaviors of following vehicles. In one implementation, a navigation system for a host vehicle may include at least one processing device. The at least one processing device may be programmed to receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify a first target vehicle and a second target vehicle in the environment of the host vehicle; analyze the plurality of images to determine a distance between the first target vehicle and the second target vehicle; and determine, from the plurality of images, a time to reach a boundary associated with one of the first target vehicle and the second target vehicle, wherein the determined time exceeds a first threshold and does not exceed a second threshold.

Abstract: Systems and methods generate an extended trained model. In one implementation, a method includes obtaining a preexisting trained model, the preexisting trained model including a plurality of preexisting weights, wherein each of the plurality of preexisting weights is associated with a preexisting value; identifying a subset of the plurality of preexisting weights; generating a plurality of extended weights based on a training process using duplicates of the subset of the plurality of preexisting weights; and generating the extended trained model, wherein the extended trained model includes the plurality of preexisting weights and the plurality of extended weights.

Abstract: Systems and methods are provided for navigating a host vehicle. A processing device may be programmed to receive an image representative of an environment of the host vehicle; determine a planned navigational action for the host vehicle; analyze the image to identify a target vehicle travelling toward the host vehicle; determine a next-state distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken; determine a stopping distance for the host vehicle based on a braking rate, a maximum acceleration capability, and a current speed of the host vehicle; determine a stopping distance for the target vehicle based on a braking rate, a maximum acceleration capability, and a current speed of the target vehicle; and implement the planned navigational action if the determined next-state distance is greater than a sum of the stopping distances for the host vehicle and the target vehicle.

Abstract: Systems and methods are provided for navigating a host vehicle. At least one processing device may be programmed to receive an image of an environment of the host vehicle; detect, based on analysis of the image, a pedestrian crosswalk in the image; detect a presence of a traffic light and determine whether the traffic light is relevant to the host vehicle and the pedestrian crosswalk; determine a state of the traffic light; determine, when a pedestrian appears in the image, a proximity of the pedestrian relative to the pedestrian crosswalk; determine a planned navigational action for navigating the host vehicle relative to the pedestrian crosswalk based on a driving policy, the state of the traffic light and the proximity of the pedestrian relative to the pedestrian crosswalk; and cause one or more actuator systems of the host vehicle to implement the planned navigational action.

Abstract: Systems and methods are provided for vehicle navigation. In one implementation, at least one processor may receive, from a camera of a vehicle, at least one image captured from an environment of the vehicle. The processor may analyze the at least one image to identify a road topology feature in the environment of the vehicle represented in the at least one image and at least one point associated with the at least one image. Based on the identified road topology feature, the processor may determine an estimated path in the environment of the vehicle associated with the at least one point. The processor may further cause the vehicle to implement a navigational action based on the estimated path.

Abstract: Systems and methods are provided for navigating a host vehicle. A processing device may be programmed to receive an image representative of an environment of the host vehicle; determine a planned navigational action for the host vehicle; analyze the image to identify a target vehicle travelling toward the host vehicle; determine a next-state distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken; determine a stopping distance for the host vehicle based on a braking profile, a maximum acceleration capability, and a current speed of the host vehicle; determine a stopping distance for the target vehicle based on a braking profile and a current speed of the target vehicle; and implement the planned navigational action if the determined next-state distance is greater than a sum of the stopping distances for the host vehicle and the target vehicle.

Abstract: Systems and methods are provided for navigating a host vehicle. A navigation system for the host vehicle may include at least one processor programmed to receive images representative of an environment of the host vehicle; analyze at least one of the images to identify navigational state information associated with the host vehicle; determine a plurality of first potential navigational actions for the host vehicle based on the navigational state information; determine respective future states for the plurality of first potential navigational actions; determine a plurality of second potential navigational actions for the host vehicle based on the determined respective future states; select, based on the plurality of second potential navigational actions, one of the plurality of first potential navigational actions; and cause an adjustment of a navigational actuator of the host vehicle to implement the selected one of the plurality of first potential navigational actions.

Abstract: Systems and methods are provided for navigating a host vehicle. At least one processing device may be programmed to receive an image representative of an environment of the host vehicle; determine a planned navigational action for the host vehicle; analyze the image to identify a target vehicle in the environment of the host vehicle; determine a next-state lateral distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken; determine a lateral braking distance for the host vehicle and the target vehicle based on a maximum yaw rate capability, a maximum change in turn radius capability, and a current lateral speed of the host vehicle and the target vehicle; and implement the planned navigational action if the determined next-state distance is greater than a sum of the lateral braking distances for the host vehicle and the target vehicle.

Abstract: The present disclosure relates to navigational systems for vehicles. In one implementation, such a navigational system may a first output from a first sensor and a second output from a second sensor; identify a target object in the first output; and determine, based on the first output, a detected driving condition associated with the target object and whether the condition triggers a navigational constraint. If the navigational constraint is triggered, the system may cause a first navigational adjustment. If the navigational constraint is not triggered, the system may determine whether a representation of the target object is included in the second output. If the representation of the target object is included in the second output, the system may cause a second navigational adjustment. If the representation of the target object is not included in the second output, the system may forego any navigational adjustments.

Abstract: Systems and methods are provided for navigating a host vehicle. In one implementation, a system may include a processing device configured to receive an image acquired by an image capture device; determine a planned navigational action for accomplishing a navigational goal of the host vehicle; analyze the at least one image to identify a first target vehicle ahead of the host vehicle and a second target vehicle ahead of the first target vehicle; determine a next-state distance between the host vehicle and the second target vehicle that would result if the planned navigational action was taken; determine a stopping distance for the host vehicle based on a maximum braking capability of the host vehicle and a current speed of the host vehicle; and cause the vehicle to implement the planned navigational action if the stopping distance is less than the determined next-state distance.

Abstract: Systems and methods are provided for vehicle navigation. In one implementation, a system may comprise an interface to obtain sensing data of an environment of the host vehicle. A processing device may be configured to determine a planned navigational action for the host vehicle; identify a target vehicle in the environment of the host vehicle; predict a resulting distance between the host and target vehicles if the planned action were taken; determine a host vehicle stopping distance based on a braking rate, maximum acceleration capability, and current speed of the host vehicle; determine a target vehicle stopping distance based on a braking rate and current speed of the target vehicle; and continue with the planned navigational action while the predicted distance is greater than a minimum safe longitudinal distance calculated based on the host vehicle stopping distance and the target vehicle stopping distance.

Abstract: A system for a host vehicle includes a processor programmed to receive, from an image capture device, an image representative of an environment of the host vehicle, detect at least one obstacle in the environment of the host vehicle based on an analysis of the at least one image, determine a velocity of the host vehicle and a predicted path for the host vehicle, monitor a driver input to at least one of a throttle control, a brake control, or a steering control associated with the host vehicle, and determine whether the driver input would result in the host vehicle navigating within a proximity buffer relative to the at least one obstacle, wherein the proximity buffer is determined based on the determined velocity, a maximum acceleration capacity of the host vehicle, and a maximum braking capacity of the host vehicle, and a reaction time associated with the host vehicle.