Abstract: Embodiments of the disclosure provide systems and methods for ballistically estimating vehicle data. The system may include a communication interface configured to receive a first vehicle measurement taken at a first time point and a second vehicle measurement taken at a second time point. The system may further include at least one processor. The at least one processor may be configured to estimate a first version of vehicle data at a first speed for each of the second time point and a plurality of intermediate time points between the first time point and the second time point based on the first vehicle measurement using a prediction model. The at least one processor may be further configured to compute a second version of vehicle data at a second speed for the second time point based on the second vehicle measurement. The first speed is faster than the second speed.

Abstract: Systems and methods are provided for probabilistic navigation planning. An exemplary probabilistic navigation method may comprise: obtaining a map of an environment comprising one or more first objects each associated with a probability model; obtaining one or more global factors and local factors to update the probability models, wherein the global factors apply to all of the first objects and the local factors apply to a portion of the first objects; and determining a navigation for a second object through at least a part of the environment based at least on minimizing a total collision probability with the first objects along the navigation according to the updated probability models.

Abstract: Embodiments of the disclosure provide a camera system and a sensing method using such a camera system. The camera system includes a plurality of distributed cameras, collectively keeping a predetermined image space relative to the camera system in focus. The plurality of cameras are configured to capture image data of at least one object located in the predetermined image space. The camera system further includes a controller configured to determine position information of the at least one object relative to the camera system based on the image data.

Abstract: Systems and methods are provided for probabilistic navigation planning. An exemplary probabilistic navigation method may comprise: obtaining a map of an environment comprising one or more first objects each associated with a probability model; obtaining one or more global factors and local factors to update the probability models, wherein the global factors apply to all of the first objects and the local factors apply to a portion of the first objects; and determining a navigation for a second object through at least a part of the environment based at least on minimizing a total collision probability with the first objects along the navigation according to the updated probability models.

Abstract: Systems and methods are provided for forming a vehicle train. An exemplary method may comprise: detecting, by a first vehicle, a second vehicle traveling in the same direction as the first vehicle on a road; receiving travel information of the second vehicle; determining, by the first vehicle, a lead vehicle of the vehicle train between the first vehicle and the second vehicle based on travel information of the first vehicle and the travel information of the second vehicle; and connecting the first vehicle and the second vehicle to form the vehicle train.

Abstract: Systems and methods are provided for probabilistic navigation planning. An exemplary probabilistic navigation method may comprise: obtaining a map of an environment comprising one or more first objects each associated with a probability model; obtaining one or more global factors and local factors to update the probability models, wherein the global factors apply to all of the first objects and the local factors apply to a portion of the first objects; and determining a navigation for a second object through at least a part of the environment based at least on minimizing a total collision probability with the first objects along the navigation according to the updated probability models.

Abstract: Embodiments of the disclosure provide systems and methods for determining depth information in a two-dimensional (2D) image. An exemplary system may include a processor and a non-transitory memory storing instructions that, when executed by the processor, cause the system to perform the various operations. The operations may include receiving a first feature map based on the 2D image and applying an extraction network having a convolution operation and a pooling operation to the first feature map to obtain a second feature map. The operations may also include applying a reconstruction network having a deconvolution operation to the second feature map to obtain a depth map.

Abstract: Embodiments of the disclosure provide systems and methods for ballistically estimating vehicle data. The system may include a communication interface configured to receive a first vehicle measurement taken at a first time point and a second vehicle measurement taken at a second time point. The system may further include at least one processor. The at least one processor may be configured to estimate a first version of vehicle data at a first speed for each of the second time point and a plurality of intermediate time points between the first time point and the second time point based on the first vehicle measurement using a prediction model. The at least one processor may be further configured to compute a second version of vehicle data at a second speed for the second time point based on the second vehicle measurement. The first speed is faster than the second speed.

Abstract: Embodiments of the disclosure provide systems and methods for ballistically estimating vehicle data. The system may include a communication interface configured to receive a first vehicle measurement taken at a first time point and a second vehicle measurement taken at a second time point. The system may further include at least one processor. The at least one processor may be configured to compute a first set of vehicle data based on the first vehicle measurement, and estimate a second set of vehicle data for the second time point based on the first set of vehicle data and a model. The at least one processor may be further configured to compute a third set of vehicle data based on the second vehicle measurement. The at least one processor may also be configured to update the model based on a comparison between the second set of vehicle data and the third set of vehicle data.

Abstract: Embodiments of the disclosure provide a camera system and a sensing method using such a camera system. The camera system includes a plurality of distributed cameras, collectively keeping a predetermined image space relative to the camera system in focus. The plurality of cameras are configured to capture image data of at least one object located in the predetermined image space. The camera system further includes a controller configured to determine position information of the at least one object relative to the camera system based on the image data.

Abstract: Embodiments of the disclosure provide systems and methods for determining depth information in a two-dimensional (2D) image. An exemplary system may include a processor and a non-transitory memory storing instructions that, when executed by the processor, cause the system to perform the various operations. The operations may include receiving a first feature map based on the 2D image and applying an extraction network having a convolution operation and a pooling operation to the first feature map to obtain a second feature map. The operations may also include applying a reconstruction network having a deconvolution operation to the second feature map to obtain a depth map.

Abstract: Embodiments of the disclosure provide a camera system for a vehicle and a sensing method using such a camera system. The camera system includes a plurality of cameras each configured with a different camera setting. The cameras collectively keep a predetermined image space in focus. The predetermined image space includes at least one object. The camera system further includes a controller. The controller is configured to receive image data captured by the plurality of cameras of the predetermined image space, and determine depth information of the at least one object based on the image data.

Abstract: Systems and methods are provided for forming a vehicle train. An exemplary method may comprise: detecting, by a first vehicle, a second vehicle traveling in the same direction as the first vehicle on a road; receiving travel information of the second vehicle; determining, by the first vehicle, a lead vehicle of the vehicle train between the first vehicle and the second vehicle based on travel information of the first vehicle and the travel information of the second vehicle; and connecting the first vehicle and the second vehicle to form the vehicle train.

Abstract: Embodiments of the disclosure provide a camera system for a vehicle and a sensing method using such a camera system. The camera system includes a plurality of cameras each configured with a different camera setting. The cameras collectively keep a predetermined image space in focus. The predetermined image space includes at least one object. The camera system further includes a controller. The controller is configured to receive image data captured by the plurality of cameras of the predetermined image space, and determine depth information of the at least one object based on the image data.

Abstract: Embodiments of the disclosure provide systems and methods for ballistically estimating vehicle data. The system may include a communication interface configured to receive a first vehicle measurement taken at a first time point and a second vehicle measurement taken at a second time point. The system may further include at least one processor. The at least one processor may be configured to compute a first set of vehicle data based on the first vehicle measurement, and estimate a second set of vehicle data for the second time point based on the first set of vehicle data and a model. The at least one processor may be further configured to compute a third set of vehicle data based on the second vehicle measurement. The at least one processor may also be configured to update the model based on a comparison between the second set of vehicle data and the third set of vehicle data.

Abstract: Systems and methods are provided for probabilistic navigation planning. An exemplary probabilistic navigation method may comprise: obtaining a map of an environment comprising one or more first objects each associated with a probability model; obtaining one or more global factors and local factors to update the probability models, wherein the global factors apply to all of the first objects and the local factors apply to a portion of the first objects; and determining a navigation for a second object through at least a part of the environment based at least on minimizing a total collision probability with the first objects along the navigation according to the updated probability models.

Abstract: Systems and methods are provided for probabilistic navigation planning. An exemplary probabilistic navigation method may comprise: obtaining a map of an environment comprising one or more first objects each associated with a probability model; obtaining one or more global factors and local factors to update the probability models, wherein the global factors apply to all of the first objects and the local factors apply to a portion of the first objects; and determining a navigation for a second object through at least a part of the environment based at least on minimizing a total collision probability with the first objects along the navigation according to the updated probability models.

Abstract: Systems and methods are provided for probabilistic navigation planning. An exemplary probabilistic navigation method may comprise: obtaining a map of an environment comprising one or more first objects each associated with a probability model; obtaining one or more global factors and local factors to update the probability models, wherein the global factors apply to all of the first objects and the local factors apply to a portion of the first objects; and determining a navigation for a second object through at least a part of the environment based at least on minimizing a total collision probability with the first objects along the navigation according to the updated probability models.