Abstract: The disclosure includes a method that receives a real-time image of a road from a camera sensor communicatively coupled to an onboard computer of a vehicle. The method includes dividing the real-time image into superpixels. The method includes merging the superpixels to form superpixel regions. The method includes generating prior maps from a dataset of road scene images. The method includes drawing a set of bounding boxes where each bounding box surrounds one of the superpixel regions. The method includes comparing the bounding boxes in the set of bounding boxes to a road prior map to identify a road region in the real-time image. The method includes pruning bounding boxes from the set of bounding boxes to reduce the set to remaining bounding boxes. The method may include using a categorization module that identifies the presence of a road scene object in the remaining bounding boxes.

Abstract: The disclosure includes implementations for executing one or more computations for a vehicle. Some implementations of a method for a vehicle may include identifying one or more computations as being un-executable by any processor-based computing device of the vehicle. The method may include generating a query including query data describing the one or more computations to be executed for the vehicle. The method may include providing the query to a network. The method may include receiving a response from the network. The response may include solution data describing a result of executing the one or more computations. The response may be provided to the network by a processor-based computing device included in a hierarchy of processor-based computing devices that have greater computational ability than any processor-based computing devices of the vehicle.

Abstract: The disclosure includes implementations for executing one or more computations for a vehicle. Some implementations of a method for a vehicle may include identifying one or more computations as being un-executable by any processor-based computing device of the vehicle. The method may include generating a query including query data describing the one or more computations to be executed for the vehicle. The method may include providing the query to a network. The method may include receiving a response from the network. The response may include solution data describing a result of executing the one or more computations. The response may be provided to the network by a processor-based computing device included in a hierarchy of processor-based computing devices that have greater computational ability than any processor-based computing devices of the vehicle.

Abstract: The disclosure includes implementations for executing one or more computations for a vehicle. Some implementations of a method for a vehicle may include identifying one or more computations as being un-executable by any processor-based computing device of the vehicle. The method may include generating a query including query data describing the one or more computations to be executed for the vehicle. The method may include providing the query to a network. The method may include receiving a response from the network. The response may include solution data describing a result of executing the one or more computations. The response may be provided to the network by a processor-based computing device included in a hierarchy of processor-based computing devices that have greater computational ability than any processor-based computing devices of the vehicle.

Abstract: The disclosure includes a method that receives a real-time image of a road from a camera sensor communicatively coupled to an onboard computer of a vehicle. The method includes dividing the real-time image into superpixels. The method includes merging the superpixels to form superpixel regions. The method includes generating prior maps from a dataset of road scene images. The method includes drawing a set of bounding boxes where each bounding box surrounds one of the superpixel regions. The method includes comparing the bounding boxes in the set of bounding boxes to a road prior map to identify a road region in the real-time image. The method includes pruning bounding boxes from the set of bounding boxes to reduce the set to remaining bounding boxes. The method may include using a categorization module that identifies the presence of a road scene object in the remaining bounding boxes.

Abstract: The disclosure includes a method that receives a real-time image of a road from a camera sensor communicatively coupled to an onboard computer of a vehicle. The method includes dividing the real-time image into superpixels. The method includes merging the superpixels to form superpixel regions. The method includes generating prior maps from a dataset of road scene images. The method includes drawing a set of bounding boxes where each bounding box surrounds one of the superpixel regions. The method includes comparing the bounding boxes in the set of bounding boxes to a road prior map to identify a road region in the real-time image. The method includes pruning bounding boxes from the set of bounding boxes to reduce the set to remaining bounding boxes. The method may include using a categorization module that identifies the presence of a road scene object in the remaining bounding boxes.

Abstract: The disclosure includes implementations for executing one or more computations for a vehicle. Some implementations of a method for a vehicle may include identifying one or more computations as being un-executable by any processor-based computing device of the vehicle. The method may include generating a query including query data describing the one or more computations to be executed for the vehicle. The method may include providing the query to a network. The method may include receiving a response from the network. The response may include solution data describing a result of executing the one or more computations. The response may be provided to the network by a processor-based computing device included in a hierarchy of processor-based computing devices that have greater computational ability than any processor-based computing devices of the vehicle.

Abstract: The disclosure includes a system and method for determine a real time driving difficulty category. The method may include determining image feature vector data based on one or more features depicted in a real time image of a road scene. The image feature vector data may describe an image feature vector for an edited version of the real time image. The method may include determining offline road map data for the road scene, which includes a static label for a road included in the road scene and offline road information describing a regulatory speed limit for the road. The method may include selecting, based on the static label, a classifier for analyzing the image feature vector. The method may include executing the selected classifier to determine a real time driving difficulty category describing the difficulty for a user of the client device to drive in the road scene.

Abstract: The disclosure includes a method that receives a real-time image of a road from a camera sensor communicatively coupled to an onboard computer of a vehicle. The method includes dividing the real-time image into superpixels. The method includes merging the superpixels to form superpixel regions. The method includes generating prior maps from a dataset of road scene images. The method includes drawing a set of bounding boxes where each bounding box surrounds one of the superpixel regions. The method includes comparing the bounding boxes in the set of bounding boxes to a road prior map to identify a road region in the real-time image. The method includes pruning bounding boxes from the set of bounding boxes to reduce the set to remaining bounding boxes. The method may include using a categorization module that identifies the presence of a road scene object in the remaining bounding boxes.

Abstract: The disclosure includes a system and method for determine a real time driving difficulty category. The method may include determining image feature vector data based on one or more features depicted in a real time image of a road scene. The image feature vector data may describe an image feature vector for an edited version of the real time image. The method may include determining offline road map data for the road scene, which includes a static label for a road included in the road scene and offline road information describing a regulatory speed limit for the road. The method may include selecting, based on the static label, a classifier for analyzing the image feature vector. The method may include executing the selected classifier to determine a real time driving difficulty category describing the difficulty for a user of the client device to drive in the road scene.