Abstract: System, methods, and other embodiments described herein relate to associating disparate tracks from multiple sensor inputs for observed objects. In one embodiment, a method includes, in response to receiving a first input from a first sensor and a second input from a second sensor, generating the disparate tracks including first sensor tracks and second sensor tracks for the observed objects that correspond to the first input and the second input. The method includes identifying correlations between the first sensor tracks and the second sensor tracks by computing association likelihoods between the first tracks and the second tracks within a permutation matrix according to an objective cost function. The method includes controlling a vehicle according to the correlations.

Abstract: System, methods, and other embodiments described herein relate to locating an object within an image and projecting the object into a map. In one embodiment, a method includes, responsive to identifying a nearby vehicle in an image of an external environment surrounding a scanning vehicle, determining a relative location of the nearby vehicle in relation to the scanning vehicle using the image. The method includes projecting the nearby vehicle into a map according to at least the relative location determined from the image. The method includes controlling the scanning vehicle according to the map.

Abstract: Systems and method for generating photorealistic images include training a generative adversarial network (GAN) model by jointly learning a first generator, a first discriminator, and a set of predictors through an iterative process of optimizing a minimax objective. The first discriminator learns to determine a synthetic-to-real image from a real image. The first generator learns to generate the synthetic-to-real image from a synthetic image such that the first discriminator determines the synthetic-to-real image is real. The set of predictors learn to predict at least one of a semantic segmentation labeled data and a privileged information from the synthetic-to-real image based on at least one of a known semantic segmentation labeled data and a known privileged information corresponding to the synthetic image. Once trained, the GAN model may generate one or more photorealistic images using the trained GAN model.

Abstract: System, methods, and other embodiments described herein relate to identifying rear indicators of a nearby vehicle. In one embodiment, a method includes, in response to detecting a nearby vehicle, capturing signal images of a rear portion of the nearby vehicle. The method includes computing a braking state for brake lights of the nearby vehicle that indicates whether the brake lights are presently active by analyzing the signal images according to a brake classifier. The method includes computing a turn state for rear turn signals of the nearby vehicle that indicates which of the turn signals are presently active by analyzing regions of interest from the signal images according to a turn classifier. The brake classifier and the turn classifier are comprised of a convolutional neural network and a long short-term memory recurrent neural network (LSTM-RNN). The method includes providing electronic outputs identifying the braking state and the turn state.

Abstract: System, methods, and other embodiments described herein relate to locating an object within an image and projecting the object into a map. In one embodiment, a method includes, responsive to identifying a nearby vehicle in an image of an external environment surrounding a scanning vehicle, determining a relative location of the nearby vehicle in relation to the scanning vehicle using the image. The method includes projecting the nearby vehicle into a map according to at least the relative location determined from the image. The method includes controlling the scanning vehicle according to the map.

Abstract: System, methods, and other embodiments described herein relate to associating disparate tracks from multiple sensor inputs for observed objects. In one embodiment, a method includes, in response to receiving a first input from a first sensor and a second input from a second sensor, generating the disparate tracks including first sensor tracks and second sensor tracks for the observed objects that correspond to the first input and the second input. The method includes identifying correlations between the first sensor tracks and the second sensor tracks by computing association likelihoods between the first tracks and the second tracks within a permutation matrix according to an objective cost function. The method includes controlling a vehicle according to the correlations.

Abstract: System, methods, and other embodiments described herein relate to estimating an object from acquired data that is a partial observation of the object. In one embodiment, a method includes accessing, from a database, object data that is a three-dimensional representation of a known object. The method includes transforming the object data to produce partial data that is a partial representation of the known object with a relative fewer number of data points than the object data. The method includes training an observation model by using the partial data that is linked to the known object to represent relationships between the object data and the partial data that provide for estimating the known object from the obscured data of a partially observed object that is unknown.

Abstract: A protective cover assembly includes a cover unit and a support plate unit. The cover unit includes an elongated hollow cover-mounting seat, two protective covers disposed on said cover-mounting seat, a positioning mechanism, and a quick release mechanism. The positioning mechanism includes front and rear positioning members disposed fixedly in a receiving chamber in the cover-mounting seat, and an abutment member disposed in an upper end of the receiving chamber. The quick release mechanism includes a cam lever and a resilient member. The cam lever is pivotable relative to the cover-mounting seat between a locking position whereat a lever-engaging unit of the support plate unit is clamped between the abutment member and the cam lever, and a release position whereat the cam lever is removed from the lever-engaging unit.

Abstract: A micro-adjustable fence device includes a fence and a carriage. The carriage is mounted to the fence, and has a chassis body with a major wall surface that has a longitudinally extending riding route to guide the major wall surface to slidably ride on a guiding rail of a cutting machine. A wheel includes a hub rotatably mounted relative to the chassis body and defining a hub center, and a rim disposed to be shiftable betweena traction gaining position, where the rim frictionally contacts the guiding rail at a tangential point so as to roll thereon, and an idle position, where the rim is disengaged from the guiding rail. The tangential point is in line with an advancing path of the hub center. A biasing member is disposed to bias the hub to move towards the idle position.