Abstract: Systems, methods, and other embodiments described herein relate to training a vehicle driver based on in-cabin or external light triggers. In one embodiment, a system includes a processor and a memory storing machine-readable instructions. The instructions, when executed by the processor, cause the processor to monitor a vehicle location along a roadway. A map of the roadway indicates a target location for a driving maneuver to be performed. When the vehicle location is within a threshold distance from a target location on the map, the instructions, when executed by the processor, cause the processor to control a vehicle light based on a vehicle light setting associated with the driving maneuver.

Abstract: A method for monitoring user purchase making activity is described. The method includes logging a potential user purchase and purchase communications corresponding to a potential option available for purchase by a user. The method also includes predicting whether loss aversion is a factor in a purchase making process of the potential option available for purchase by the user. The method further includes determining a purchase recommendation in response to identifying that the loss aversion is the factor in the potential purchase making process of the potential option available for purchase by the user. The method also includes displaying the purchase recommendation based on a use frequency of the option purchased by the user.

Abstract: Systems and Methods for controlling an autonomous vehicle, may include: receiving sensor data, the sensor data comprising vehicle parameter information for the autonomous vehicle; using the sensor data to determine a vehicle state for the autonomous vehicle, wherein the vehicle state comprises information regarding a magnitude of an actual or predicted effective understeer gradient for the vehicle; computing a yaw moment required to correct the effective understeer gradient based on the magnitude of the effective understeer gradient; and determining a combination of one or more vehicle control inputs, including applying a brake torque, to correct the effective understeer gradient; applying the brake torque to a single wheel of the vehicle, wherein an amount of brake torque applied is sufficient to lock up the single wheel to create a yaw moment on the vehicle to achieve the computed yaw moment required to correct the effective understeer gradient.

Abstract: Systems, methods, and other embodiments described herein relate to keeping a vehicle within a safety envelope. In one embodiment, a method includes determining a safety envelope having a boundary, determining a risk level for an environment surrounding the vehicle, and associating an index value with a location within the safety envelope based on at least one of the risk level and a relationship between the location and the boundary. The method includes, in response to a vehicle being located at the location, tilting a vehicle seat to a tilt angle relative to a floor of the vehicle. The tilt angle can be based on the index value associated with the location.

Abstract: A method for generating a dense light detection and ranging (LiDAR) representation by a vision system includes receiving, at a sparse depth network, one or more sparse representations of an environment. The method also includes generating a depth estimate of the environment depicted in an image captured by an image capturing sensor. The method further includes generating, via the sparse depth network, one or more sparse depth estimates based on receiving the one or more sparse representations. The method also includes fusing the depth estimate and the one or more sparse depth estimates to generate a dense depth estimate. The method further includes generating the dense LiDAR representation based on the dense depth estimate and controlling an action of the vehicle based on identifying a three-dimensional object in the dense LiDAR representation.

Abstract: Systems, methods, and other embodiments described herein relate to altering an image and propagating changes to other images of the same object using a diffusion model. In one embodiment, a method includes acquiring object images depicting an object. The method includes, responsive to altering one of the object images into an edited image, adapting the object images to reflect changes in the edited image by iteratively applying a diffusion model to the object images until satisfying a consistency threshold. The method includes providing the object images to represent an edited version of the object.

Abstract: Systems, methods, and other embodiments described herein relate to shape optimization using a diffusion model. In one embodiment, a method includes optimizing a parameter of a shape in an image based on a predetermined constraint using a diffusion model. The parameter is a pixel value for each pixel forming the shape.

Abstract: Systems, methods, and other embodiments described herein relate to generating vehicle design using a diffusion model. In one embodiment, a method includes generating an image based on a reference image and a text description of the reference image using a diffusion model. The method includes, based on whether the image meets a predetermined constraint, generating an intermediate image based on the image and the predetermined constraint using an analysis model such that the intermediate image is incrementally closer to meeting the predetermined constraint than the image, regenerating the image based on the intermediate image using the diffusion model, and outputting the image.

Abstract: Systems, methods, and other embodiments described herein relate to generating vehicle design using a diffusion model. In one embodiment, a method includes generating a plurality of images based on a textual artistic guidance and a numerical constraint using a diffusion model.

Abstract: Systems and methods described herein relate to estimating vehicle physical-design parameters from image data. In one embodiment, a system that estimates vehicle physical-design parameters receives one or more images representing a physical design of a vehicle. The system also processes the one or more images using a machine-learning-based model that includes a pre-trained feature extractor whose output layer has been replaced with a regression layer. The regression layer, after the regression layer has replaced the output layer, is trained to output an estimate of a physical-design parameter of the vehicle whose physical design is represented by the one or more images. The physical design of the vehicle is modified based, at least in part, on the estimate of the physical-design parameter.

Abstract: A vehicular warning system control system can include a processor and a memory in communication with the processor. The memory can include a warning system control module having instructions that, when executed by the processor, cause the processor to detect, using sensor data having information about a gaze of each eye of a driver of a vehicle, an abnormality of a gaze of the driver. The instructions further cause the processor to modify, using the sensor data, a signal emitted by the vehicle when the abnormality is detected.

Abstract: Datasets for autonomous driving systems and multi-modal scenes may be automatically labeled using previously trained models as priors to mitigate the limitations of conventional manual data labeling. Properly versioned models, including model weights and knowledge of the dataset on which the model was previously trained, may be used to run an inference operation on unlabeled data, thus automatically labeling the dataset. The newly labeled dataset may then be used to train new models, including sparse data sets, in a semi-supervised or weakly-supervised fashion.

Abstract: System, methods, and other embodiments described herein relate to emulating vehicle dynamics. In one embodiment, a method for emulating vehicle dynamics in a vehicle having a plurality of wheels and equipped with all-wheel steering, includes receiving emulation settings that indicate one or more environment parameters and/or vehicle parameters, detecting driver inputs including at least steering input and throttle input, executing a simulation model that receives the driver inputs and emulation settings, simulates the vehicle operating based on the driver inputs and the emulation settings, and outputs one or more simulated states of the vehicle based on the simulated operation of the vehicle, determining one or more actuation commands for each wheel of the vehicle to cause the vehicle to emulate the one or more simulated states, and executing the one or more actuation commands, wherein the actuation commands include at least wheel angle commands and torque commands.

Abstract: In accordance with one embodiment of the present disclosure, a method includes receiving an input image having an object and a background, intrinsically decomposing the object and the background into an input image data having a set of features, augmenting the input image data with a 2.5D differentiable renderer for each feature of the set of features to create a set of augmented images, and compiling the input image and the set of augmented images into a training data set for training a downstream task network.

Abstract: Systems and methods of using a common control scheme to autonomously controlling a vehicle during semi-autonomous and fully autonomous driving modes are provided. In particular, embodiments of the presently disclosed technology incorporate reference tracking for driving input and vehicle state into this common control scheme. In some embodiments, this common control scheme may be implemented using Model Predictive Control (MPC).

Abstract: A method for vehicle prediction, planning, and control is described. The method includes separately encoding traffic state information at an intersection into corresponding traffic state latent spaces. The method also includes aggregating the corresponding traffic state latent spaces to form a generalized traffic geometry latent space. The method further includes interpreting the generalized traffic geometry latent space to form a traffic flow map including current and future vehicle trajectories. The method also includes decoding the generalized traffic geometry latent space to predict a vehicle behavior according to the traffic flow map based on the current and future vehicle trajectories.

Abstract: A method for performing a task by a robotic device includes mapping a group of task image pixel descriptors associated with a first group of pixels in a task image of a task environment to a group of teaching image pixel descriptors associated with a second group of pixels in a teaching image based on positioning the robotic device within the task environment. The method also includes determining a relative transform between the task image and the teaching image based on mapping the plurality of task image pixel descriptors. The relative transform indicates a change in one or more of points of 3D space between the task image and the teaching image. The method also includes performing the task associated with the set of parameterized behaviors based on updating one or more parameters of a set of parameterized behaviors associated with the teaching image based on determining the relative transform.

Abstract: A cluster-supporting catalyst including porous carrier particles having acid sites, and catalyst metal clusters supported within the pores of the porous carrier particles. The catalyst metal clusters are obtained by supporting catalyst metal clusters having a positive charge, which is formed in a dispersion liquid containing a dispersion medium and the porous carrier particles dispersed in the dispersion medium, on the acid sites within the pores of the porous carrier particles through an electrostatic interaction.

Abstract: Systems, methods, and other embodiments described herein relate to estimating scaled depth maps by sampling variational representations of an image using a learning model. In one embodiment, a method includes encoding data embeddings by a learning model to form conditioned latent representations using attention networks, the data embeddings including features about an image from a camera and calibration information about the camera. The method also includes computing a probability distribution of the conditioned latent representations by factoring scale priors. The method also includes sampling the probability distribution to generate variations for the data embeddings. The method also includes estimating scaled depth maps of a scene from the variations at different coordinates using the attention networks.

Abstract: A method for fusing neural radiance fields (NeRFs) is described. The method includes re-rendering a first NeRF and a second NeRF at different viewpoints to form synthesized images from the first NeRF and the second NeRF. The method also includes inferring a transformation between a re-rendered first NeRF and a re-rendered second NeRF based on the synthesized images from the first NeRF and the second NeRF. The method further includes blending the re-rendered first NeRF and the re-rendered second NeRF based on the inferred transformation to fuse the first NeRF and the second NeRF.