Abstract: Provided is a system that includes a processor to provide a first input to an autoencoder machine learning model; generate a first output of the autoencoder machine learning model based on the first input; provide the first input to a production machine learning model; provide the first output of the autoencoder machine learning model as a second input to the production machine learning model; generate a first output of the production machine learning model based on the first input; generate a second output of the production machine learning model based on the second input; determine a metric of divergence between the first output and the second output of the production machine learning model, wherein the metric of divergence comprises an indication of whether the first input is associated with an adversarial attack; and perform an action. Methods and computer program products are also provided.

Abstract: Provided is a system for generating an inference based on real-time selection of a machine learning model using a machine learning model framework that includes at least one processor programmed or configured to receive a request for inference, wherein the request includes a payload, select a machine learning model of a plurality of machine learning models based on the request for inference, determine an aggregation of data based on the machine learning model and the payload of the request, transform the aggregation of data into inference data, wherein the inference data has a configuration that is capable of being processed by the machine learning model, and generate an inference based on the inference data using the machine learning model. Methods and computer program products are also provided.

Abstract: Provided is a system for generating an inference based on real-time selection of a machine learning model using a machine learning model framework that includes at least one processor programmed or configured to receive a request for inference, wherein the request includes a payload, select a machine learning model of a plurality of machine learning models based on the request for inference, determine an aggregation of data based on the machine learning model and the payload of the request, transform the aggregation of data into inference data, wherein the inference data has a configuration that is capable of being processed by the machine learning model, and generate an inference based on the inference data using the machine learning model. Methods and computer program products are also provided.

Abstract: Methods, systems, and computer program products are provided for implementing a model agnostic framework to provide Shapley values associated with a machine learning model. A method may include receiving an executable file for a neural network machine learning model, converting a format of the executable file for the neural network machine learning model to an agnostic model format to provide an agnostic model format file for the neural network machine learning model, parsing the agnostic model format file, to provide a forward symbolic graph associated with the neural network machine learning model and a backward symbolic graph associated with the neural network machine learning model, receiving a real-time inference request, and determining an output of the neural network machine learning model associated with the real-time inference request and one or more Shapley values associated with the output of the neural network machine learning model.

Abstract: Described are a system, method, and computer program product for secure edge computing of a machine learning model. The method includes transmitting, with a server, a first portion of a machine learning model to a computing device remote from the server. The first portion includes at least one first layer of the machine learning model configured to process a first input of data collected by the computing device and generate an output. The method also includes receiving, with the server from the computing device, encoded model data including the output. The method further includes decoding, with the server, the encoded model data to produce decoded model data, and generating, with the server, a classification based on the first input of data by executing a second portion of the machine learning model.

Abstract: Systems, methods, and computer program products are provided for dynamically processing model inference or training requests. A system may include at least one processor to receive a plurality of requests from a plurality of requesting systems, create a plurality of instantiations of at least one machine-learning model based on the plurality of requests and service data associated with each requesting system of the plurality of requesting systems, stream data associated with at least one request of the plurality of requests to each instantiation of the plurality of instantiations, adjust a rate limit for each instantiation of the plurality of instantiations based on the service data associated with at least one requesting system related to a respective instantiation, resulting in an adjusted rate limit, and process at least one request of the plurality of requests with an instantiation of the plurality of instantiations based on the adjusted rate limit.

Abstract: Embodiments of the present disclosure are directed to onboarding a model from a training platform to an inference platform and selecting parameters of the model to optimize performance of the model. For example, the onboarding of the model to the inference platform can be based on a series of interactions between a model onboarding systems at the training platform and at the inference platform. An optimization process can include a searching-based process to derive optimal settings for the model. The optimization process can simulate feature combinations of the model and identify an optimal combination of settings of the model for increased model performance.

Abstract: Provided is a system for generating an inference based on real-time selection of a machine learning model using a machine learning model framework that includes at least one processor programmed or configured to receive a request for inference, wherein the request includes a payload, select a machine learning model of a plurality of machine learning models based on the request for inference, determine an aggregation of data based on the machine learning model and the payload of the request, transform the aggregation of data into inference data, wherein the inference data has a configuration that is capable of being processed by the machine learning model, and generate an inference based on the inference data using the machine learning model. Methods and computer program products are also provided.

Abstract: A method of navigating an autonomous driving vehicle (ADV) includes determining a target function for an open space model based on one or more obstacles and map information within a proximity of the ADV, then iteratively performing first and second quadratic programming (QP) optimizations on the target function. Then, generating a second trajectory based on results of the first and second QP optimizations to control the ADV autonomously using the second trajectory. The first QP optimization is based on fixing a first set of variables of the target function. The second QP optimization is based on maximizing a sum of the distances from the ADV to each of the obstacles over a plurality of points of the first trajectory, and minimizing a difference between a target end-state of the ADV and a determined final state of the ADV using the first trajectory.

Abstract: Provided are systems for generating a machine learning model and a prediction based on encoded time series data using model reduction techniques that include a processor to receive a training dataset of a plurality of data instances, wherein each data instance includes a time series of data points, perform an encoding operation on the training dataset to provide an encoded dataset having a lower dimension space than a dimension space of the training dataset, generate one or more prediction models based on the encoded dataset, determine an output of the one or more prediction models in the lower dimension space based on an input provided to the one or more prediction models, and perform a decoding operation on the output to project the output from the lower dimension space to the dimension space of the training dataset. Methods and computer program products are also provided.

Abstract: In one embodiment, a computer-implemented method of operating an autonomous driving vehicle (ADV) includes perceiving a driving environment surrounding the ADV based on sensor data obtained from one or more sensors mounted on the ADV, determining a driving scenario, in response to a driving decision based on the driving environment, applying a predetermined machine-learning model to data representing the driving environment and the driving scenario to generate a set of one or more driving parameters, and planning a trajectory to navigate the ADV using the set of the driving parameters according to the driving scenario through the driving environment.

Abstract: In one embodiment, a method of training dynamic models for autonomous driving vehicles includes the operations of receiving a first set of training data from a training data source, the first set of training data representing driving statistics for a first set of features; training a dynamic model based on the first set of training data for the first set of features; determining a second set of features as a subset of the first set of features based on evaluating the dynamic model, each of the second set of features representing a feature whose performance score is below a predetermined threshold. The method further includes the following operations for each of the second set of features: retrieving a second set of training data associated with the corresponding feature of the second set of features, and retraining the dynamic model using the second set of training data.

Abstract: In one embodiment, a system and method for partitioning a region for point cloud registration of LIDAR poses of an autonomous driving vehicle (ADV) using a regional iterative closest point (ICP) algorithm is disclosed. The method determines the frame pair size of one or more pairs of related LIDAR poses of a region of an HD map to be constructed. If the frame pair size is greater than a threshold, the region is further divided into multiple clusters. The method may perform the ICP algorithm for each cluster. Inside a cluster, the ICP algorithm focuses on a partial subset of the decision variables and assumes the rest of the decision variables are fixed. To construct the HD map, the method may determine if the results of the ICP algorithms from the clusters converge. If the solutions converge, a solution to the point cloud registration for the region is found.

Abstract: In one embodiment, a system is disclosed for registration of point clouds for autonomous driving vehicles (ADV). The system receives a number of point clouds and corresponding poses from ADVs equipped with LIDAR sensors capturing point clouds of a navigable area to be mapped, where the point clouds correspond to a first coordinate system. The system partitions the point clouds and the corresponding poses into one or more loop partitions based on navigable loop information captured by the point clouds. For each of the loop partitions, the system applies an optimization model to point clouds corresponding to the loop partition to register the point clouds. They system merges the one or more loop partitions together using a pose graph algorithm, where the merged partitions of point clouds are utilized to perceive a driving environment surrounding the ADV.

Abstract: A machine learning system includes a training platform and an inference platform, where the inference platform is coupled to receive the output of the training platform. Based upon an updating of hyperparameters in the training platform, an optimized inference model is configured to be deployed to the inference platform from the training platform. The optimized inference model is further optimized in the inference platform by using an observation difference between a client observation and a prediction response to update the optimized inference model. The updated optimized inference model is used to provide a prediction response to a client.

Abstract: In one embodiment, a system and method for point cloud registration of LIDAR poses of an autonomous driving vehicle (ADV) is disclosed. The method selects poses of the point clouds that possess higher confidence level during the data capture phase as fixed anchor poses. The fixed anchor points are used to estimate and optimize the poses of non-anchor poses during point cloud registration. The method may partition the points clouds into blocks to perform the ICP algorithm for each block in parallel by minimizing the cost function of the bundle adjustment equation updated with a regularity term. The regularity term may measure the difference between current estimates of the poses and previous or the initial estimates. The method may also minimize the bundle adjustment equation updated with a regularity term when solving the pose graph problem to merge the optimized poses from the blocks to make connections between the blocks.

Abstract: In one embodiment, a system generates a plurality of driving scenarios to train a reinforcement learning (RL) agent and replays each of the driving scenarios to train the RL agent by: applying a RL algorithm to an initial state of a driving scenario to determine a number of control actions from a number of discretized control/action options for the ADV to advance to a number of trajectory states which are based on a number of discretized trajectory state options, determining a reward prediction by the RL algorithm for each of the controls/actions, determining a judgment score for the trajectory states, and updating the RL agent based on the judgment score.

Abstract: In one embodiment, a computer-implemented method of autonomously parking an autonomous driving vehicle, includes generating environment descriptor data describing a driving environment surrounding the autonomous driving vehicle (ADV), including identifying a parking space and one or more obstacles within a predetermined proximity of the ADV, generating a parking trajectory of the ADV based on the environment descriptor data to autonomously park the ADV into the parking space, including optimizing the parking trajectory in view of the one or more obstacles, segmenting the parking trajectory into one or more trajectory segments based on a vehicle state of the ADV, and controlling the ADV according to the one or more trajectory segments of the parking trajectory to autonomously park the ADV into the parking space without collision with the one or more obstacles.

Abstract: In one embodiment, a system uses an actor-critic reinforcement learning model to generate a trajectory for an autonomous driving vehicle (ADV) in an open space. The system perceives an environment surrounding an ADV. The system applies a RL algorithm to an initial state of a planning trajectory based on the perceived environment to determine a plurality of controls for the ADV to advance to a plurality of trajectory states based on map and vehicle control information for the ADV. The system determines a reward prediction by the RL algorithm for each of the plurality of controls in view of a target destination state. The system generates a first trajectory from the trajectory states by maximizing the reward predictions to control the ADV autonomously according to the first trajectory.

Abstract: In one embodiment, a system identifies a road to be navigated by an ADV, the road being captured by one or more point clouds from one or more LIDAR sensors. The system extracts road marking information of the identified road from the point clouds, the road marking information describing one or more road markings of the identified road. The system partitions the road into one or more road partitions based on the road markings. The system generates a point cloud map based on the road partitions, where the point cloud map is utilized to perceive a driving environment surrounding the ADV.