Abstract: A method for detecting road edges at a predetermined intersection, comprising: receiving, by the controller, aerial imagery data about the predetermined intersection; receiving, by the controller, vehicle telemetry data from at least one vehicle passing through the predetermined intersection; detecting, using the aerial imagery data and generative adversarial networks (GANs) executed on the controller, road edges at the predetermined intersection; classifying, using the vehicle telemetry data and a random forest classifier (RFC) executed on the controller, each vehicle trajectory passing through the predetermined intersection with a label corresponding to a unique maneuver to create a maneuver labeling at the predetermined intersection; constructing, using the maneuver labeling determined by the RFC and the road edges, a probabilistic finite state automata (PFSA) to pair inbound lanes with outbound lanes at the predetermined intersection; and determining lane edges at the predetermined intersection using a ho

Abstract: A system comprises a processor and a memory storing instructions. The processor receives an image for processing using a reinforcement learning based agent comprising a neural network trained using a reward function. The image includes N lane lines of a roadway, where N is a positive integer. The instructions configure the processor to traverse the image using the agent at least N times from a first end of the image to a second end of the image by: incrementally moving the agent from a first side of the image to a second side of the image after each traversal; and maximizing rewards for the agent using the reward function during each traversal of the image using the agent. The instructions configure the processor to identify the N lane lines of the roadway as a single cluster of lane lines after traversing the image using the agent at least N times.

Abstract: A method for detecting road edges at a predetermined intersection, comprising: receiving, by the controller, aerial imagery data about the predetermined intersection; receiving, by the controller, vehicle telemetry data from at least one vehicle passing through the predetermined intersection; detecting, using the aerial imagery data and generative adversarial networks (GANs) executed on the controller, road edges at the predetermined intersection; classifying, using the vehicle telemetry data and a random forest classifier (RFC) executed on the controller, each vehicle trajectory passing through the predetermined intersection with a label corresponding to a unique maneuver to create a maneuver labeling at the predetermined intersection; constructing, using the maneuver labeling determined by the RFC and the road edges, a probabilistic finite state automata (PFSA) to pair inbound lanes with outbound lanes at the predetermined intersection; and determining lane edges at the predetermined intersection using a ho

Abstract: System and methods for estimating a centerline of a road that separates traffic moving in opposite directions include aggregating a data set from each of a plurality of vehicles traversing the road over a period of time as telemetry data. Each data set of the telemetry data indicates a location and a heading. The method includes clustering the data sets of the telemetry data based on the heading indicated by each data set, and identifying a separator to separate the data sets indicating a first heading from the data sets indicating a second heading, opposite to the first heading. The centerline is estimated based on applying a spatial smoothing to the separator.

Abstract: A system and method are provided for controlling a vehicle. In one embodiment, a system includes: a sensor system configured to generate sensor data sensed from an environment of the vehicle; and a control module configured to, by a processor, predict parked vehicles within the scene of the environment, identify an outer edge associated with the parked vehicles, generate a virtual lane line based on the outer edge associated with the parked vehicles, and generate signal data based on the virtual lane line to at least one of display the virtual lane line within the scene and control the vehicle.

Abstract: A system comprises a processor and a memory storing instructions. The processor receives an image for processing using a reinforcement learning based agent comprising a neural network trained using a reward function. The image includes N lane lines of a roadway, where N is a positive integer. The instructions configure the processor to traverse the image using the agent at least N times from a first end of the image to a second end of the image by: incrementally moving the agent from a first side of the image to a second side of the image after each traversal; and maximizing rewards for the agent using the reward function during each traversal of the image using the agent. The instructions configure the processor to identify the N lane lines of the roadway as a single cluster of lane lines after traversing the image using the agent at least N times.

Abstract: Systems and method are provided for predicting a vehicle pose. In one embodiment, a system includes a non-transitory computer readable medium. The non-transitory computer readable medium includes: an asynchronous prediction module configured to, by a processor, receive vehicle data from a vehicle, and compute a prediction of vehicle pose from the vehicle data, wherein the computation is performed asynchronously using a vehicle time frame and a common coordinate system; and a synchronous prediction module configured to, by the processor, compute a final prediction of the vehicle pose across the vehicle population based on the prediction of vehicle pose from the asynchronous prediction module, wherein the computation is performed synchronously using a global time frame and the common system.

Abstract: A system and method for automated vehicle control includes referencing map based attributes relevant to an ego vehicles GPS coordinates, establishing control points based upon the map based attributes, and controlling the ego vehicle to the control points with at least one of a steering system, braking system, and powertrain system.

Abstract: Systems and method are provided for predicting a vehicle pose. In one embodiment, a system includes a non-transitory computer readable medium. The non-transitory computer readable medium includes: an asynchronous prediction module configured to, by a processor, receive vehicle data from a vehicle, and compute a prediction of vehicle pose from the vehicle data, wherein the computation is performed asynchronously using a vehicle time frame and a common coordinate system; and a synchronous prediction module configured to, by the processor, compute a final prediction of the vehicle pose across the vehicle population based on the prediction of vehicle pose from the asynchronous prediction module, wherein the computation is performed synchronously using a global time frame and the common coordinate system.

Abstract: Apparatuses, methods, and systems of an edge server communicating with vehicles by continuously receiving messages which include data of state transitions of Electronic Control Units (ECUs) contained in each vehicle; monitoring in real-time the streamed message data from each vehicle for detection of updates of data about state transitions of ECUs used in the vehicular operations; crowdsourcing in real-time the state transition data of the ECUs during the vehicular operations to group together state transition data having commonality as crowdsource transition data; processing, using a machine learning model, the crowdsource transition data, to classify the crowdsource transition data of an anomaly event; and transmit in advance to an identified vehicle an alert of the anomaly event in order for a driver or ADAS to execute a response.

Abstract: Apparatuses, methods, and systems of an edge server communicating with vehicles by continuously receiving messages which include data of state transitions of Electronic Control Units (ECUs) contained in each vehicle; monitoring in real-time the streamed message data from each vehicle for detection of updates of data about state transitions of ECUs used in the vehicular operations; crowdsourcing in real-time the state transition data of the ECUs during the vehicular operations to group together state transition data having commonality as crowdsource transition data; processing, using a machine learning model, the crowdsource transition data, to classify the crowdsource transition data of an anomaly event; and transmit in advance to an identified vehicle an alert of the anomaly event in order for a driver or ADAS to execute a response.

Abstract: Near-real-time monitoring and analysis of a road segment are described, and include a remotely-located controller that is arranged to wirelessly communicate with a plurality of connected vehicles. This includes periodically capturing vehicle positional data and associated temporal data that are communicated from a subset of the connected vehicles, wherein the subset of the connected vehicles traverse a road segment associated with the digitized roadway map. Vehicle positional data and the associated temporal data are analyzed over a period of time, including evaluating the vehicle positional data in context of the digitized roadway map. Occurrence of an anomalous traffic pattern for the road segment can be detected based upon the evaluating of the vehicle positional data in context of the digitized roadway map and the associated temporal data. A control action is executed based upon detection of the occurrence of the anomalous traffic pattern for the road segment.

Abstract: The present application relates to predicting an automated driving system disengagement for a motor vehicle by calculating a route between a host vehicle location and a destination, segmenting the route into at least a first route segment and a second route segment, generating a first motion path for the first route segment and controlling the host vehicle over the first route segment, generating a second motion path for the second route segment and simulating a simulated host vehicle operation over the second route segment, predicting a disengagement event in response to the simulated host vehicle operation over the second route segment, and providing a driver alert indicative of the disengagement event while controlling the host vehicle over the first route segment.

Abstract: System and methods for estimating a centerline of a road that separates traffic moving in opposite directions include aggregating a data set from each of a plurality of vehicles traversing the road over a period of time as telemetry data. Each data set of the telemetry data indicates a location and a heading. The method includes clustering the data sets of the telemetry data based on the heading indicated by each data set, and identifying a separator to separate the data sets indicating a first heading from the data sets indicating a second heading, opposite to the first heading. The centerline is estimated based on applying a spatial smoothing to the separator.

Abstract: At least one transceiver is configured to wirelessly receive a unique identifier (ID) of a source node that is external to a vehicle. A distributed ledger includes a list of unique IDs associated with trusted source nodes. An ID comparison module is configured to compare the unique ID of the source node with the list. The at least one transceiver is configured to, in response to a determination that the unique ID of the source node is not within the list, wirelessly transmit the unique ID of the source node the trusted source nodes of a connected network for execution of a consensus algorithm. A ledger management module is configured to, in response to a determination to trust the source node via execution of the consensus algorithm, add the unique ID of the source node to the list.