Abstract: In one example implementation according to aspects of the present disclosure, a computer-implemented method for generating map features includes receiving, by a processing device, aerial image data. The method further includes receiving, by the processing device, telemetry data. The method further includes performing, by the processing device, data fusion on the aerial image data and the telemetry data to generate map features. The method further includes controlling, by the processing system, a vehicle based at least in part on the map features.

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 test system for detecting impairment aboard a motor vehicle includes an electronic control unit (ECU) and sensors in communication therewith. The sensors are positioned within a vehicle interior, and include at least a touch screen and a microphone, and possibly an eye-tracking camera. In response to receiving a start request indicative of a requested start event of the motor vehicle, the ECU executes instructions to initiate a cognitive response test via the sensors. The ECU determines a test score of a driver during the test while the motor vehicle remains off, compares the test score to baseline scores to determine a passing or failing test result, and executes a control action aboard the motor vehicle in response to the passing or failing test result. The ECU may disable or immobilize the motor vehicle in response to the failing test result.

Abstract: An obstacle detection and notification system for a motorcycle. The system includes a forward looking camera and a backward looking camera mountable to the motorcycle and a processor in operable communication with the forward looking camera and the backward looking camera. The processor executes program instructions to execute processes including: receiving video from the of the forward looking camera and the backward looking camera, performing a computer vision and machine learning based object detection and tracking process to detect, classify and track obstacles in the video and to output detected object data, defining a blind spot region around one or more other vehicles using the detected object data, determining whether the motorcycle is located in the blind spot region, and outputting audible, tactile or visual feedback, via an output system, to a rider of the motorcycle when the motorcycle is determined to be located in the blind spot region.

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 method of surveying roads includes generating a dynamic flight plan for a drone using a vehicle traveling on a road as a target. The dynamic flight plan includes instructions for movement of the drone. The method includes controlling the drone as a function of position of the vehicle based on the dynamic flight plan. The method includes maintaining, based on the controlling, line of sight with the drone while the drone with an onboard camera follows the vehicle and captures images of the road being traveled by the vehicle using the onboard camera.

Abstract: An obstacle detection and notification system for a motorcycle. The system includes a forward looking camera and a backward looking camera mountable to the motorcycle and a processor in operable communication with the forward looking camera and the backward looking camera. The processor executes program instructions to execute processes including: receiving video from the of the forward looking camera and the backward looking camera, performing a computer vision and machine learning based object detection and tracking process to detect, classify and track obstacles in the video and to output detected object data and outputting directional audible, tactile or visual feedback, via an output system, to a rider of the motorcycle based on the detected object data.

Abstract: An obstacle detection and notification system for a motorcycle. The system includes a forward looking camera and a backward looking camera mountable to the motorcycle and a processor in operable communication with the forward looking camera and the backward looking camera. The processor executes program instructions to execute processes including: receiving video from the of the forward looking camera and the backward looking camera, performing a computer vision and machine learning based object detection and tracking process to detect, classify and track obstacles in the video and to output detected object data, determining an approaching vehicle threat based on the detected object data, and outputting audible, tactile or visual feedback, via an output system, to a rider of the motorcycle indicating the approaching vehicle threat.

Abstract: An obstacle detection and notification system for a motorcycle. The system includes a forward looking thermal camera and a backward looking thermal camera mountable to the motorcycle and a processor in operable communication with the forward looking camera and the backward looking camera. The processor executes program instructions to execute processes including: receiving video from the of the forward looking camera and the backward looking camera, performing a computer vision and machine learning based object detection and tracking process to detect, classify and track obstacles in the video and to output detected object data and outputting audible, tactile or visual feedback, via an output system, to a rider of the motorcycle based on the detected object data.

Abstract: An obstacle detection and notification system for a motorcycle. The system includes a forward looking camera and a backward looking camera mountable to the motorcycle and a processor in operable communication with the forward looking camera and the backward looking camera. The processor executes program instructions to execute processes including: receiving video from the of the forward looking camera and the backward looking camera, performing a computer vision and machine learning based object detection and tracking process to detect, classify and track obstacles in the video and to output detected object data, defining a blind spot region around one or more other vehicles using the detected object data, determining whether the motorcycle is located in the blind spot region, and outputting audible, tactile or visual feedback, via an output system, to a rider of the motorcycle when the motorcycle is determined to be located in the blind spot region.

Abstract: A system for inferring lane boundaries via vehicle telemetry for a road section is provided. The system includes a computerized remote server device operable to receive through a communications network sensor data describing lane markings upon roads bordering the road section, determine established lanes upon roads bordering the road section based upon the sensor data, receive through the communications network the vehicle telemetry generated by a plurality of vehicles traversing the road section, generate inferred lanes for the road section based upon the vehicle telemetry, match the inferred lanes to the established lanes to generate unified lane geometries, and publish the unified lane geometries.

Abstract: An obstacle detection and notification system for a motorcycle. The system includes a forward looking camera and a backward looking camera mountable to the motorcycle and a processor in operable communication with the forward looking camera and the backward looking camera. The processor executes program instructions to execute processes including: receiving video from the of the forward looking camera and the backward looking camera, performing a computer vision and machine learning based object detection and tracking process to detect, classify and track obstacles in the video and to output detected object data, defining a blind spot region around one or more other vehicles using the detected object data, determining whether the motorcycle is located in the blind spot region, and outputting audible, tactile or visual feedback, via an output system, to a rider of the motorcycle when the motorcycle is determined to be located in the blind spot region.

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: An obstacle detection and notification system for a motorcycle. The system includes a forward looking camera and a backward looking camera mountable to the motorcycle and a processor in operable communication with the forward looking camera and the backward looking camera. The processor executes program instructions to execute processes including: receiving video from the of the forward looking camera and the backward looking camera, performing a computer vision and machine learning based object detection and tracking process to detect, classify and track obstacles in the video and to output detected object data, defining a blind spot region around one or more other vehicles using the detected object data, determining whether the motorcycle is located in the blind spot region, and outputting audible, tactile or visual feedback, via an output system, to a rider of the motorcycle when the motorcycle is determined to be located in the blind spot region.

Abstract: A method to detect a roadway edge includes calculating a first likelihood of a roadway edge from an aerial image of a roadway by shifting a centerline of the roadway perpendicular to the centerline and overlapping the centerline with image gradients. A second likelihood of the roadway edge is determined using a vehicle telemetry fitting a probability distribution to telemetry points along the roadway. The first likelihood of the roadway edge and the second likelihood of the roadway edge are fused to identify a final likelihood of the roadway edge.

Abstract: A method to detect a roadway edge includes calculating a first likelihood of a roadway edge from an aerial image of a roadway by shifting a centerline of the roadway perpendicular to the centerline and overlapping the centerline with image gradients. A second likelihood of the roadway edge is determined using a vehicle telemetry fitting a probability distribution to telemetry points along the roadway. The first likelihood of the roadway edge and the second likelihood of the roadway edge are fused to identify a final likelihood of the roadway edge.

Abstract: A system and method for communicating data using lidar, the method being carried out by a lidar communication system, the method including: activating a data communication mode of a lidar unit; preparing data for communication using lidar; after activating the data communication mode, emitting a plurality of light pulses using the lidar unit, wherein the plurality of light pulses are emitted in a manner so as to convey the prepared data to an external lidar communication device; and receiving an acknowledgment message, wherein the acknowledgment message indicates receipt of the prepared data at the external lidar communication device.

Abstract: The present application generally relates communications and hazard avoidance within a monitored driving environment. More specifically, the application teaches a system for improved target object detection in a vehicle equipped with a laser detection and ranging LIDAR system by determining a plurality of ranges in response to a plurality of light pulse reflections and determining a false target indication by exploiting the expected continuity of surfaces in the environment.

Abstract: A test system for detecting impairment aboard a motor vehicle includes an electronic control unit (ECU) and sensors in communication therewith. The sensors are positioned within a vehicle interior, and include at least a touch screen and a microphone, and possibly an eye-tracking camera. In response to receiving a start request indicative of a requested start event of the motor vehicle, the ECU executes instructions to initiate a cognitive response test via the sensors. The ECU determines a test score of a driver during the test while the motor vehicle remains off, compares the test score to baseline scores to determine a passing or failing test result, and executes a control action aboard the motor vehicle in response to the passing or failing test result. The ECU may disable or immobilize the motor vehicle in response to the failing test result.