Gm Global Technolgoy Operations, Inc.

Cavnue Technology, LLC

Other

David Hahn Clifford

Orhan Bulan

Yehenew G Mengistu

Sheetal Mahesh

Christopher L Oesterling

David J Hiemenga

Andrew J Lingg

Mason D Gemar

Mohammed H Al Qizwini

Mohannad F Murad

Michael Ames

Michael A Losh

Paul H Pebbles

Orhan Demirovic

Patrick Minjeur

Daniel Taylor

Robert R Miles

Hrushit B Raval

Eric Hatfield

Benjamin J Cool

Christopher Edward Hoover

Paul A Avery

Demetrios Vasili Kellari

Samuel Walker Beck

Jack G Stepanian

Xuewei Qi

Dawn M Chorbagian

Paul Mark De La Vergne

Systems and techniques are described for identifying, monitoring, and sharing vehicle information amongst sensors. In some implementations, a system includes a central server and a plurality of sensors. The plurality of sensors are positioned in a fixed location relative to a roadway. Each sensor in the plurality of sensors is configured to: detect vehicles in a first field of view on the roadway. For each detected vehicle, each sensor is configured to identify features of the detected vehicle and perform operations for each feature. The operations include generating feature data representing the feature, generating a unique identification of the detected vehicle from the detected vehicles by concatenating the feature data representing the identified features of the detected vehicle, and adding the unique identification to a list.

Road element sensors and identifiers

Systems and techniques are described for enabling access and egress to dedicated lanes in a vehicular environment. In some implementations, a system include a central server, a gantry system, and a plurality of sensors. The plurality of sensors are positioned in a fixed location relative to a roadway. Each sensor in the plurality of sensors can detect vehicles in a field of view on the roadway. For each detected vehicle, each sensor can generate sensor data and provide the generated sensor data to the gantry system. The gantry system can receive the sensor data and determine whether the detected vehicle can access the dedicated lane based on the received sensor data. In response to determining the detected vehicle can access the dedicated lane, the gantry system can display an entry speed, open a gate to enable the detected vehicle access, and display an access indicator to the detected vehicle.

Intelligent entry and egress for dedicated lane

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for monitoring vehicles traversing a roadway using acoustic sensors. In some implementations, a server obtains data from an acoustic sensor monitoring road actors traversing a roadway at a first location. The server obtains data from an imaging sensor monitoring the road actors traversing the roadway at a second location. The server generates correlation data using the data from the acoustic sensor and the data from the imaging sensor. The server determines observations of the road actors traversing the roadway using the data from the acoustic sensor and the data from the imaging sensor. The server trains a machine-learning model to estimate characteristics of the road actors using the correlation data and the determined observations of the road actors from the imaging sensor and the acoustic sensor.

Acoustic sensor processing

Systems and techniques are described for a signal head processing system. A signal head detector can be mounted by or attached to an existing control light signal head and provide data indicating the state, i.e., on, off, blinking, of each light of the control light signal head. The signal head detector can communicate with a nearby gateway device. The gateway device, in turn, communicates with a back end server, and the back end server performs near real-time control light pattern analysis and provides near real-time light status. Alternatively, the signal head detector can communication directly with the based end server. The server analysis can include frequency of light change per canister, intersection geometry and lane flows, per-intersection control lighting patterns, seasonal, e.g., time of day, lighting patterns, anomaly detection and failure detection.

Signal head processing system

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.

Generating map features based on aerial data and telemetry data

Systems and techniques are described for consuming data in an intelligent transport system. In some implementations, a system includes a display screen device and sensors. The sensors generates data describing sensor observations of a roadway at a first location and provides data describing the observations to the display screen device. The display screen device receives the data and determines an event and a type of the event. The display screen device displays second data indicative of the type of event, the second data being of a format that is consumable by a sensor on a vehicle traversing the roadway towards the first location, the sensor (i) located within a first resolution distance from the display screen device and (ii) located outside a second resolution distance of detecting the event, wherein the second data is used by an on-board processing system of the vehicle to adjust its driving behavior.

Data consumable for intelligent transport system

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.

Identification and clustering of lane lines on roadways using reinforcement learning

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.

Motor vehicle with cognitive response test system for preemptively detecting potential driver impairment

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.

Obstacle detection and notification for motorcycles

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.

Estimation of road centerline based on vehicle telemetry

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.

Reinforcement learning based system for aerial imagery acquisition using drone following target vehicle

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.

Inferring lane boundaries via high speed vehicle telemetry

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.

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Royal Oak, MI, United States of America

David Hahn Clifford