Gm Global Technolgoy Operations, Inc.

Lawrence A Bush

Upali Priyantha Mudalige

Shuqing Zeng

Brent N Bacchus

Donald K Grimm

Prabhjot Kaur

Aravindhan Mani

Fan Bai

Wei Tong

Shifang Li

Michael A Losh

Yuchen Zhou

Zachariah E Tyree

Joseph F Szczerba

Bo Yu

Mohammad Naserian

Mohannad F Murad

Roy J Mathieu

Prakash Mohan Peranandam

Stephen W Decker

Alireza Esna Ashari Esfahani

Eric L Raphael

Guangyu J Zou

Kevin K Hoang

Ashok Yendluri

Evripidis Paraskevas

James P Neville

Manpreet S Bajwa

Shihchye Allen Lin

Rickie A Sprague

Pengfei Ren

A sequence of images and a vehicle location associated with each of the images is received at a traffic light ROI management system. At least one traffic light is detected in each image. A ECS traffic light ROI is defined for each image. The ECS traffic light ROI encloses the detected traffic lights. A visual feature template is generated for each image. The visual feature template is based on the ECS traffic light ROI for the image. Each visual feature template is mapped to the vehicle location associated with the image to a HD map. The HD map is transmitted to an autonomous vehicle to enable the autonomous vehicle to identify a real-time traffic light ROI in a real-time image based on a match between a first visual feature template and real-time visual features of the real-time image at the vehicle location associated with the first visual feature template.

Systems and methods for providing traffic light region of interest parameters to an autonomous vehicle

A method for reducing parking violations includes: receiving, by a controller of a vehicle, sign information of one or more traffic signs in an area surrounding the vehicle, wherein the sign information is a time series of data acquired by a set of cameras as the vehicle moves, using the vehicle as a frame of reference; identifying, by the controller, a potential parking spot in an area surrounding the vehicle; determining, by the controller, using the sign information, whether the potential parking spot is valid or invalid for the vehicle; and generating, by the controller, a notification if the potential parking spot is invalid.

Intelligent park assist system with sign interpretation

A supervisory control system is disclosed that provides an operator situational awareness interface use with monitoring a plurality of automated vehicles (AVs). The system is configured to: generate a map of a geographical area of interest; obtain location data and perceived risk data for a plurality of AVs in the geographical area; generate a vehicle icon corresponding to each AV; position the vehicle icon for each AV on the map based on the location data for a corresponding AV; apply a color coding to each vehicle icon based on a perceived risk level for a corresponding AV; and signal a display device to display an AV fleet map graphic that includes the color coded vehicle icons positioned on the map. The controller may be further configured to: generate an AV servicing queue graphic that displays vehicle icons in an order based on a determined servicing priority.

System and method for providing situational awareness interfaces for autonomous vehicle operators

Systems and methods of detecting a traffic object outside of a vehicle and controlling the vehicle. The systems and methods receive perception data from a sensor system included in the vehicle, determine a focused Region Of Interest (ROI) in the perception data, scale the perception data of the at least one focused ROI, process the scaled perception data of the focused ROI using a neural network (NN)-based traffic object detection algorithm to provide traffic object detection data, and control at least one vehicle feature based, in part, on the traffic object detection data.

Systems and methods for detecting traffic objects

A system and method for monitoring a road segment includes determining a geographic position of a vehicle in context of a digitized roadway map. A perceived point cloud and a mapped point cloud associated with the road segment are determined. An error vector is determined based upon a transformation between the mapped point cloud and the perceived point cloud. A first confidence interval is derived from a Gaussian process that is composed from past observations. A second confidence interval associated with a longitudinal dimension and a third confidence interval associated with a lateral dimension are determined based upon the mapped point cloud and the perceived point cloud. A Kalman filter analysis is executed to dynamically determine a position of the vehicle relative to the roadway map based upon the error vector, the first confidence interval, the second confidence interval, and the third confidence interval.

Method and system for localized travel lane perception

A system for generating a map includes a processing device configured to determine a provenance of each received map of a plurality of maps, parse each received map into objects of interest, and compare the objects of interest to identify one or more sets of objects representing a common feature. For each set of objects, the processing device is configured to select a subset of the set of objects based on the provenance associated with each object in the set of objects, and calculate a similarity metric for each object in the subset. The similarity metric is selected from an alignment between an object and a reference object in the subset, and/or a positional relationship between the object and the reference object. The processing device is configured to generate a common object representing the common feature based on the similarity metric, and generate a merged map including the common object.

Combining heterogeneous types of maps

Methods and systems are provided for detecting objects within an environment of a vehicle. In one embodiment, a method includes: receiving, by a processor, image data sensed from the environment of the vehicle; determining, by a processor, an area within the image data that object identification is uncertain; controlling, by the processor, a position of a lighting device to illuminate a location in the environment of the vehicle, wherein the location is associated with the area; controlling, by the processor, a position of one or more sensors to obtain sensor data from the location of the environment of the vehicle while the lighting device is illuminating the location; identifying, by the processor, one or more objects from the sensor data; and controlling, by the processor, the vehicle based on the one or more objects.

Perception methods and systems for low lighting conditions

Systems and methods are provided for interpreting traffic information by a vehicle. In one embodiment, a method includes: receiving, by a processor, sensor data from one or more sensing devices of the vehicle, where the sensor data depicts a traffic device in an environment of the vehicle; receiving, by the processor, map data associated with the environment of the vehicle, where the map data includes traffic devices; matching, by the processor, the traffic device of the sensor data with a traffic device of the map data; determining, by the processor, a probability distribution of a traffic signal associated with the matched traffic devices based on a Hidden Markov model (HMM); and planning, by the processor, control of the vehicle based on the probability distribution.

Methods and systems for interpretating traffic signals and negotiating signalized intersections

The vehicle mounted perception sensor gathers environment perception data from a scene using first and second heterogeneous (different modality) sensors, at least one of the heterogeneous sensors is directable to a predetermined region of interest. A perception processor receives the environment perception data and performs object recognition to identify objects each with a computed confidence score. The processor assesses the confidence score vis-&#xe0;-vis a predetermined threshold, and based on that assessment, generates an attention signal to redirect the one of the heterogeneous sensors to a region of interest identified by the other heterogeneous sensor. In this way information from one sensor primes the other sensor to increase accuracy and provide deeper knowledge about the scene and thus do a better job of object tracking in vehicular applications.

Selective attention mechanism for improved perception sensor performance in vehicular applications

A control system for a subject vehicle includes an autonomous braking system, a forward monitoring sensor and a rearward monitoring sensor. The controller monitors a first speed of a first vehicle travelling in front of the subject vehicle and a second speed of a second vehicle travelling to the rear of the subject vehicle. A first gap-closing time is determined based upon the speed of the subject vehicle and the first speed of the first vehicle. A second gap-closing time is determined based upon the speed of the subject vehicle and the second speed of the second vehicle. The controller controls the speed of the subject vehicle based upon the first gap-closing time and the second gap-closing time when one of the first gap-closing time or the second gap-closing time is less than a first threshold time.

Method and subsystem for controlling an autonomous braking system for a vehicle

A risk maneuver assessment system and method to generate a perception of an environment of a vehicle and a behavior decision making model for the vehicle; a sensor system configured to provide the sensor input in the environment for filtering target objects; one or more modules configured to map and track target objects to make a candidate detection from multiple candidate detections of a true candidate detection as the tracked target object; apply a Markov Random Field (MRF) algorithm for recognizing a current situation of the vehicle and predict a risk of executing a planned vehicle maneuver at the true detection of the dynamically tracked target; apply mapping functions to sensed data of the environment for configuring a machine learning model of decision making behavior of the vehicle; and apply adaptive threshold to cells of an occupancy grid for representing an area of tracking of objects within the vehicle environment.

System and method for radar cross traffic tracking and maneuver risk estimation

A method for vehicle tracking and localization includes receiving, by a controller, odometry data from a sensor of the first vehicle; geospatial data from a Global Positioning System (GPS) device of the first vehicle; inertial data from an inertial measurement unit (IMU) of the first vehicle; estimating an estimated-current location of the first vehicle and an estimated-current trajectory of the first vehicle using the odometry data from the sensor, the geospatial data from the GPS device, and the inertial data from the IMU of the first vehicle; inputting the inertial data into a Bayesian Network to determine a predicted location of the first vehicle and a predicted trajectory of the first vehicle, and updating the Bayesian Network using the estimated-current location and the estimated-current trajectory of the first vehicle using the odometry data and the geospatial data.

Method and apparatus of parallel tracking and localization via multi-mode slam fusion process

A disambiguating system for disambiguating between ambiguous detections is provided. The system includes a plurality of modules, wherein each module is configured to disambiguate between ambiguous detections by selecting, as a true detection, one candidate detection in a set of ambiguous detections and wherein each module is configured to apply a different selection technique. The system includes: one or more modules configured to select as the true detection, the candidate detection whose associated position is closer to a position indicated by other data and one or more modules configured to select as the true detection, the candidate detection with the highest probability of being true based on other sensor data.

Method for disambiguating ambiguous detections in sensor fusion systems

A driver monitoring system for a vehicle and method of operating the driver operating system. The method, in one implementation, involves receiving a plurality of glance aim points for a driver of the vehicle; inputting the plurality of glance aim points into a predictive probability of distraction model to obtain a predictive distraction distribution; determining whether one or more informative glance locations are present in the plurality of glance aim points; comparing the predictive distraction distribution to a predictive distraction distribution threshold when one or more informative glance locations are present in the plurality of glance aim points; and alerting the driver when the predictive distraction distribution satisfies or exceeds the predictive distraction distribution threshold.

Driver monitoring system and method of operating the same

Technical solutions are described for providing a driver performance feedback to a driver of a vehicle. An example method includes receiving, by a controller, a first maneuver control from an automated driving system of the vehicle. The method further includes receiving, by the controller, a second maneuver control from the driver. The method further includes, in response to the first maneuver control being different from the second maneuver control, generating a driver notification that is indicative of the first maneuver control from the automated driving system, and operating the vehicle using the second maneuver control.

Growing community of inventors

Shelby Township, MI, United States of America

Lawrence A Bush