Gm Global Technolgoy Operations, Inc.

University of Michigan

Vivek Vijaya Kumar

Bo Yu

Hariharan Krishnan

Fan Bai

Donald K Grimm

Mohammad Naserian

Joon Hwang

Carl P Darukhanavala

Gui Chen

Shu Chen

Curtis L Hay

Kamran Ali

Chuan Li

Mason D Gemar

Robert A Bordo

Shuqing Zeng

Upali Priyantha Mudalige

Norman Joseph Weigert

Yiran Hu

Douglas C Martin

Raymond J Kiefer

Akram M Abdel-Rahman

Kausalya Singuru

Mohamed A Layouni

Steven E Muldoon

Syed B Mehdi

Omer Tsimhoni

Junfeng Zhao

Yi G Glaser

Sandeep Vig

Ashok Yendluri

Kemal E Tepe

Priya B Abraham

Kevin A O'dea

Pei Xu

Dustin H M Smith

Dustin Howard Malcom Smith

Carolyn Heather MacLeod

Shengyin Shen

Muhammad Rehan

Zihao Wang

Zachary Jerome

Xingmin Wang

Henry Xianghong Liu

Xiaoguang Huang

A system for determining an optimal behavior for a vehicle includes at least one vehicle sensor and a controller in electrical communication with the at least one vehicle sensor. The controller is programmed to determine an ingress stop bar location and an egress stop bar location of a traffic-signal-controlled intersection in a path of the vehicle using the at least one vehicle sensor. The controller is further programmed to determine a current signal phase of the traffic signal using the at least one vehicle sensor. The controller is further programmed to determine a location of a first remote vehicle using the at least one vehicle sensor. The controller is further programmed to determine the optimal behavior for the vehicle based at least in part on the ingress stop bar location, the egress stop bar location, the current signal phase, and the location of the first remote vehicle.

Determining optimal vehicle behavior at intersections

A system for fusing two or more versions of map data together includes one or more central computers that receive road network data representing a road network for a predefined geofenced area. The central computers compute a plurality of points that are each positioned at a predetermined distance from one another. The central computers create a plurality of bounding boxes for the road network based on the plurality of points and create a set of closest matched map data points for each bounding box that is part of the road network by executing a map-matching registration algorithm to align the two or more versions of map data with one another. The central computers execute a maximum likelihood estimation algorithm to determine probability distribution parameters of the set of closest matched map data points compared to the ground truth map data.

System for fusing two or more versions of map data

A method for determining the route of a vehicle includes receiving, by a vehicle controller of a host vehicle, traffic-light data, map data, and vehicle-telemetry data. Further, the method includes identifying instances of chain waiting along the plurality of routes in the predetermined area using the traffic-light data and the vehicle-telemetry data and determining the fastest route of the plurality of routes to travel from a starting point to an end point within the predetermined area using the map data, the instances of chain waiting along the plurality of routes, the vehicle-telemetry data, and the traffic-light data.

Routing with traffic light information

A system for enhancing efficient of vehicle to everything (V2X) stream communication includes transmitting and receiving vehicles. Sensors capture information about the transmitting and receiving vehicles, and capture environmental information about an environment of the transmitting and receiving vehicles. Each vehicle has a controller that executes an application for enhancing V2X stream communication (EVSC). The EVSC obtains, from the sensors, transmitting and receiving vehicle information and environmental information. The EVSC actively and continuously engages a sparse signing approach (SSA) and a chained packet approach (CPA) portion of the EVSC to monitor bandwidth utilization and computational resource utilization for the transmitting and receiving vehicle information and environmental information, and to actively and continuously reduce bandwidth and computational resource utilization from a first level to a second level less than the first level while ensuring security of V2X communications.

Methods for enhancing efficiency of secure vehicle to everything (V2X) stream communication

A method and system of map data reconciliation in connected vehicles. The method includes processing information to detect a same object; determining an identification of the same object; determining a confidence level of the identification of the same object; determining a probability mass function (PMF) of the identification of the same object using Dempster-Shafer Theory; determining a most probably identification of the same object based on the PMF; and communicating the most probably identification of the same object to a vehicle to implement a vehicle function. The system includes a conflict resolution module configured to receive confidence levels from multiple vehicles detecting the same object and to determine a reconciled identification of the same object using an algorithm based on Dempster-Shafer Theory, and a weight calculation module configured to apply a weight function based on a scene context weight function and on a historical score of the vehicles.

Method and system of inconsistent map data reconciliation in connected vehicles

A system for optimizing traffic signal timing using telemetry includes: wirelessly connected devices (WCDs), traffic operations centers (TOCs) controlling one or more traffic signals, and control modules storing and continuously executing control logic in a closed loop. The control logic continuously receives real-time WCD telemetry data and performs real-time processing of the telemetry data to match the telemetry data to physical locations. Processed telemetry data is aggregated to determine WCD trajectories. The system selectively performs time-of-day (TOD), offset, and green split optimizations, and receives the WCD trajectories and outputs of the TOD, offset, and green split optimization within an API. The system generates an optimized signal plan from data from the API, and verifies the optimized signal plan. The optimized signal plan is received by the TOCs and selectively uploaded to the traffic signals. The optimized signal plan increases throughput of traffic by decreasing total delays, and quantities of stops.

Optimizing traffic signal timing using vehicle telemetry data

A method for lateral assist includes receiving work-zone data. The work-zone data includes information about a location of a work zone and a geometry of the work zone. The method further includes determining a route of a vehicle using the work-zone data. Determining the route path of the vehicle includes selecting a lane segment using the work-zone data. The lane segment is adjacent to the work zone. Determining the route path further includes determining a lane departure tolerance for the lane segment previously selected using the work-zone data. The method further includes commanding the vehicle to move within the lane departure tolerance along the lane segment previously selected.

Method to enhance lateral assist applications in the presence of road features

A global positioning system (GPS)-bias detection and reduction system including a GPS-bias model having GPS statistical data creating a database representing data collected from a vehicle group having thousands or multiple thousands of vehicles saved in a database. At least one newly collected vehicle GPS data point is compared to the GPS statistical data to reduce negative effects of GPS-bias and to update the vehicle GPS-bias correction based on a previous GPS-bias model. A selected road node and a segment of a roadway have a map matching performed using a nearest service from a collection location of the GPS statistical data. A GPS-bias is calculated using a look-up of the database. An estimated horizontal position error (EHPE) defining a quality indicator is applied to distinguish a good quality GPS statistical data from a poor quality GPS statistical data.

Global positioning system bias detection and reduction

A method of determining the position of a vehicle includes generating a vehicle-based point cloud of objects in proximity to the vehicle, referenced to a vehicle-based coordinate system. The method also includes receiving an infrastructure-based point cloud, referenced to a global coordinate system, of objects detected by a camera mounted at a fixed location external to the vehicle, and registering the vehicle-based point cloud with the infrastructure-based point cloud to determine the vehicle position in the global coordinate system.

Method and apparatus for leveraging infrastructure camera for vehicle positioning

A vehicle system is provided for correcting in real-time a camera-based estimated position of a road object. The system includes a camera for generating an image input signal including image sensor data associated with the road object. The system further includes one or more input devices for generating a vehicle input signal including vehicle sensor data associated with a position, a speed, and a heading of the vehicle. The system further includes a computer, which includes one or more processors and a non-transitory computer readable medium (CRM) storing instructions. The processor is programmed to match the image sensor data and the vehicle sensor data to one another based on a common time of collection. The processor is further programmed to determine an error model and a deviation of a current camera-based position from a predicted position. The processor is further programmed to update the error model based on the deviation.

System and method for correcting in real-time an estimated position of a road object

A method of creating a high-definition (HD) map of a roadway includes receiving a multi-layer probability density bitmap. The multi-layer probability density bitmap represents a plurality of lane lines of the roadway sensed by a plurality of sensors of a plurality of vehicles. The multi-layer probability density bitmap includes a plurality of points. The method further includes recursively conducting a hill climbing search using the multi-layer probability density bitmap to create a plurality of lines. In addition, the method includes creating the HD map of the roadway using the plurality of lines determined by the hill climbing search.

Hill climbing algorithm for constructing a lane line map

A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: receive safety messages from a plurality of vehicles in communication with the edge server, determine an uplink frequency recommendation for transmitting safety messages from at least one vehicle of the plurality of vehicles based on at least one of a position error or a collision risk, determine a downlink frequency recommendation for transmitting safety messaging to at least one vehicle of the plurality of vehicles based on at least one of a position error or a collision risk, and transmit the frequency recommendations to the at least one vehicle.

Adaptive messaging within a cloud and edge computing environment for V2X applications

A full speed range adaptive cruise control system for a vehicle that stops at an intersection includes one or more controllers that execute instructions to receive localization data and situational data related to the vehicle. The one or more controllers determine, based on localization data and situational data, that the vehicle is approaching an intersection and will come to a stop at the intersection, where the vehicle is part of a queue including one or more surrounding vehicles. In response to determining the vehicle has come to a stop, the controller determines a position of the vehicle within the queue and an overall length of the queue. The controller calculates an adaptive launch time based on at least the position of the vehicle within the queue, the overall length of the queue, the situational data, and a timing delay associated with the queue.

Full speed range adaptive cruise control system for determining an adaptive launch time for a vehicle

A method includes obtaining information about a lead vehicle. The lead vehicle is traveling directly ahead of the host vehicle and the information includes velocity of the lead vehicle and a gap g between the host vehicle and the lead vehicle. Based on detecting a change in the velocity of the lead vehicle in the information, a corresponding desired acceleration is computed for the host vehicle using perceived acceleration of the lead vehicle to maintain the gap g within a specified range of gap values. The perceived acceleration of the lead vehicle is based on the change in the velocity indicated by the information. Based on a check of parameters involved in the computing the corresponding desired acceleration, a modified desired acceleration is computed for the host vehicle using a lag for the lead vehicle that results in an intended acceleration of the lead vehicle differing from the perceived acceleration.

Consideration of acceleration lag in lead vehicle to enhance host vehicle operation

A method includes receiving sensor data from a plurality of sensors of a plurality of vehicles. The sensor data includes vehicle GPS data and sensed lane line data of the roadway. The method further includes creating a plurality of multi-layer bitmaps for each of the plurality of vehicles using the sensor data, fusing the plurality of the multi-layer bitmaps of each of the plurality of vehicles to create a fused multi-layer bitmap, creating a plurality of multi-layer probability density bitmaps using the fused multi-layer bitmap, extracting lane line data from the plurality of multi-layer probability density bitmaps, and creating the high-definition (HD) map of the roadway using the multi-layer probability density bitmaps and the lane line data extracted from the plurality of multi-layer probability density bitmaps.

Lane line map construction using probability density bitmaps

Growing community of inventors

Shelby Township, MI, United States of America

Vivek Vijaya Kumar