View source for 20230186646. SYSTEM AND METHOD FOR DETECTING ROAD AND LANE CONNECTIONS AT INTERSECTIONS simplified abstract (GM Global Technology Operations LLC)

=SYSTEM AND METHOD FOR DETECTING ROAD AND LANE CONNECTIONS AT INTERSECTIONS=


==Organization Name==

[[:Category:GM Global Technology Operations LLC|GM Global Technology Operations LLC]]


[[Category:GM Global Technology Operations LLC]]

==Inventor(s)==

[[:Category:Najah Ghalyan of Austin TX (US)|Najah Ghalyan of Austin TX (US)]][[Category:Najah Ghalyan of Austin TX (US)]]

[[:Category:Kaydee Hartmann of Austin TX (US)|Kaydee Hartmann of Austin TX (US)]][[Category:Kaydee Hartmann of Austin TX (US)]]

[[:Category:Sean D. Vermillion of Austin TX (US)|Sean D. Vermillion of Austin TX (US)]][[Category:Sean D. Vermillion of Austin TX (US)]]

[[:Category:Mason D. Gemar of Cedar Park TX (US)|Mason D. Gemar of Cedar Park TX (US)]][[Category:Mason D. Gemar of Cedar Park TX (US)]]

[[:Category:Rajesh Ayyalasomayajula of Austin TX (US)|Rajesh Ayyalasomayajula of Austin TX (US)]][[Category:Rajesh Ayyalasomayajula of Austin TX (US)]]

==SYSTEM AND METHOD FOR DETECTING ROAD AND LANE CONNECTIONS AT INTERSECTIONS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20230186646 titled 'SYSTEM AND METHOD FOR DETECTING ROAD AND LANE CONNECTIONS AT INTERSECTIONS

==Simplified Explanation==

The patent application describes a method for detecting road edges at a specific intersection using aerial imagery data and vehicle telemetry data. The method involves the use of generative adversarial networks (GANs) and a random forest classifier (RFC) to detect road edges and classify vehicle trajectories, respectively. 

* The method receives aerial imagery data and vehicle telemetry data about a predetermined intersection.
* Generative adversarial networks (GANs) are used to detect road edges at the intersection.
* Random forest classifier (RFC) is used to classify each vehicle trajectory passing through the intersection with a unique maneuver label.
* The maneuver labeling and road edges are used to construct a probabilistic finite state automata (PFSA) to pair inbound and outbound lanes at the intersection.
* A homotopy model is used to determine lane edges at the intersection.

== Potential Applications ==
This technology has potential applications in various areas, including:

* Autonomous vehicles: The method can be used to improve the perception and understanding of road edges and lane markings, which is crucial for autonomous vehicles to navigate intersections safely.
* Traffic management: By accurately detecting road edges and classifying vehicle trajectories, the method can help optimize traffic flow and improve intersection management.
* Urban planning: The data obtained from this method can be used to analyze traffic patterns and make informed decisions for urban planning and infrastructure development.

== Problems Solved ==
The method addresses several problems related to road edge detection and intersection analysis:

* Accurate road edge detection: By utilizing aerial imagery data and GANs, the method can accurately detect road edges, even in complex intersection scenarios.
* Vehicle trajectory classification: The use of RFC allows for the classification of vehicle trajectories with unique maneuver labels, providing valuable information for intersection analysis.
* Lane pairing at intersections: The construction of a PFSA using maneuver labeling and road edges helps pair inbound and outbound lanes, enabling a better understanding of intersection dynamics.

== Benefits ==
The technology offers several benefits:

* Improved intersection safety: Accurate road edge detection and vehicle trajectory classification can enhance the safety of intersections, especially for autonomous vehicles.
* Enhanced traffic flow: By optimizing lane pairing and intersection management, the method can contribute to smoother traffic flow and reduced congestion.
* Efficient urban planning: The data obtained from this method can provide valuable insights for urban planners, helping them make informed decisions for infrastructure development and traffic management.


==Original Abstract Submitted==

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 homotopy model.



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