Waymo llc (20240375684). PULL-OVER LOCATION SELECTION USING MACHINE LEARNING simplified abstract

PULL-OVER LOCATION SELECTION USING MACHINE LEARNING

Organization Name

waymo llc

Inventor(s)

Jonathan Lee Pedersen of Brooklyn NY (US)

Yu Zheng of Milpitas CA (US)

Eamonn Michael Doherty of Mountain View CA (US)

Brian Clair Williammee of Santa Cruz CA (US)

Kevin Joseph Malta of San Francisco CA (US)

Chung Eun Kim of San Mateo CA (US)

Xu Dong of Sunnyvale CA (US)

PULL-OVER LOCATION SELECTION USING MACHINE LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240375684 titled 'PULL-OVER LOCATION SELECTION USING MACHINE LEARNING

The patent application describes methods, systems, and apparatus for selecting a pull-over location using machine learning for autonomous vehicles.

• Data specifying a target pull-over location is obtained for an autonomous vehicle on a roadway.
• Candidate pull-over locations in the vicinity of the target location are identified.
• Features of each candidate location are processed using a machine learning model to generate a likelihood score.
• The likelihood score represents the predicted optimal location for the vehicle to pull over.
• One candidate pull-over location is selected based on the likelihood scores as the actual pull-over location for the autonomous vehicle.

1. 1. 1. Potential Applications:

This technology can be applied in autonomous vehicles to improve decision-making processes for selecting safe and optimal pull-over locations.

1. 1. 1. Problems Solved:

This technology addresses the challenge of efficiently and accurately selecting suitable pull-over locations for autonomous vehicles to ensure safety and convenience.

1. 1. 1. Benefits:

- Enhances the safety and efficiency of autonomous vehicles on roadways. - Reduces the risk of accidents by selecting optimal pull-over locations. - Improves the overall driving experience for passengers in autonomous vehicles.

1. 1. 1. Commercial Applications:

Title: Autonomous Vehicle Safety Enhancement System This technology can be utilized by companies developing autonomous vehicles to enhance safety features and improve the overall driving experience for customers. It can also be integrated into existing autonomous vehicle systems to optimize pull-over location selection.

1. 1. 1. Prior Art:

Readers can explore prior research on machine learning applications in autonomous vehicles and road safety systems to understand the evolution of this technology.

1. 1. 1. Frequently Updated Research:

Stay updated on advancements in machine learning algorithms for autonomous vehicles and road safety systems to leverage the latest innovations in pull-over location selection technology.

1. 1. 1. 1. Questions about Autonomous Vehicle Safety Enhancement System:

1. How does machine learning improve the selection of pull-over locations for autonomous vehicles?

  - Machine learning processes features of candidate locations to predict optimal pull-over spots based on a likelihood score.

2. What are the key benefits of using machine learning in autonomous vehicles for selecting pull-over locations?

  - The technology enhances safety, efficiency, and overall driving experience for passengers in autonomous vehicles.

Original Abstract Submitted

methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting a pull-over location using machine learning. one of the methods includes obtaining data specifying a target pull-over location for an autonomous vehicle travelling on a roadway. a plurality of candidate pull-over locations in a vicinity of the target pull-over location are identified. for each candidate pull-over location, an input that includes features of the candidate pull-over location is processed using a machine learning model to generate a respective likelihood score representing a predicted likelihood that the candidate pull-over location is an optimal location. the features of the candidate pull-over location include one or more features that compare the candidate pull-over location to the target pull-over location. using the respective likelihood scores, one of the candidate pull-over locations is selected as an actual pull-over location for the autonomous vehicle.