Waymo LLC (20240281575). HIGH FIDELITY SIMULATIONS FOR AUTONOMOUS VEHICLES BASED ON RETRO-REFLECTION METROLOGY simplified abstract

HIGH FIDELITY SIMULATIONS FOR AUTONOMOUS VEHICLES BASED ON RETRO-REFLECTION METROLOGY

Organization Name

Waymo LLC

Inventor(s)

Arthur Dov Safira of Los Altos CA (US)

Harrison Lee McKenzie Chapter of Santa Clara CA (US)

Colin Andrew Braley of Mountain View CA (US)

Hui Seong Son of Hayward CA (US)

Aleksandar Rumenov Gabrovski of Mountain View CA (US)

Brian Choung Choi of Sunnyvale CA (US)

HIGH FIDELITY SIMULATIONS FOR AUTONOMOUS VEHICLES BASED ON RETRO-REFLECTION METROLOGY - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240281575 titled 'HIGH FIDELITY SIMULATIONS FOR AUTONOMOUS VEHICLES BASED ON RETRO-REFLECTION METROLOGY

The abstract of this patent application discusses the implementation of autonomous vehicle simulations based on retro-reflection optical data to address shortcomings in existing technology. The method involves initiating a simulation of an autonomous driving vehicle environment, accessing simulated reflection data and retro-reflectivity data for different material types of simulated objects, and determining a driving path using an autonomous vehicle control system based on the simulated reflection data.

• Autonomous vehicle simulations based on retro-reflection optical data
• Initiating a simulation of an autonomous driving vehicle environment
• Accessing simulated reflection data and retro-reflectivity data for different material types of simulated objects
• Determining a driving path using an autonomous vehicle control system
• Addressing shortcomings in existing technology related to autonomous vehicle simulations

Potential Applications: - Autonomous vehicle testing and development - Training simulations for autonomous vehicle systems - Virtual testing environments for autonomous driving technologies

Problems Solved: - Lack of accurate and realistic simulations for autonomous vehicles - Difficulty in testing autonomous vehicle systems in various scenarios - Limited access to diverse driving environments for training purposes

Benefits: - Improved accuracy and realism in autonomous vehicle simulations - Enhanced training capabilities for autonomous vehicle systems - Increased efficiency in testing and development of autonomous driving technologies

Commercial Applications: Title: "Retro-Reflection Optical Data for Autonomous Vehicle Simulations" This technology could be utilized by automotive companies, tech firms, and research institutions for developing and testing autonomous driving systems. It has the potential to streamline the testing process, improve the accuracy of simulations, and enhance the overall performance of autonomous vehicles in real-world scenarios.

Questions about Retro-Reflection Optical Data for Autonomous Vehicle Simulations: 1. How does retro-reflection optical data improve the accuracy of autonomous vehicle simulations? - Retro-reflection optical data enhances the realism of simulations by providing detailed information on the reflective properties of different materials, allowing for more accurate representation of real-world scenarios. 2. What are the potential challenges in implementing retro-reflection optical data in autonomous vehicle simulations? - Challenges may include the need for accurate calibration of reflection data, integration with existing simulation platforms, and ensuring compatibility with different types of autonomous vehicle systems.

Original Abstract Submitted

aspects and implementations of the present disclosure address shortcomings of existing technology by enabling autonomous vehicle simulations based on retro-reflection optical data. the subject matter of this specification can be implemented in, among other things, a method that involves initiating a simulation of an environment of an autonomous driving vehicle, the simulation including a plurality of simulated objects, each having an identification of a material type of the respective object. the method can further involve accessing simulated reflection data based on the plurality of simulated objects and retro-reflectivity data for the material types of the simulated objects, and determining, using an autonomous vehicle control system for the autonomous vehicle, a driving path relative to the simulated objects, the driving path based on the simulated reflection data.