Retro-Reflection Metrology for Autonomous Vehicle Optical Simulation
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately simulating real-world driving environments due to the lack of available retro-reflectivity data for materials encountered in typical driving scenarios, which affects the precision of their sensing systems and driving algorithms.
Innovation Solution
Collecting and utilizing retro-reflection data in a laboratory setting to create high-fidelity simulations of optical sensing data for various materials, allowing for the generation of accurate simulated optical data that mimics real-world conditions, enabling improved autonomous vehicle control systems and driving path determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional simulation methods are used without retro-reflectivity data, then the simulation can be run with available data, but the accuracy and realism of the optical sensing simulation deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-measuring and storing retro-reflectivity data for various materials in a laboratory setting before actual autonomous driving scenarios. This pre-collected data is then integrated into simulation environments, allowing the simulation to accurately reproduce real-world optical sensing conditions without requiring real-time data collection during driving tests.
2Reliability
If more realistic simulation environments are created using retro-reflection data, then the testing capability improves, but the complexity of data collection and processing increases
Solution Approach 1:
The patent applies copying by creating virtual copies of real-world materials and their optical properties within the simulation environment. Instead of physically testing every material condition, the system creates digital replicas of materials with measured retro-reflectivity characteristics, allowing comprehensive testing of autonomous vehicle sensing systems in diverse virtual scenarios without proportional increases in physical test complexity.
3Measurement precision
If simulations use accurate material properties from laboratory measurements, then the precision of driving algorithm testing improves, but the time and resources required for data collection increase
Solution Approach 1:
The patent applies preliminary action by conducting comprehensive laboratory measurements of retro-reflectivity data for various materials in advance, before deployment in autonomous driving simulations. This pre-collected database of material properties eliminates the need for repeated real-time measurements, allowing accurate simulation testing without recurring time investments in laboratory work.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the safety and efficiency of autonomous vehicle operations by providing realistic simulation environments that can test and improve the vehicle's control systems, even in challenging or unsafe real-world conditions, through the use of retro-reflection data-based simulations.
Implementation Method 1
adjusting a characteristic of the plurality of simulated reflected beams based on a motion of the representation of the autonomous vehicle between the first location and a second location relative to the one or more objects
Data Source
AI summary
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.