Road Object Matching Between Sensor Data and AV Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The manual process of pairing real-world scene features and objects with their simulated counterparts in autonomous vehicle simulations is time-consuming and inefficient, necessitating an automated method to identify and validate simulated representations in simulation data using real-world road data.
Innovation Solution
The use of machine learning models to compare and pair objects in real-world road data with their simulated representations in simulation data, determining differences and discrepancies, and updating simulation data to improve realism and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual pairing process is used to match real-world objects with simulated representations, then accuracy of validation can be maintained, but time consumption and processing efficiency deteriorate
Solution Approach 1:
The patent replaces the manual mechanical pairing process with an automated machine learning system. The ML model automatically compares sensor data from real-world objects with simulated representations, identifying matches without human intervention. This substitution maintains validation accuracy while dramatically reducing the time required for the pairing process.
Solution Approach 2:
The system enables self-service automation where the machine learning model independently performs the object pairing and validation tasks. The automated system compares features, identifies matches, and validates simulated representations without requiring manual human effort, thereby eliminating time loss while preserving accuracy through algorithmic precision.
2Productivity
If automated machine learning methods are used to identify objects in road data, then productivity and efficiency are improved, but system complexity increases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component that bridges real-world sensor data and simulated representations. This intermediary automatically performs the complex comparison and matching tasks, improving productivity by handling large volumes of data efficiently. The added complexity is localized to the ML model, which streamlines the overall system workflow.
3Measurement precision
If detailed comparison of objects is performed to ensure accurate matching, then measurement precision is improved, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by focusing the machine learning model's comparison on key distinguishing features of objects rather than analyzing every detail. The system identifies and compares critical attributes that are sufficient for accurate matching, achieving high precision without the computational overhead of exhaustive detailed analysis. This selective approach reduces energy consumption while maintaining matching accuracy.
Data Source
AI summary
Systems and techniques are provided for identifying an object in road data corresponding to a simulated representation in simulation data using machine learning. An example method includes receiving simulation data descriptive of one or more assets, wherein the one or more assets are synthetic representations of an object in a simulation scene. The example method further includes receiving sensor data collected by one or more sensors of an autonomous vehicle (AV) while navigating in a real-world environment, identifying an object in the sensor data using a machine learning model, the object corresponding to an asset of the one or more assets in the simulation data, determining a difference between the asset in the simulation data and the object in the sensor data, and based on the difference, determining whether to modify at least one of the asset in the simulation scene and one or more synthetic sensors.