Autonomous Perception System Redacting Visual Data for Computational Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face computational challenges in quickly processing large quantities of sensor data from multiple sensors, making it difficult to accurately perceive the surrounding environment and make timely decisions.
Innovation Solution
A perception system that reverse-engineers human driver perception by selectively modifying the visualization of the driving scene using machine vision techniques, allowing the system to focus on meaningful information and reduce computational effort, thereby improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system processes all sensor data from multiple sensors to accurately perceive the surrounding environment, then the perception accuracy is improved, but the computational time and resources increase significantly
Solution Approach 1:
The patent segments the sensor data processing by dividing the surrounding environment into multiple zones (e.g., front, rear, left, right zones) and selectively processing data from different sensors based on the zone of interest. This allows the system to focus computational resources on critical areas while reducing overall processing time.
Solution Approach 2:
The system applies local quality by using different processing strategies for different spatial regions. High-precision processing is applied to critical zones (e.g., front area where pedestrians are most likely), while lower-precision or simplified processing is used for less critical areas, optimizing the balance between accuracy and speed.
2Measurement precision
If the system processes all sensor data from multiple sensors to accurately perceive the surrounding environment, then the perception accuracy is improved, but the computational resources increase significantly
Solution Approach 1:
The patent extracts and processes only the essential and critical sensor data needed for safe autonomous driving decisions. By identifying and removing redundant or less important data elements, the system maintains high perception accuracy while significantly reducing computational resource consumption.
Solution Approach 2:
The system applies partial action by processing a selective subset of sensor data rather than all available data. It focuses computational effort on the most relevant sensors and data points for the current driving context, achieving sufficient perception accuracy with reduced resource usage.
3Measurement precision
If the system uses extensive computations to accurately perceive the surrounding environment, then the perception accuracy is improved, but the decision-making speed decreases
Solution Approach 1:
The patent implements preliminary action by pre-processing and pre-identifying critical data elements and patterns before they are needed for decision-making. The system prepares sensor data in advance, organizing and filtering it so that when decisions are required, the processing time is minimized while maintaining accuracy.
Data Source
AI summary
System, methods, and other embodiments described herein relate to identifying human-based perception techniques for analyzing a driving scene. In one embodiment, a method includes generating the driving scene as a simulated environment of a vehicle. The method includes modifying the simulated environment according to a visualization algorithm that approximates a machine vision technique to transform the simulated environment into a modified environment with redacted information in comparison to the simulated environment. The method includes displaying the modified environment on an electronic display to an operator to assess how the operator perceives the modified environment when operating the vehicle.