Unified Visualization for Autonomous Vehicle Perception Error Assessment
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Solution Overview
Problem
Current methods for evaluating the performance of autonomous vehicle systems and trajectory planners lack efficiency in identifying perception errors and their impact on driving performance, especially in real or simulated scenarios.
Innovation Solution
A computer system and method for testing real-time perception systems in autonomous vehicles, which includes data input for real-world driving runs, a rendering component for generating graphical user interfaces, a ground truthing pipeline for processing sensor data, and a perception oracle for comparing run-time perception outputs with ground-truth outputs to identify perception errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional separate evaluation methods are used for perception errors and driving performance, then evaluation coverage is comprehensive, but evaluation efficiency is low and correlation analysis is difficult
Solution Approach 1:
The patent combines separate evaluation processes for perception errors and driving performance into a single unified evaluation system. The evaluation module simultaneously assesses both perception outputs and driving performance metrics, and the visualization module presents them in an integrated timeline view, enabling efficient correlation analysis without losing information about their relationships
Solution Approach 2:
The unified evaluation system serves multiple functions: it evaluates perception errors, assesses driving performance, and analyzes their correlations all through a single integrated platform. This multi-functional approach improves productivity while maintaining comprehensive evaluation coverage
2Measurement precision
If detailed perception error analysis is performed, then identification accuracy improves, but analysis complexity increases
Solution Approach 1:
The evaluation system segments the complex analysis task into distinct functional modules: a perception evaluation module that identifies perception errors with high accuracy, a driving performance evaluation module that assesses performance metrics, and a visualization module that presents results. This segmentation maintains measurement precision while managing system complexity through modular design
3Reliability
If extensive driving data is collected to ensure safety levels, then safety guarantee improves, but data processing time and resources increase
Solution Approach 1:
The system performs preliminary evaluation of perception errors and driving performance on collected data using automated evaluation criteria and rules. This preliminary action quickly identifies safety-relevant issues without requiring extensive manual analysis of all collected data, thereby maintaining high safety guarantees while reducing processing time
Data Source
AI summary
A computer-implemented method for assessing autonomous vehicle performance comprising receiving, at an input, performance data of at least one autonomous driving run, the performance data comprising at least one time series of perception errors and at least one time series of driving performance results; and generating, at a rendering component, rendering data for rendering a graphical user interface, the graphical user interface for visualizing the performance data and comprising: a perception error timeline, and a driving assessment timeline, wherein the timelines are aligned in time, and divided into multiple time steps of the at least one driving run, wherein, for each time step: the perception timeline comprises a visual indication of whether a perception error occurred at that time step, and the driving assessment timeline comprises a visual indication of driving performance at that time step.