Lane Detection Error Modeling for Autonomous Vehicle Testing
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Solution Overview
Problem
Current methods for testing autonomous vehicle safety are inefficient and fail to accurately simulate realistic perception outputs, particularly lane detection, due to the sensitivity of perception components like CNNs to simulated data quality and the difficulty in modeling sensor data such as RADAR, leading to high computational costs and limited generalizability to real-world scenarios.
Innovation Solution
The use of Perception Statistical Performance Models (PSPMs) to model probabilistic uncertainty distributions based on actual perception outputs, simulating realistic lane detection errors by applying learned perturbation models that impose correlations between neighboring lane border points, allowing for efficient and realistic simulation of lane detection outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If photorealistic simulation is used to model sensor data like RADAR, then realism of simulation is improved, but computational complexity and difficulty of modeling increase significantly
Solution Approach 1:
The patent creates simplified copies of perception outputs that replicate the statistical properties and error characteristics of real perception systems without copying the entire complex perception pipeline. These synthetic perception outputs are generated by applying learned perturbation models to ground truth data, capturing essential realism while avoiding the computational burden of full photorealistic simulation.
Solution Approach 2:
The patent transforms the simulation approach by changing from modeling physical sensor data (which is computationally intensive) to modeling perception output parameters directly. By learning perturbation parameters from real perception errors and applying them to ground truth, the system achieves realistic simulation with significantly reduced computational complexity.
2Reliability
If full photorealistic simulation of the entire AV stack is used, then perception realism is improved, but computational cost and processing time increase enormously
Solution Approach 1:
The patent extracts only the essential perceptual uncertainty and error characteristics from real perception systems, separating these from the full perception pipeline. By taking out just the relevant statistical properties and error patterns, the system achieves realistic simulation without the computational overhead of running complete perception stacks.
Solution Approach 2:
The patent segments the simulation task into two parts: generating ground truth data and applying learned perturbation models to create synthetic perception outputs. This segmentation allows the system to achieve realistic perception simulation without needing to simulate the entire complex perception pipeline, thereby improving computational efficiency.
3Measurement precision
If CNN-based perception components are used in simulation, then detection accuracy is improved, but sensitivity to simulated data quality increases, requiring exceptionally high-quality simulated image data
Solution Approach 1:
The patent applies beforehand cushioning by pre-learning perturbation models from real perception errors and incorporating them into the simulation framework. This prepares the synthetic perception outputs to naturally include realistic error patterns, cushioning against the sensitivity of CNNs to data quality issues without requiring exceptionally high-quality simulated images.
4Reliability
If real-world test miles are increased to reduce errors per decision, then safety level is improved, but time and cost increase significantly
Solution Approach 1:
The patent creates synthetic copies of real-world perception errors and scenarios through learned perturbation models. By generating realistic failure modes and edge cases in simulation, the system can achieve comprehensive safety testing without requiring proportionally large numbers of real-world test miles, significantly reducing testing time and cost.
Data Source
AI summary
A computer-implemented method of generating lane detector outputs, the method comprising receiving a ground truth lane image containing one or more ground truth lane borders, each ground truth lane border comprising multiple border points; and generating a lane detector output image, by applying horizontal perturbations to the multiple border points of each ground truth lane border, the horizontal perturbations determined using a learned perturbation model, the learned perturbation model constructed to impose mutual correlation in the horizontal perturbations between vertically neighbouring border points of each ground truth lane border, and comprising parameters learned by performing a statistical analysis of lane detector errors computed between computed output images of a modelled lane detector and ground truth lane border annotations corresponding to the computed output images.