Adversarial Object Injection for Autonomous Driving Model Diagnostics
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Solution Overview
Problem
Current visual analytics solutions for autonomous driving focus primarily on object detection, and it is challenging to evaluate and diagnose when and why semantic segmentation models fail to detect critical objects, especially in context-dependent locations, due to the complexity of massive datasets and the need to quickly identify failure cases and their root causes.
Innovation Solution
A computer-implemented method and system that utilizes a context-aware spatial representation machine learning model to derive the spatial distribution of movable objects and a spatial adversarial machine learning model to generate unseen objects, which are then moved to different locations to fail the object-detecting model, allowing for interactive user interface analysis of model performance with and without the unseen objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep convolutional neural networks are used for semantic segmentation in autonomous driving, then object detection accuracy is improved, but model robustness and reliability deteriorate due to safety concerns and difficulty in evaluating model performance under unseen conditions
Solution Approach 1:
The system performs preliminary actions by generating adversarial examples and conducting robustness evaluations before deploying the model to autonomous vehicles. This allows potential vulnerabilities to be identified and addressed in advance, ensuring the model can handle unseen driving scenes reliably before real-world deployment
Solution Approach 2:
The system applies preliminary anti-action by using adversarial examples to proactively attack and stress-test the model before deployment. This preemptive approach identifies weaknesses and potential failure modes, allowing the model to be hardened against attacks and edge cases before encountering them in real autonomous driving scenarios
2Measurement precision
If thorough evaluation of model accuracy over numerous semantic classes and data sources is performed, then understanding of model failure conditions is improved, but system complexity and time consumption worsen
Solution Approach 1:
The system creates simplified copies of complex evaluation scenarios by generating synthetic adversarial examples that replicate failure conditions. Instead of manually creating and evaluating countless real-world edge cases, the system copies and generalizes failure patterns through adversarial generation, dramatically reducing evaluation complexity while maintaining thoroughness
Solution Approach 2:
The system changes evaluation parameters by transitioning from exhaustive manual testing across numerous semantic classes to automated adversarial generation that systematically varies input parameters. This allows comprehensive evaluation of model robustness across diverse conditions without proportionally increasing system complexity, as the automated parameter variation handles the combinatorial explosion of test cases
3Reliability
If identification and understanding of model vulnerabilities is performed to improve robustness, then model reliability is improved, but time consumption and computational resources worsen
Solution Approach 1:
The system implements periodic action by conducting robustness evaluations at regular intervals during model development and before deployment. Adversarial examples are generated periodically to stress-test the model, allowing continuous monitoring and improvement of robustness without requiring constant exhaustive testing, thus balancing reliability improvement with time efficiency
Solution Approach 2:
The system applies self-service by implementing automated adversarial generation and robustness evaluation that requires minimal human intervention. The system autonomously identifies vulnerabilities, generates targeted adversarial examples, and evaluates model performance, reducing the time and computational resources required compared to manual vulnerability assessment methods
Data Source
AI summary
Embodiments of systems and methods for diagnosing an object-detecting machine learning model for autonomous driving are disclosed herein. An input image is received from a camera mounted in or on a vehicle that shows a scene. A spatial distribution of movable objects within the scene is derived using a context-aware spatial representation machine learning model. An unseen object is generated in the scene that is not originally in the input image utilizing a spatial adversarial machine learning model. Via the spatial adversarial machine learning model, the unseen object is moved to different locations to fail the object-detecting machine learning model. An interactive user interface enables a user to analyze performance of the object-detecting machine learning model with respect to the scene without the unseen object and the scene with the unseen object.