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CooTest: An Automated Testing Approach for V2X Communication Systems

An Guo, Xinyu Gao, Zhenyu Chen, Yuan Xiao, Jiakai Liu, Xiuting Ge, Weisong Sun, Chunrong Fang

TL;DR

CooTest introduces an automated metamorphic-testing framework tailored to V2X cooperative perception, addressing the gap between single-vehicle robustness and multi-agent communication challenges. It defines Misleading Cooperation Error (MCE) and formalizes CP and ego perception, then employs seven transformation operators (communication and weather) along with a V2X-oriented guided transformation to efficiently generate test scenes. Empirical results across six CP models show that VGT improves fault detection and that retraining with transformed data enhances AP and reduces MCE, though some risks persist due to data and modeling complexity. The work demonstrates practical benefits for validating and robustifying V2X perception systems, with open-source resources to support continued development and evaluation.

Abstract

Perceiving the complex driving environment precisely is crucial to the safe operation of autonomous vehicles. With the tremendous advancement of deep learning and communication technology, Vehicle-to-Everything (V2X) collaboration has the potential to address limitations in sensing distant objects and occlusion for a single-agent perception system. However, despite spectacular progress, several communication challenges can undermine the effectiveness of multi-vehicle cooperative perception. The low interpretability of Deep Neural Networks (DNNs) and the high complexity of communication mechanisms make conventional testing techniques inapplicable for the cooperative perception of autonomous driving systems (ADS). Besides, the existing testing techniques, depending on manual data collection and labeling, become time-consuming and prohibitively expensive. In this paper, we design and implement CooTest, the first automated testing tool of the V2X-oriented cooperative perception module. CooTest devises the V2X-specific metamorphic relation and equips communication and weather transformation operators that can reflect the impact of the various cooperative driving factors to produce transformed scenes. Furthermore, we adopt a V2X-oriented guidance strategy for the transformed scene generation process and improve testing efficiency. We experiment CooTest with multiple cooperative perception models with different fusion schemes to evaluate its performance on different tasks. The experiment results show that CooTest can effectively detect erroneous behaviors under various V2X-oriented driving conditions. Also, the results confirm that CooTest can improve detection average precision and decrease misleading cooperation errors by retraining with the generated scenes.

CooTest: An Automated Testing Approach for V2X Communication Systems

TL;DR

CooTest introduces an automated metamorphic-testing framework tailored to V2X cooperative perception, addressing the gap between single-vehicle robustness and multi-agent communication challenges. It defines Misleading Cooperation Error (MCE) and formalizes CP and ego perception, then employs seven transformation operators (communication and weather) along with a V2X-oriented guided transformation to efficiently generate test scenes. Empirical results across six CP models show that VGT improves fault detection and that retraining with transformed data enhances AP and reduces MCE, though some risks persist due to data and modeling complexity. The work demonstrates practical benefits for validating and robustifying V2X perception systems, with open-source resources to support continued development and evaluation.

Abstract

Perceiving the complex driving environment precisely is crucial to the safe operation of autonomous vehicles. With the tremendous advancement of deep learning and communication technology, Vehicle-to-Everything (V2X) collaboration has the potential to address limitations in sensing distant objects and occlusion for a single-agent perception system. However, despite spectacular progress, several communication challenges can undermine the effectiveness of multi-vehicle cooperative perception. The low interpretability of Deep Neural Networks (DNNs) and the high complexity of communication mechanisms make conventional testing techniques inapplicable for the cooperative perception of autonomous driving systems (ADS). Besides, the existing testing techniques, depending on manual data collection and labeling, become time-consuming and prohibitively expensive. In this paper, we design and implement CooTest, the first automated testing tool of the V2X-oriented cooperative perception module. CooTest devises the V2X-specific metamorphic relation and equips communication and weather transformation operators that can reflect the impact of the various cooperative driving factors to produce transformed scenes. Furthermore, we adopt a V2X-oriented guidance strategy for the transformed scene generation process and improve testing efficiency. We experiment CooTest with multiple cooperative perception models with different fusion schemes to evaluate its performance on different tasks. The experiment results show that CooTest can effectively detect erroneous behaviors under various V2X-oriented driving conditions. Also, the results confirm that CooTest can improve detection average precision and decrease misleading cooperation errors by retraining with the generated scenes.
Paper Structure (27 sections, 9 equations, 4 figures, 4 tables, 1 algorithm)

This paper contains 27 sections, 9 equations, 4 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Cooperative Driving Systems
  3. V2X in Autonomous Driving
  4. V2X Cooperative Perception with On-road Agents
  5. Approach
  6. Formalization and Definition
  7. Formal Representation
  8. Misleading Cooperation Error (MCE)
  9. Metamorphic Testing for Cooperative Perception System
  10. Transformation Operators
  11. Communication Transformation
  12. Weather Transformation
  13. V2X-oriented Guided Transformation
  14. Experimental Design
  15. Dataset and Cooperative Perception Models
  16. ...and 12 more sections

Figures (4)

  • Figure 1: The architecture of cooperative perception systems in autonomous driving.
  • Figure 2: The Workflow of CooTest.
  • Figure 3: Testing results on different models with seven transformation operators.
  • Figure 4: Visualization samples showing misleading cooperation errors detected by CooTest using transformed scenes. The cars detected by the ego vehicle are marked in green, while the cars detected by cooperative perception systems are marked in red.