Multi-sensor fusion stands as a pivotal technique in addressing numerous safety-critical tasks and applications, e.g., self-driving cars and automated robotic arms. With the continuous advancement in data-driven artificial intelligence (AI), MSF's potential for sensing and understanding intricate external environments has been further amplified, bringing a profound impact on intelligent systems and specifically on their perception systems. Similar to traditional software, adequate testing is also required for AI-enabled MSF systems. Yet, existing testing methods primarily concentrate on single-sensor perception systems (e.g., image-/point cloud-based object detection systems). There remains a lack of emphasis on generating multi-modal test cases for MSF systems. To address these limitations, we design and implement MultiTest, a fitness-guided metamorphic testing method for complex MSF perception systems. MultiTest employs a physical-aware approach to synthesize realistic multi-modal object instances and insert them into critical positions of background images and point clouds. A fitness metric is designed to guide and boost the test generation process. We conduct extensive experiments with five SOTA perception systems to evaluate MultiTest from the perspectives of: (1) generated test cases' realism, (2) fault detection capabilities, and (3) performance improvement. The results show that MultiTest can generate realistic and modality-consistent test data and effectively detect hundreds of diverse faults of an MSF system under test. Moreover, retraining an MSF system on the test cases generated by MultiTest can improve the system's robustness.

翻译：多传感器融合是解决自动驾驶、机械臂控制等安全关键任务的核心技术之一。随着数据驱动人工智能的持续发展，多传感器融合在感知和理解复杂外部环境方面的潜力得到进一步增强，对智能系统特别是其感知系统产生了深远影响。与传统软件类似，基于人工智能的多传感器融合系统同样需要充分的测试。然而，现有测试方法主要集中于单传感器感知系统（例如基于图像/点云的目标检测系统），缺乏针对多传感器融合系统生成多模态测试用例的关注。为弥补这一不足，我们设计并实现了多测试——一种面向复杂多传感器融合感知系统的适应度导向蜕变测试方法。该方法采用物理感知方式合成逼真的多模态物体实例，并将其插入背景图像和点云的关键位置，同时设计了适应度指标引导并加速测试用例生成过程。我们通过五个前沿感知系统开展大量实验，从以下维度评估多测试：（1）生成测试用例的逼真度；（2）故障检测能力；（3）性能提升效果。结果表明，多测试能够生成真实且模态一致的测试数据，有效检测被测多传感器融合系统中数百种不同类型的故障。此外，利用多测试生成的测试用例重新训练多传感器融合系统，可显著提升系统鲁棒性。