16 citations · 16 across the 4 of their papers we have counts for
8 papers
Scalpel: Automotive Deep Learning Framework Testing via Assembling Model Components
Yinglong Zou, Juan Zhai, Chunrong Fang +3
Deep learning (DL) plays a key role in autonomous driving systems. DL models support perception modules, equipped with tasks such as object detection and sensor fusion. These DL mo…
When Autonomous Vehicle Meets V2X Cooperative Perception: How Far Are We?
An Guo, Shuoxiao Zhang, Enyi Tang +7
With the tremendous advancement of deep learning and communication technology, Vehicle-to-Everything (V2X) cooperative perception has the potential to address limitations in sensin…
Improving Deep Learning Framework Testing with Model-Level Metamorphic Testing
Yanzhou Mu, Juan Zhai, Chunrong Fang +6
Deep learning (DL) frameworks are essential to DL-based software systems, and framework bugs may lead to substantial disasters, thus requiring effective testing. Researchers adopt…
Generate Realistic Test Scenes for V2X Communication Systems
An Guo, Xinyu Gao, Chunrong Fang +6
Accurately perceiving complex driving environments is essential for ensuring the safe operation of autonomous vehicles. With the tremendous progress in deep learning and communicat…
Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis
Yanzhou Mu, Rong Wang, Juan Zhai +7
Large language models (LLMs) have driven significant progress across a wide range of real-world applications. Realizing such models requires substantial system-level support. Deep…
Testing the Fault-Tolerance of Multi-Sensor Fusion Perception in Autonomous Driving Systems
Haoxiang Tian, Wenqiang Ding, Xingshuo Han +5
High-level Autonomous Driving Systems (ADSs), such as Google Waymo and Baidu Apollo, typically rely on multi-sensor fusion (MSF) based approaches to perceive their surroundings. Th…