面向自动驾驶的软件在环仿真与物理测试的相关性研究

DeepRoad: GAN-based Metamorphic Autonomous Driving System Testing

While Deep Neural Networks (DNNs) have established the fundamentals of DNN-based autonomous driving systems, they may exhibit erroneous behaviors and cause fatal accidents. To resolve the safety issues of autonomous driving systems, a recent set of testing techniques have been designed to automatically generate test cases, e.g., new input images transformed from the original ones. Unfortunately, many such generated input images often render inferior authenticity, lacking accurate semantic information of the driving scenes and hence compromising the resulting efficacy and reliability. In this p…

基于模型的自动驾驶软件开发中考虑并行化

Model-based Development for Autonomous Driving Software Considering Parallelization

本文提出了一种基于模型的开发方法，用于提高自动驾驶软件的并行化和实时性能。

Testing of Autonomous Driving Systems: Where Are We and Where Should We Go?

Autonomous driving has shown great potential to reform modern transportation. Yet its reliability and safety have drawn a lot of attention and concerns. Compared with traditional software systems, autonomous driving systems (ADSs) often use deep neural networks in tandem with logic-based modules. This new paradigm poses unique challenges for software testing. Despite the recent development of new ADS testing techniques, it is not clear to what extent those techniques have addressed the needs of ADS practitioners. To fill this gap, we present the first comprehensive study to identify the curren…

A Declarative Metamorphic Testing Framework for Autonomous Driving

Autonomous driving has gained much attention from both industry and academia. Currently, Deep Neural Networks (DNNs) are widely used for perception and control in autonomous driving. However, several fatal accidents caused by autonomous vehicles have raised serious safety concerns about autonomous driving models. Some recent studies have successfully used the metamorphic testing technique to detect thousands of potential issues in some popularly used autonomous driving models. However, prior study is limited to a small set of metamorphic relations, which do not reflect rich, real-world traffic…

Concepts in Testing of Autonomous Systems: Academic Literature and Industry Practice

Testing of autonomous systems is extremely important as many of them are both safety-critical and security-critical. The architecture and mechanism of such systems are fundamentally different from traditional control software, which appears to operate in more structured environments and are explicitly instructed according to the system design and implementation. To gain a better understanding of autonomous systems practice and facilitate research on testing of such systems, we conducted an exploratory study by synthesizing academic literature with a focus group discussion and interviews with i…

ADPerf：自动驾驶系统中的性能调查与测试

ADPerf: Investigating and Testing Performance in Autonomous Driving Systems

针对自动驾驶系统中障碍物检测延迟问题，首次对Apollo和Autoware的检测模块进行性能测量与建模，提出ADPerf工具生成点云测试用例以暴露高延迟，并验证其对轨迹预测模块的传播影响，强调性能测试必要性。