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DeepTest: automated testing of deep-neural-network-driven autonomous cars

Tian, Yuchi ; Pei, Kexin ; Jana, Suman ; Ray, Baishakhi

2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE), 2018, p.303-314

New York, NY, USA: ACM

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Título:
DeepTest: automated testing of deep-neural-network-driven autonomous cars
Autor: Tian, Yuchi ; Pei, Kexin ; Jana, Suman ; Ray, Baishakhi
Assuntos: Automobiles ; Autonomous automobiles ; autonomous vehicle ; Computer architecture ; Computing methodologies -- Machine learning -- Machine learning approaches -- Neural networks ; deep learning ; deep neural networks ; neuron coverage ; Neurons ; Security and privacy -- Software and application security ; self-driving cars ; Sensors ; Software ; Software and its engineering -- Software creation and management -- Software verification and validation -- Software defect analysis -- Software testing and debugging ; Testing
É parte de: 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE), 2018, p.303-314
Descrição: Recent advances in Deep Neural Networks (DNNs) have led to the development of DNN-driven autonomous cars that, using sensors like camera, LiDAR, etc., can drive without any human intervention. Most major manufacturers including Tesla, GM, Ford, BMW, and Waymo/Google are working on building and testing different types of autonomous vehicles. The lawmakers of several US states including California, Texas, and New York have passed new legislation to fast-track the process of testing and deployment of autonomous vehicles on their roads. However, despite their spectacular progress, DNNs, just like traditional software, often demonstrate incorrect or unexpected corner-case behaviors that can lead to potentially fatal collisions. Several such real-world accidents involving autonomous cars have already happened including one which resulted in a fatality. Most existing testing techniques for DNN-driven vehicles are heavily dependent on the manual collection of test data under different driving conditions which become prohibitively expensive as the number of test conditions increases. In this paper, we design, implement, and evaluate DeepTest, a systematic testing tool for automatically detecting erroneous behaviors of DNN-driven vehicles that can potentially lead to fatal crashes. First, our tool is designed to automatically generated test cases leveraging real-world changes in driving conditions like rain, fog, lighting conditions, etc. DeepTest systematically explore different parts of the DNN logic by generating test inputs that maximize the numbers of activated neurons. DeepTest found thousands of erroneous behaviors under different realistic driving conditions (e.g., blurring, rain, fog, etc.) many of which lead to potentially fatal crashes in three top performing DNNs in the Udacity self-driving car challenge.
Editor: New York, NY, USA: ACM
Idioma: Inglês

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DeepTest: automated testing of deep-neural-network-driven autonomous cars

Tian, Yuchi ; Pei, Kexin ; Jana, Suman ; Ray, Baishakhi

New York, NY, USA: ACM

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