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Recent advances in Deep Neural Networks (DNNs) have led to the development ofDNN-driven autonomous cars that, using sensors like camera, LiDAR, etc., candrive without any human intervention. Most major manufacturers including Tesla,GM, Ford, BMW, and Waymo/Google are working on building and testing differenttypes of autonomous vehicles. The lawmakers of several US states includingCalifornia, Texas, and New York have passed new legislation to fast-track theprocess of testing and deployment of autonomous vehicles on their roads.  However, despite their spectacular progress, DNNs, just like traditionalsoftware, often demonstrate incorrect or unexpected corner case behaviors thatcan lead to potentially fatal collisions. Several such real-world accidentsinvolving autonomous cars have already happened including one which resulted ina fatality. Most existing testing techniques for DNN-driven vehicles areheavily dependent on the manual collection of test data under different drivingconditions which become prohibitively expensive as the number of testconditions increases.  In this paper, we design, implement and evaluate DeepTest, a systematictesting tool for automatically detecting erroneous behaviors of DNN-drivenvehicles that can potentially lead to fatal crashes. First, our tool isdesigned to automatically generated test cases leveraging real-world changes indriving conditions like rain, fog, lighting conditions, etc. DeepTestsystematically explores different parts of the DNN logic by generating testinputs that maximize the numbers of activated neurons. DeepTest found thousandsof erroneous behaviors under different realistic driving conditions (e.g.,blurring, rain, fog, etc.) many of which lead to potentially fatal crashes inthree top performing DNNs in the Udacity self-driving car challenge.

DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars