A novel framework for adaptive stress testing of autonomous vehicles in highways

Guaranteeing the safe operations of autonomous vehicles (AVs) is crucial for their widespread adoption and public acceptance. It is thus of a great significance to not only assess the AV against the standard safety tests, but also discover potential corner cases of the AV under test that could lead to unsafe behaviour or scenario. In this paper, we propose a novel framework to systematically explore corner cases that can result in safety concerns in a highway traffic scenario. The framework is based on an adaptive stress testing (AST) approach, an emerging validation method that leverages a Markov decision process to formulate the scenarios and deep reinforcement learning (DRL) to discover the desirable patterns representing corner cases. To this end, we develop a new reward function for DRL to guide the AST in identifying crash scenarios based on the collision probability estimate between the AV under test (i.e., the ego vehicle) and the trajectory of other vehicles on the highway. The proposed framework is further integrated with a new driving model enabling us to create more realistic traffic scenarios capturing both the longitudinal and lateral movements of vehicles on the highway. In our experiment, we calibrate our model using real-world crash statistics involving automated vehicles in California, and then we analyze the characteristics of the AV and the framework. Quantitative and qualitative analyses of our experimental results demonstrate that our framework outperforms other existing AST schemes. The study can help discover crash scenarios of AV that are unknown or absent in human driving, thereby enhancing the safety and trustworthiness of AV technology.

Can ChatGPT advance software testing intelligence? An experience report on metamorphic testing

While ChatGPT is a well-known artificial intelligence chatbot being used to answer human's questions, one may want to discover its potential in advancing software testing. We examine the capability of ChatGPT in advancing the intelligence of software testing through a case study on metamorphic testing (MT), a state-of-the-art software testing technique. We ask ChatGPT to generate candidates of metamorphic relations (MRs), which are basically necessary properties of the object program and which traditionally require human intelligence to identify. These MR candidates are then evaluated in terms of correctness by domain experts. We show that ChatGPT can be used to generate new correct MRs to test several software systems. Having said that, the majority of MR candidates are either defined vaguely or incorrect, especially for systems that have never been tested with MT. ChatGPT can be used to advance software testing intelligence by proposing MR candidates that can be later adopted for implementing tests; but human intelligence should still inevitably be involved to justify and rectify their correctness.

An Empirical Testing of Autonomous Vehicle Simulator System for Urban Driving

Safety is one of the main challenges that prohibit autonomous vehicles (AV), requiring them to be well tested ahead of being allowed on the road. In comparison with road tests, simulators allow us to validate the AV conveniently and affordably. However, it remains unclear how to best use the AV-based simulator system for testing effectively. Our paper presents an empirical testing of AV simulator system that combines the SVL simulator and the Apollo platform. We propose 576 test cases which are inspired by four naturalistic driving situations with pedestrians and surrounding cars. We found that the SVL can imitate realistic safe and collision situations; and at the same time, Apollo can drive the car quite safely. On the other hand, we noted that the system failed to detect pedestrians or vehicles on the road in three out of four classes, accounting for 10.0% total number of scenarios tested. We further applied metamorphic testing to identify inconsistencies in the system with additional 486 test cases. We then discussed some insights into the scenarios that may cause hazardous situations in real life. In summary, this paper provides a new empirical evidence to strengthen the assertion that the simulator-based system can be an indispensable tool for a comprehensive testing of the AV.