Related papers: Single and Multi-objective Test Cases Prioritizati…
Simulation platforms facilitate the development of emerging Cyber-Physical Systems (CPS) like self-driving cars (SDC) because they are more efficient and less dangerous than field operational test cases. Despite this, thoroughly testing…
Abstract Simulation platforms facilitate the development of emerging cyber-physical systems (CPS) like self-driving cars (SDC) because they are more efficient and less dangerous than field operational tests. Despite this, thoroughly testing…
Simulation environments are essential for the continuous development of complex cyber-physical systems such as self-driving cars (SDCs). Previous results on simulation-based testing for SDCs have shown that many automatically generated…
Safe deployment of self-driving cars (SDC) necessitates thorough simulated and in-field testing. Most testing techniques consider virtualized SDCs within a simulation environment, whereas less effort has been directed towards assessing…
Software metrics such as coverage and mutation scores have been extensively explored for the automated quality assessment of test suites. While traditional tools rely on such quantifiable software metrics, the field of self-driving cars…
The rise of self-driving cars (SDCs) presents important safety challenges to address in dynamic environments. While field testing is essential, current methods lack diversity in assessing critical SDC scenarios. Prior research introduced…
This paper demonstrates the applicability of the safe model predictive control (SMPC) framework to autonomous driving scenarios, focusing on the design of adaptive cruise control (ACC) and automated lane-change systems. Building on the SMPC…
Autonomous Driving Assistance Systems (ADAS) rely on extensive testing to ensure safety and reliability, yet road scenario datasets often contain redundant cases that slow down the testing process without improving fault detection. To…
Simulation-based testing represents an important step to ensure the reliability of autonomous driving software. In practice, when companies rely on third-party general-purpose simulators, either for in-house or outsourced testing, the…
Regression testing of software is a crucial but time-consuming task, especially in the context of user interface (UI) testing where multiple microservices must be validated simultaneously. Test case prioritization (TCP) is a cost-efficient…
When developing autonomous driving systems (ADS), developers often need to replay previously collected driving recordings to check the correctness of newly introduced changes to the system. However, simply replaying the entire recording is…
There is a growing body of research indicating the potential of machine learning to tackle complex software testing challenges. One such challenge pertains to continuous integration testing, which is highly time-constrained, and generates a…
Testing Highly Configurable Systems (HCSs) is a challenging task, especially in an evolution scenario where features are added, changed, or removed, which hampers test case selection and prioritization. Existing work is usually based on the…
An open question in autonomous driving is how best to use simulation to validate the safety of autonomous vehicles. Existing techniques rely on simulated rollouts, which can be inefficient for finding rare failure events, while other…
Self-driving cars require extensive testing, which can be costly in terms of time. To optimize this process, simple and straightforward tests should be excluded, focusing on challenging tests instead. This study addresses the test selection…
Stochastic Optimal Control (SOC) problems arise in systems influenced by uncertainty, such as autonomous robots or financial models. Traditional methods like dynamic programming are often intractable for high-dimensional, nonlinear systems…
Off-road autonomy validation presents unique challenges due to the unpredictable and dynamic nature of off-road environments. Traditional methods focusing on sequentially sweeping across the parameter space for variability analysis struggle…
Rigorous Verification and Validation (V&V) of Autonomous Driving Functions (ADFs) is paramount for ensuring the safety and public acceptance of Autonomous Vehicles (AVs). Current validation relies heavily on simulation to achieve sufficient…
Continuous integration testing is an important step in the modern software engineering life cycle. Test prioritization is a method that can improve the efficiency of continuous integration testing by selecting test cases that can detect…
Discovering potential failures of an autonomous system is important prior to deployment. Falsification-based methods are often used to assess the safety of such systems, but the cost of running many accurate simulation can be high. The…