31 research outputs found
Complete Agent-driven Model-based System Testing for Autonomous Systems
In this position paper, a novel approach to testing complex autonomous
transportation systems (ATS) in the automotive, avionic, and railway domains is
described. It is intended to mitigate some of the most critical problems
regarding verification and validation (V&V) effort for ATS. V&V is known to
become infeasible for complex ATS, when using conventional methods only. The
approach advocated here uses complete testing methods on the module level,
because these establish formal proofs for the logical correctness of the
software. Having established logical correctness, system-level tests are
performed in simulated cloud environments and on the target system. To give
evidence that 'sufficiently many' system tests have been performed with the
target system, a formally justified coverage criterion is introduced. To
optimise the execution of very large system test suites, we advocate an online
testing approach where multiple tests are executed in parallel, and test steps
are identified on-the-fly. The coordination and optimisation of these
executions is achieved by an agent-based approach. Each aspect of the testing
approach advocated here is shown to either be consistent with existing
standards for development and V&V of safety-critical transportation systems, or
it is justified why it should become acceptable in future revisions of the
applicable standards.Comment: In Proceedings FMAS 2021, arXiv:2110.1152
An Agency-Directed Approach to Test Generation for Simulation-based Autonomous Vehicle Verification
Simulation-based verification is beneficial for assessing otherwise dangerous
or costly on-road testing of autonomous vehicles (AV). This paper addresses the
challenge of efficiently generating effective tests for simulation-based AV
verification using software testing agents. The multi-agent system (MAS)
programming paradigm offers rational agency, causality and strategic planning
between multiple agents. We exploit these aspects for test generation, focusing
in particular on the generation of tests that trigger the precondition of an
assertion. On the example of a key assertion we show that, by encoding a
variety of different behaviours respondent to the agent's perceptions of the
test environment, the agency-directed approach generates twice as many
effective tests than pseudo-random test generation, while being both efficient
and robust. Moreover, agents can be encoded to behave naturally without
compromising the effectiveness of test generation. Our results suggest that
generating tests using agency-directed testing significantly improves upon
random and simultaneously provides more realistic driving scenarios.Comment: 18 pages, 8 figure
Risk-Based Triggering of Bio-inspired Self-preservation to Protect Robots from Threats
Safety in autonomous systems has been mostly studied from a human-centered
perspective. Besides the loads they may carry, autonomous systems are also
valuable property, and self-preservation mechanisms are needed to protect them
in the presence of external threats, including malicious robots and
antagonistic humans. We present a biologically inspired risk-based triggering
mechanism to initiate self-preservation strategies. This mechanism considers
environmental and internal system factors to measure the overall risk at any
moment in time, to decide whether behaviours such as fleeing or hiding are
necessary, or whether the system should continue on its task. We integrated our
risk-based triggering mechanism into a delivery rover that is being attacked by
a drone and evaluated its effectiveness through systematic testing in a
simulated environment in Robot Operating System (ROS) and Gazebo, with a
variety of different randomly generated conditions. We compared the use of the
triggering mechanism and different configurations of self-preservation
behaviours to not having any of these. Our results show that triggering
self-preservation increases the distance between the drone and the rover for
many of these configurations, and, in some instances, the drone does not catch
up with the rover. Our study demonstrates the benefits of embedding risk
awareness and self-preservation into autonomous systems to increase their
robustness, and the value of using bio-inspired engineering to find solutions
in this area