You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact

Model-Based Policy Synthesis and Test-Case Generation for Autonomous Systems


Publication Type:

Conference/Workshop Paper


19th Workshop on Advances in Model Based Testing


Autonomous systems are supposed to automatically plan their actions and execute the plan without human intervention. In this paper, we propose a model-based two-layer framework for policy synthesis and test-case generation for autonomous systems. At the high-level layer of the framework, we have two kinds of methods for synthesizing policies whose correctness is guaranteed by model checking. The autonomous system's controller executes synthesised policies at the low-level layer. As the kinematics of autonomous systems is often nonlinear and the environment may influence the results of their actions, formally verifying the controllers is extremely difficult. We propose a novel method for generating test cases for the controllers at the low-level layer. The method employs reinforcement learning for test-case generation and model checking to ensure that the test cases faithfully realize the execution of the policy. The framework is designed in Uppaal Stratego, which integrates model checkers and algorithms for policy synthesis. Therefore, the framework separates concerns and seamlessly interchanges the information between two layers.


author = {Rong Gu and Eduard Paul Enoiu},
title = {Model-Based Policy Synthesis and Test-Case Generation for Autonomous Systems},
month = {April},
year = {2023},
booktitle = {19th Workshop on Advances in Model Based Testing},
url = {}