@misc {43362,
	title = {DeepScenario: An Open Driving Scenario Dataset for Autonomous Driving System Testing},
	howpublished = {2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR)},
	year = {2023},
	publisher = {IEEE},
	address = {Melbourne, Australia},
	abstract = {With the rapid development of autonomous driving systems (ADSs), testing ADSs under various environmental conditions has become a key method to ensure the successful deployment of ADS in the real world. However, it is impossible to test all the scenarios due to the inherent complexity and uncertainty of ADSs and the driving tasks. Further, testing of ADSs is expensive regarding time and computational resources. Therefore, a large-scale driving scenario dataset consisting of various driving conditions is needed. To this end, we present an open driving scenario dataset DeepScenario, containing over 30K executable driving scenarios, which are collected by 2880 test executions of three driving scenario generation strategies. Each scenario in the dataset is labeled with six attributes characterizing test results. We further show the attribute statistics and distribution of driving scenarios. For example, there are 1050 collision scenarios, in 917 scenarios there were collisions with other vehicles, 105 and 28 with pedestrians and static obstacles, respectively. Target users include ADS developers who need to validate their systems under various environmental conditions.},
	doi = {10.1109/MSR59073.2023.00020},
	url = {https://ieeexplore.ieee.org/document/10174023/http://xplorestaging.ieee.org/ielx7/10173934/10173935/10174023.pdf?arnumber=10174023},
	author = {Lu, Chengjie}
}
@inproceedings {43359,
	title = {DeepScenario: An Open Driving Scenario Dataset for Autonomous Driving System Testing},
	journal = {2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR)},
	year = {2023},
	month = {05/2023},
	publisher = {IEEE},
	address = {Melbourne, Australia},
	abstract = {With the rapid development of autonomous driving systems (ADSs), testing ADSs under various environmental conditions has become a key method to ensure the successful deployment of ADS in the real world. However, it is impossible to test all the scenarios due to the inherent complexity and uncertainty of ADSs and the driving tasks. Further, testing of ADSs is expensive regarding time and computational resources. Therefore, a large-scale driving scenario dataset consisting of various driving conditions is needed. To this end, we present an open driving scenario dataset DeepScenario, containing over 30K executable driving scenarios, which are collected by 2880 test executions of three driving scenario generation strategies. Each scenario in the dataset is labeled with six attributes characterizing test results. We further show the attribute statistics and distribution of driving scenarios. For example, there are 1050 collision scenarios, in 917 scenarios there were collisions with other vehicles, 105 and 28 with pedestrians and static obstacles, respectively. Target users include ADS developers who need to validate their systems under various environmental conditions.},
	keywords = {autonomous driving system testing, dataset, driving scenario, Open Source},
	doi = {10.1109/MSR59073.2023.00020},
	url = {https://ieeexplore.ieee.org/document/10174023/http://xplorestaging.ieee.org/ielx7/10173934/10173935/10174023.pdf?arnumber=10174023},
	author = {Lu, Chengjie and Yue, Tao and Ali, Shaukat}
}
@misc {43360,
	title = {Evolutionary Computation and Reinforcement Learning for Cyber-physical System Design},
	howpublished = {2023 IEEE/ACM 45th International Conference on Software Engineering},
	year = {2023},
	publisher = {IEEE},
	address = {Melbourne, Australia},
	abstract = {Cyber-physical systems (CPSs) are designed to in-tegrate computation and physical processes through constantly interacting with the physical environment. The complexity and uncertainty of the environment often come up with unpredictable situations, which place high demands on the dynamic adaptability of CPSs. Further, as the environment evolves, the CPS needs to constantly evolve itself to adapt to the changing environment. This paper presents a research plan that aims to develop a novel framework to address CPS design challenges under uncertain environments. We propose to utilize evolutionary computation and reinforcement learning techniques to design control policies that can adapt to the dynamic changes and uncertainties of the environment. Further, novel testing and evaluation approaches that can generate test cases while adapting to dynamic changes in the system and the environment will be explored.},
	doi = {10.1109/ICSE-Companion58688.2023.00071},
	url = {https://ieeexplore.ieee.org/document/10172815/http://xplorestaging.ieee.org/ielx7/10172482/10172487/10172815.pdf?arnumber=10172815},
	author = {Lu, Chengjie}
}
@inproceedings {43357,
	title = {Evolutionary Computation and Reinforcement Learning for Cyber-physical System Design},
	journal = {2023 IEEE/ACM 45th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)},
	year = {2023},
	month = {05/2023},
	pages = {264-266},
	publisher = {IEEE},
	address = {Melbourne, Australia},
	abstract = {Cyber-physical systems (CPSs) are designed to integrate computation and physical processes through constantly interacting with the physical environment. The complexity and uncertainty of the environment often come up with unpredictable situations, which place high demands on the dynamic adaptability of CPSs. Further, as the environment evolves, the CPS needs to constantly evolve itself to adapt to the changing environment. This paper presents a research plan that aims to develop a novel framework to address CPS design challenges under uncertain environments. We propose to utilize evolutionary computation and reinforcement learning techniques to design control policies that can adapt to the dynamic changes and uncertainties of the environment. Further, novel testing and evaluation approaches that can generate test cases while adapting to dynamic changes in the system and the environment will be explored.\&nbsp;},
	keywords = {Cyber-Physical System, evolutionary computation, reinforcement learning, Uncertainty},
	doi = {10.1109/ICSE-Companion58688.2023.00071},
	url = {https://ieeexplore.ieee.org/document/10172815/http://xplorestaging.ieee.org/ielx7/10172482/10172487/10172815.pdf?arnumber=10172815},
	author = {Lu, Chengjie}
}
@misc {43363,
	title = {Learning configurations of operating environment of autonomous vehicles to maximize their collisions},
	howpublished = {2023 IEEE/ACM 45th International Conference on Software Engineering},
	year = {2023},
	publisher = {IEEE},
	address = {Melbourne, Australia},
	abstract = {Autonomous vehicles must operate safely in their dynamic and continuously-changing environment. However, the operating environment of an autonomous vehicle is complicated and full of various types of uncertainties. Additionally, the operating environment has many configurations, including static and dynamic obstacles with which an autonomous vehicle must avoid collisions. Though various approaches targeting environment configuration for autonomous vehicles have shown promising results, their effectiveness in dealing with a continuous-changing environment is limited. Thus, it is essential to learn realistic environment configurations of continuously-changing environment, under which an autonomous vehicle should be tested regarding its ability to avoid collisions. Featured with agents dynamically interacting with the environment, Reinforcement Learning (RL) has shown great potential in dealing with complicated problems requiring adapting to the environment. To this end, we present an RL-based environment configuration learning approach, i.e., DeepCollision , which intelligently learns environment configurations that lead an autonomous vehicle to crash. DeepCollision employs Deep Q-Learning as the RL solution, and selects collision probability as the safety measure, to construct the reward function. We trained four DeepCollision models and conducted an experiment to compare them with two baselines, i.e., random and greedy. Results show that DeepCollision demonstrated significantly better effectiveness in generating collisions compared with the baselines. We also provide recommendations on configuring DeepCollision with the most suitable time interval based on different road structures.},
	author = {Lu, Chengjie}
}
@misc {43361,
	title = {Test Scenario Generation for Autonomous Driving Systems with Reinforcement Learning},
	howpublished = {2023 IEEE/ACM 45th International Conference on Software Engineering},
	year = {2023},
	publisher = {IEEE},
	address = {Melbourne, Australia},
	abstract = {We have seen rapid development of autonomous driving systems (ADSs) in recent years. These systems place high requirements on safety and reliability for their mass adoption, and ADS testing is one of the crucial approaches to ensure the success of ADSs. To this end, this paper presents RLTester, a novel ADS testing approach, which adopts reinforcement learning (RL) to learn critical environment configurations (i.e., test scenarios) of the operating environment of ADSs that could reveal their unsafe behaviors. To generate diverse and critical test scenarios, we defined 142 environment configuration actions, and adopted the Time- To-Collision metric to construct the reward function. Our evaluation shows that RLTester discovered a total of 256 collisions, of which 192 are unique collisions, and took on average 11.59 seconds for each collision. Further, RLTester is effective in generating more diverse test scenarios compared to a state-of-the art approach, DeepCollision.},
	doi = {10.1109/ICSE-Companion58688.2023.00086},
	url = {https://ieeexplore.ieee.org/document/10172814/http://xplorestaging.ieee.org/ielx7/10172482/10172487/10172814.pdf?arnumber=10172814},
	author = {Lu, Chengjie}
}
@inproceedings {43358,
	title = {Test Scenario Generation for Autonomous Driving Systems with Reinforcement Learning},
	journal = {2023 IEEE/ACM 45th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)},
	year = {2023},
	month = {05/2023},
	pages = {317-319},
	publisher = {IEEE},
	address = {Melbourne, Australia},
	abstract = {We have seen rapid development of autonomous driving systems (ADSs) in recent years. These systems place high requirements on safety and reliability for their mass adoption, and ADS testing is one of the crucial approaches to ensure the success of ADSs. To this end, this paper presents\&nbsp;RLTester, a novel ADS testing approach, which adopts reinforcement learning (RL) to learn critical environment configurations (i.e., test scenarios) of the operating environment of ADSs that could reveal their unsafe behaviors. To generate diverse and critical test scenarios, we defined 142 environment configuration actions, and adopted the\&nbsp;Time-To-Collision\&nbsp;metric to construct the reward function. Our evaluation shows that\&nbsp;RLTester\&nbsp;discovered a total of 256 collisions, of which 192 are unique collisions, and took on average 11.59 seconds for each collision. Further,\&nbsp;RLTester\&nbsp;is effective in generating more diverse test scenarios compared to a state-of-the art approach,\&nbsp;DeepCollision.},
	doi = {10.1109/ICSE-Companion58688.2023.00086},
	url = {https://ieeexplore.ieee.org/document/10172814/http://xplorestaging.ieee.org/ielx7/10172482/10172487/10172814.pdf?arnumber=10172814},
	author = {Lu, Chengjie}
}
@inproceedings {42862,
	title = {Enhancing the realism of autonomous driving simulation with real-time co-simulation},
	journal = {4th International Workshop on Multi-Paradigm Modelling for Cyber-Physical Systems (MPM4CPS{\textquoteright}22) },
	year = {2022},
	publisher = { IEEE/ACM},
	author = {Chen, Qiwei and Wang, Tiexin and Lu, Chengjie and Yue, Tao and Ali, Shaukat},
	editor = {Amrani, Moussa and Blouin, Dominique and Challenger, Moharram and Exelmans, Joeri and Heinrich, Robert and Paredis, Randy}
}
@misc {42873,
	title = {Generating and Optimizing of Test Scenarios for Autonomous Driving Systems},
	howpublished = {Simula Research Laboratory, Norway},
	year = {2022},
	author = {Lu, Chengjie}
}
@misc {42875,
	title = {Learning Configurations of OperatingEnvironment of Autonomous Vehicles toMaximize their Collisions},
	howpublished = {NASA Jet Propulsion Laboratory (JPL)},
	year = {2022},
	author = {Lu, Chengjie}
}
@article {28452,
	title = {Learning Configurations of Operating Environment of Autonomous Vehicles to Maximize their Collisions},
	journal = {IEEE Transactions on Software Engineering},
	volume = {49},
	year = {2022},
	pages = {384-402},
	publisher = { IEEE},
	author = {Lu, Chengjie and Shi, Yize and Zhang, Huihui and Zhang, Man and Wang, Tiexin and Yue, Tao and Ali, Shaukat}
}
@inproceedings {27926,
	title = {Restricted Natural Language and Model-based Adaptive Test Generation for Autonomous Driving},
	journal = {ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS)},
	year = {2021},
	publisher = {ACM/IEEE},
	author = {Shi, Yize and Lu, Chengjie and Zhang, Man and Zhang, Huihui and Yue, Tao and Ali, Shaukat}
}
@misc {28282,
	title = {Search-based Selection and Prioritization of Test Scenarios for Autonomous Driving Systems},
	howpublished = {13th Symposium on Search-Based Software Engineering},
	year = {2021},
	publisher = {LNCS},
	type = {Conference Paper},
	author = {Lu, Chengjie}
}
@inproceedings {27925,
	title = {Search-based Selection and Prioritization of Test Scenarios for Autonomous Driving Systems},
	journal = {13th Symposium on Search-Based Software Engineering},
	year = {2021},
	publisher = {LNCS Springer},
	author = {Lu, Chengjie and Zhang, Huihui and Yue, Tao and Ali, Shaukat}
}
