3. Motivation
Autonomy in Vehicles
NP hard Problem
[1][2]
Test and Evaluation tools for maritime,
air and ground Autonomous Systems
Limited Test to Evaluate Test Systems
Important or Not Important
Cost and Reliability
Slide 3
4. Technology
BeamNG Research
Open Platform solve problems of
mostly vehicle autonomy
Python
API interface language
MATLAB
Data Transformations and
Visualizations
Slide 4
5. Introduction
Paper focus: relation between Scenario
configuration and Performance
Not Focused on Fault Detection or Model
Checking
Identify regions indicating performance
boundaries
Classify test cases based on
performance
Slide 5
9. DB SCAN
It is a density-based
clustering algorithm
[4]
Boundary Detection Classification
Problem
Performance modes treated as
continuous space thus we use DB scan
K nearest neighbour to identify final
Slide 9
15. Conclusion
Narrows down the input space
Helps Testers focus on the important
scenarios
Relating Scenarios help design
suitable test cases
Reduce Cost and increase reliability
Slide 14
17. References
Mullins, G. E., Stankiewicz, P. G., Hawthorne, R. C., Appler, J. D., Biggins, M. H., Chiou, K., ... &
Watkins, A. S. (2017). Delivering Test and Evaluation Tools for Autonomous Unmanned Vehicles to the
Fleet. JOHNS HOPKINS APL TECHNICAL DIGEST, 33(4), 279-288. [1]
Abdessalem, R. B., Nejati, S., Briand, L. C., & Stifter, T. (2018, May). Testing vision-based control
systems using learnable evolutionary algorithms. In 2018 IEEE/ACM 40th International Conference on
Software Engineering (ICSE) (pp. 1016-1026). IEEE.[2]
Wittmann, D., Wang, C., & Lienkamp, M. (2015). Definition and identification of system boundaries of
highly automated driving. In 7. Tagung Fahrerassistenz[3]
Ester, Martin; Kriegel, Hans-Peter; Sander, Jörg; Xu, Xiaowei (1996). Simoudis, Evangelos; Han,
Jiawei; Fayyad, Usama M., eds. A density-based algorithm for discovering clusters in large spatial
databases with noise. Proceedings of the Second International Conference on Knowledge Discovery
and Data Mining (KDD-96). AAAI Press. pp. 226–231. CiteSeerX 10.1.1.121.9220. ISBN 1-57735-004-
9.[4]
https://en.wikipedia.org/wiki/Gaussian_function[5]
17Slide