Name
Title | ||
|---|---|---|
Functional Gradient Descent Optimization For Automatic Test Case Generation For Vehicle Controllers |
Abstract | ||
|---|---|---|
A hierarchical framework is proposed for improving the automatic test case generation process for high-fidelity models with long execution times. The framework incorporates related low-fidelity models for which certain properties can be analytically or computationally evaluated with provable guarantees (e.g., gradients of safety or performance metrics). The low-fidelity models drive the test case generation process for the high-fidelity models. The proposed framework is demonstrated on a model of a vehicle with Full Range Adaptive Cruise Control with Collision Avoidance (FRACC), for which it generates more challenging test cases on average compared to test cases generated using Simulated Annealing. |
Year | Venue | Field |
|---|---|---|
2017 | 2017 13TH IEEE CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE) | Simulated annealing,Gradient descent,Cruise control,Control theory,Computer science,Collision,Test case,System dynamics,Trajectory |
DocType | ISSN | Citations |
Conference | 2161-8070 | 1 |
PageRank | References | Authors |
0.40 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cumhur Erkan Tuncali | 1 | 5 | 2.14 |
| Shakiba Yaghoubi | 2 | 13 | 2.96 |
| Theodore P. Pavlic | 3 | 42 | 10.50 |
| Georgios E. Fainekos | 4 | 804 | 52.65 |