Functional gradient descent optimization for automatic test case generation for vehicle controllers
- Resource Type
- Conference
- Authors
- Tuncali, Cumhur Erkan; Yaghoubi, Shakiba; Pavlic, Theodore P.; Fainekos, Georgios
- Source
- 2017 13th IEEE Conference on Automation Science and Engineering (CASE) Automation Science and Engineering (CASE), 2017 13th IEEE Conference on. :1059-1064 Aug, 2017
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Computational modeling
Optimization
Trajectory
System dynamics
Adaptation models
Testing
Safety
- Language
- ISSN
- 2161-8089
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.