Systems and Controls
About
The vision of the Systems Simulation and Controls group at SIMCenter is to perform fundamental and applied research in systems-level simulation, modeling and control which includes uncertainty analysis, model-based design for complex systems, system integration, etc. We apply these methods to a broad spectrum of problems such as, multi-modal traffic simulation for autonomous vehicles and advanced powertrains, air-force mission planning, vehicle design, digital twins etc.
Simulation and modeling are key tools for efficient problem solving, particularly in engineering. Simulation models could be used in the design process in multiple ways, such as, system optimization, control design, validation and verification, decision making etc.
Fundamental research areas
- Model fidelity selection based on requirements
- Uncertainty analysis of model-based design
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Test case/scenario generation for simulation-based design
- Model integration, uncertainty/sensitivity modeling, uncertainty propagation through subsystems
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Integrated systems design
- Data-driven modeling of complex engineering systems (machine learning)
Application areas
- Connected and Autonomous Vehicles and Advanced Driver Assistance Systems (ADAS) safety validation
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Traffic simulation and calibration for AV/ADAS testing
- Automotive vehicle modeling (Vehicle dynamics, powertrain, sensors and control)
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Powertrain design and control
- Perception systems modeling and sensitivity
Software and equipment expertise
DSpace Hardware-In-the-Loop (HIL) bench, Matlab, Simulink, Python, C++, Prescan, Unreal Gaming Engine, Simulation of Urban Mobility (SUMO), Opencv, DeepStack and other opensource image processing tools
Accordions
Requirements generation for simulation-based autonomous vehicles safety assessment
- Developed a way to generate safety-critical driving scenarios that can be simulated using NHTSA CDS crash records data
- Developed an approach to filter, categorize CDS cases
- Used CDS cases to define logical scenarios with a descriptive story of the event
- Developed an optimal control and driver model-based approach to identify scenario parameters such that the scenario becomes marginally critical
Publications/Thesis:
Kibalama, D., Chen, B.S., Tulpule P. ,(2021) AV/ADAS Safety-Critical Testing Scenario Generation from Vehicle Crash Data, SAE World Congress
Chen, B.S., Tulpule P. (2022), Criticality Assessment of Simulation-Based AV/ADAS Test Scenarios, SAE World Congress
Multi-Modal traffic simulation for The Ohio State University campus
- As a part of OSU campus digital twin a procedure to calibrate multi-modal traffic model was developed
- A SUMO and dSpace ASM co-simulation platform was built to simulate traffic in Hardware-in The Loop setting
Thesis:
Kalra, V., (2021) Multi-modal Simulation and Calibration for OSU Campus Mobility, MS Thesis, The Ohio State University
Traffic modeling at roundabouts using infrastructure camera video feed
- An infrastructure camera data fusion method was developed to generate O-D matrix for multi-lane roundabout
- A custom trained YOLO algorithm along with SORT was used to track vehicles across the roundabout
- A Machine Learning-based approach was proposed to generate uncommon behavior at the roundabout
- A calibrated micro traffic simulation model was developed for the roundabout
Publications/Thesis:
Patil, J., Tulpule, P. (2021). Infrastructure Camera Video Data Processing of Traffic at Roundabouts (No. 2021-01-0165). SAE Technical Paper
Patil, J. (2021). Camera Video Data Processing of Traffic and Development of Microsimulation Traffic SUMO Model for Roundabout, MS Thesis, The Ohio State University
Toolset for AV/ADAS validation verification
- A modular open-source software tool was developed for AV/ADAS algorithms V&V
- A SUMO and Unreal (gaming engine) co-simulation platform was developed
- A vehicle dynamics based algorithm allows seamless time synchronization between multi-fidelity simulation/rendering tools
Publications/Thesis:
Tulpule, P., Midlam-Mohler, S., Karumanchi, A., Jin, Y. (2021). A Simulation Tool for Virtual Validation and Verification of Advanced Driver Assistance Systems (No. 2021-01-0865). SAE Technical Paper
Sensitivity analysis of computer vision algorithms
- An Image Quality Assessment (IQA) based novel approach is developed to understand the relationship between image properties and object detection performance
- Various IQAs and performance measures for different detection algorithms were analyzed
- A support vector machine-based classifier was developed to understand relationships between image properties and detection performance
- The approach was verified using various pre-trained models (e.g. YOLO-v3) for the Audi-A2D2 dataset
Publications/Thesis:
Padisala, S.K., (2021) Development of Frameworks for Environment Dependent Traffic Simulation and ADAS Algorithm Testing, MS Thesis, The Ohio State University
Center for Automotive Research (CAR)
EcoCAR Motorsports team
Current team members
Shawn Midlam-Mohler, Professor, Mechanical and Aerospace Engineering
David Hillstrom, Senior Researcher, SIMCenter
Mayur Patil, PhD Candidate, Mechanical and Aerospace Engineering
Pooja Tambolkar, Masters Research Assistant, Mechanical and Aerospace Engineering
Jake Isaman, Undergraduate Research Assistant, Electrical and Computer Engineering
Recent Graduates
Jagruti Patil, Masters, Mechanical and Aerospace Engineering
Vikhyat Kalra , Masters, Mechanical and Aerospace Engineering
Shanthan Kumar Padisala, Masters, Mechanical and Aerospace Engineering
Yishen Jin, Masters, Mechanical and Aerospace Engineering
Harnarayan Singh, Masters, Mechanical and Aerospace Engineering
Vivek Bither, Ph.D., Masters, Mechanical and Aerospace Engineering
- Bithar, V., Tulpule P., Midlam-Mohler, S., (Submitted), Online Robust MPC based Emergency Maneuvering System for Autonomous Vehicles, IEEE Transactions of Intelligent Vehicles
- Patil J., Tulpule P., (In preparation) Traffic model calibration of roundabouts using infrastructure camera video feed, Journal of Transportation Systems: Part A
- Tulpule, P., Vaidya U., (2021, Accepted), Information-Theoretic Approach for Model Reduction Over Finite Time Horizon, IFAC Modeling Estimation and Controls Conference
- Karumanchi, A., Tulpule P., (2021, Accepted), Comparing Linear Systems with Gaussian Mixture Model Additive Uncertainties Using Kullback-Leibler Rate, IFAC Modeling Estimation and Controls Conference
- Patil, J., Tulpule, P. (2021). Infrastructure Camera Video Data Processing of Traffic at Roundabouts (No. 2021-01-0165). SAE Technical Paper
- Singh, H., Midlam-Mohler, S., Tulpule, P. (2021). Simulation Based Virtual Testing for Safety of ADAS Algorithms-Case Studies (No. 2021-01-0114). SAE Technical Paper
- Tulpule, P., Midlam-Mohler, S., Karumanchi, A., Jin, Y. (2021). A Simulation Tool for Virtual Validation and Verification of Advanced Driver Assistance Systems (No. 2021-01-0865). SAE Technical Paper
- Perez, W., Ruhela, A., & Tulpule, P. (2020). Benchmarking Computational Time of Dynamic Programming for Autonomous Vehicle Powertrain Control (No. 2020-01-0968). SAE Technical Paper
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