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Table of Contents
Physical Testing
Physical testing infrastructures across ground, airborne, marine, and space systems reflect a progression from high-access, repeatable environments to extremely constrained, high-cost, and often non-replicable conditions. Each domain builds specialized facilities to bridge the gap between simulation and real-world deployment, with increasing emphasis on safety, controllability, and observability of complex system interactions.
Ground Systems (Automotive & Robotics)
Ground systems benefit from the most accessible and diverse physical testing environments. Proving grounds and AV test tracks—such as Mcity and American Center for Mobility—replicate urban, suburban, and highway conditions with controllable variables (traffic signals, pedestrian dummies, weather systems). OEMs also use large private facilities (e.g., General Motors Milford Proving Ground) for durability, ADAS, and edge-case testing. These environments enable repeatable scenario testing, fault injection, and safe validation of perception and decision-making systems. Increasingly, they are instrumented with high-precision localization, V2X infrastructure, and synchronized data capture to support validation at scale.
Airbone Systems (Aviation & UAVs)
Airborne testing combines ground-based facilities and open-air test ranges. Wind tunnels (e.g., NASA Ames Research Center Wind Tunnel) provide controlled aerodynamic testing across regimes, while iron-bird rigs and avionics labs enable hardware/software integration before flight. Actual flight testing occurs at restricted ranges such as Edwards Air Force Base or FAA-designated UAV corridors, where telemetry, radar tracking, and chase aircraft ensure safety. Compared to ground systems, repeatability is lower, and environmental factors (weather, airspace constraints) play a larger role, but the combination of lab + flight test provides a structured certification pathway.
Marine testing relies on a mix of controlled hydrodynamic facilities and open-water trials. Towing tanks and wave basins—such as those at Naval Surface Warfare Center—allow precise study of hull performance, propulsion, and wave interaction. For autonomy, sheltered environments (harbors, test lakes) are used for early-stage validation, followed by coastal and deep-sea trials. Facilities often include instrumented buoys, GPS-denied navigation testing zones, and long-duration endurance setups. Compared to ground and air, marine systems emphasize disturbance realism (waves, currents) and long-horizon reliability, with less focus on dense, repeatable interaction scenarios.
| # | Project Title | Description | Learning Objectives |
|---|---|---|---|
| 1 | Classical vs AI Control Benchmark Study | Implement and compare a classical controller (e.g., PID or LQR) with an AI-based controller (e.g., reinforcement learning) for a simplified vehicle model in simulation. Evaluate performance under nominal and disturbed conditions. | - Understand differences between model-based and data-driven control - Analyze stability, robustness, and interpretability trade-offs - Evaluate controller performance under uncertainty and disturbances |
| 2 | Behavioral & Motion Planning Stack Design | Design a hierarchical autonomy stack that includes a behavioral layer (FSM or behavior tree) and a motion planner (A*, RRT*, or MPC). Apply it to a scenario such as lane change or obstacle avoidance. | * Distinguish between behavioral decision-making and motion planning * Implement planning algorithms under constraints * Understand integration between perception, planning, and control |
| 3 | Scenario-Based Validation Framework | Develop a scenario-based testing framework using parameterized scenarios (e.g., varying speeds, distances, agent behaviors). Use a simulator to evaluate planning/control performance across these scenarios. | * Apply design-of-experiments (DOE) to autonomy validation * Define and measure safety metrics (e.g., TTC, collision rate) * Understand coverage and edge-case testing challenges |
| 4 | Digital Twin & Multi-Fidelity Simulation Study | Build a simplified digital twin of a vehicle and environment. Perform validation using both low-fidelity and high-fidelity simulation setups, comparing results and identifying discrepancies. | * Understand role of digital twins in V&V * Analyze trade-offs between simulation fidelity and scalability * Quantify sim-to-real gaps and their implications |
| 5 | Formal Methods for Safety Validation | Define safety requirements using a formal specification approach (e.g., temporal logic or rule-based constraints). Apply these to simulation traces and identify violations or edge cases. | * Translate safety requirements into formal, testable properties * Use formal methods for falsification and validation * Understand limitations of simulation without formal rigor |
