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--- en:safeav:ctrl:summary [2026/04/23 11:33] – raivo.sell
+++ en:safeav:ctrl:summary [2026/04/24 09:59] (current) – raivo.sell
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 Finally, the chapter focuses on **validation and assurance**, highlighting the central role of digital twins, scenario-based testing, and formal methods. A modern V&V framework combines **multi-fidelity simulation (low- and high-fidelity)**, **design-of-experiments scenario generation**, and **formal specification of safety properties** (e.g., using Scenic and temporal logic). These methods enable systematic exploration of edge cases, measurement of safety metrics (e.g., time-to-collision, trajectory error), and structured comparison between simulation and real-world testing. Physical testing—from AV tracks to space qualification facilities—complements simulation, while continuous feedback from deployed systems updates the digital twin. The overarching theme is that **credible safety assurance requires a tightly integrated loop between simulation, formalism, and real-world validation**, with explicit measurement of the sim-to-real gap.
-Assessments:
-^ # ^ 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 |

en/safeav/ctrl/summary.txt · Last modified: 2026/04/24 09:59 by raivo.sell