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| en:safeav:ctrl:summary [2026/04/23 11:27] – created raivo.sell | en:safeav:ctrl:summary [2026/04/23 11:33] (current) – raivo.sell |
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| | ====== Summary ====== |
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| | This chapter develops a comprehensive view of how **control, decision-making, and motion planning** form the core of autonomous system behavior, and how these elements vary across domains and implementation paradigms. It begins by contrasting **classical control methods**—such as PID, LQR, and state estimation—with **AI-based approaches** like reinforcement learning and neural network controllers. Classical methods offer strong guarantees in stability, transparency, and certifiability, making them well-suited for safety-critical low-level control. In contrast, AI-based methods provide adaptability and the ability to handle complex, nonlinear dynamics but introduce challenges in explainability, verification, and robustness. The chapter emphasizes that **hybrid architectures**—where AI handles high-level decisions and classical control ensures safe execution—are emerging as the most practical and safety-aligned approach. |
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| | The chapter then explores the **decision and planning hierarchy**, distinguishing between behavioral algorithms (“what to do”) and motion planning (“how to do it”). Behavioral methods such as finite state machines, behavior trees, and utility-based reasoning govern high-level actions like lane changes or yielding, while motion planners generate feasible trajectories using techniques like A*, RRT*, and model predictive control. A key insight is the tight coupling between these layers and the control system: perception feeds behavior, behavior drives planning, and planning feeds control in a continuous loop. Safety emerges not from any single layer, but from their coordinated operation under uncertainty, including prediction of other agents, adherence to constraints, and real-time replanning. |
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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. |
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| | Assessments: |
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| | ^ # ^ 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 | |
