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| en:safeav:ctrl:strategies [2025/07/02 16:01] – pczekalski | en:safeav:ctrl:strategies [2026/04/29 16:45] (current) – raivo.sell |
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| ====== Classical and AI-Based Control Strategies ====== | ====== Classical and AI-Based Control Strategies ====== |
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| {{:en:iot-open:czapka_b.png?50| Bachelors (1st level) classification icon }} | |
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| <todo @tgrzejszczak></todo> | |
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| The control system of an autonomous vehicle is the final arbiter of safety, translating high-level plans and decisions into precise, real-time actions that govern the vehicle's movement. It is responsible for managing the vehicle's speed, steering, acceleration, and braking, ensuring that the vehicle follows the planned trajectory accurately and safely, even in the face of disturbances, sensor noise, and dynamic environmental changes. The effectiveness and robustness of the control strategy are paramount to overall vehicle safety. This sub-chapter explores the two primary paradigms shaping modern vehicle control: classical control strategies and AI-based control strategies, examining their principles, applications, safety implications, and the ongoing convergence between them. | |
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| ===== Classical Control Strategies ===== | ===== Classical Control Strategies ===== |
| * **Future Trends:** Research is actively focused on making AI controllers more transparent (e.g., via explainable AI), more robust (e.g., via adversarial training, safe exploration in RL), and better integrated with classical methods. There is also growing interest in "learning-to-control" approaches that combine model learning with control policy learning. | * **Future Trends:** Research is actively focused on making AI controllers more transparent (e.g., via explainable AI), more robust (e.g., via adversarial training, safe exploration in RL), and better integrated with classical methods. There is also growing interest in "learning-to-control" approaches that combine model learning with control policy learning. |
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| ===== Conclusion ===== | |
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| Classical control strategies provide a foundation of predictability, stability, and transparency, making them essential for safety-critical low-level vehicle control. AI-based control strategies offer the potential to handle unprecedented complexity and adaptability, learning optimal behaviors from data. Neither approach is a silver bullet; each has distinct strengths and weaknesses regarding safety. The future of safe autonomous vehicle control likely lies in sophisticated hybrid systems that intelligently combine the rigor of classical control with the power of AI, all underpinned by rigorous verification, validation, and a relentless focus on ensuring robust and predictable behavior in the real world. The ongoing development and integration of these strategies are key to achieving the high levels of safety required for widespread deployment of autonomous vehicles. | Classical control strategies provide a foundation of predictability, stability, and transparency, making them essential for safety-critical low-level vehicle control. AI-based control strategies offer the potential to handle unprecedented complexity and adaptability, learning optimal behaviors from data. Neither approach is a silver bullet; each has distinct strengths and weaknesses regarding safety. The future of safe autonomous vehicle control likely lies in sophisticated hybrid systems that intelligently combine the rigor of classical control with the power of AI, all underpinned by rigorous verification, validation, and a relentless focus on ensuring robust and predictable behavior in the real world. The ongoing development and integration of these strategies are key to achieving the high levels of safety required for widespread deployment of autonomous vehicles. |
