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en:safeav:ctrl:vctrl [2026/03/26 10:59] – airien:safeav:ctrl:vctrl [2026/04/29 17:01] (current) – raivo.sell
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 ====== Validation of Control & Planning ====== ====== Validation of Control & Planning ======
-{{:en:iot-open:czapka_m.png?50| Masters (2nd level) classification icon }} 
  
-<todo @momala></todo> 
  
-====== Principles and Scope ======+===== Principles and Scope =====
  
  
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 A final principle is lifecycle realism. A digital twin is not just a CAD model; it is a live feedback loop receiving data from the physical system and its environment, so the simulator remains predictive as the product evolves. The same infrastructure that generates scenarios can replay field logs, inject updated vehicle parameters, and reflect map changes, enabling continuous V&V of planning and control post-deployment. A final principle is lifecycle realism. A digital twin is not just a CAD model; it is a live feedback loop receiving data from the physical system and its environment, so the simulator remains predictive as the product evolves. The same infrastructure that generates scenarios can replay field logs, inject updated vehicle parameters, and reflect map changes, enabling continuous V&V of planning and control post-deployment.
  
-====== Example: Scenario-Based Validation with Digital Twins ======+===== Scenario-Based Validation with Digital Twins =====
  
  
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 Finally, environment twins are built from aerial photogrammetry and point-cloud processing (with RTK-supported georeferencing), yielding maps and 3D assets that match the real campus, so trajectory-level decisions (overtake, yield, return-to-lane) are evaluated against faithful road geometries and occlusion patterns((Pikner, Heiko, et al. "Autonomous Driving Validation and Verification Using Digital Twins." VEHITS (2024): 204-211.)). Finally, environment twins are built from aerial photogrammetry and point-cloud processing (with RTK-supported georeferencing), yielding maps and 3D assets that match the real campus, so trajectory-level decisions (overtake, yield, return-to-lane) are evaluated against faithful road geometries and occlusion patterns((Pikner, Heiko, et al. "Autonomous Driving Validation and Verification Using Digital Twins." VEHITS (2024): 204-211.)).
  
-====== Methods and Metrics for Planning & Control ======+===== Methods and Metrics for Planning & Control =====
  
  
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 Finally, your program extends to low-level control via HIL-style twins. A Simulink-based network of virtual ECUs and data buses sits between Autoware’s navigation outputs and simulator actuation. This lets you simulate bus traffic, counters, and checksums; disable subsystems (e.g., steering module) to provoke graceful degradation; and compare physical ECUs against their twin under identical inputs to detect divergence. It is an efficient route to validating actuator-path integrity without building a full physical rig. Finally, your program extends to low-level control via HIL-style twins. A Simulink-based network of virtual ECUs and data buses sits between Autoware’s navigation outputs and simulator actuation. This lets you simulate bus traffic, counters, and checksums; disable subsystems (e.g., steering module) to provoke graceful degradation; and compare physical ECUs against their twin under identical inputs to detect divergence. It is an efficient route to validating actuator-path integrity without building a full physical rig.
  
-====== Case Study and Safety Argumentation ====== 
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-On the TalTech iseAuto shuttle, the digital twin (vehicle model, sensor suite, and campus environment) is integrated with LGSVL/Autoware through a ROS bridge so that “photons-to-torque” loops are exercised under realistic scenes before any track test. Scenarios are distributed over the campus xodr network using Scenic/M-SDL; multiple events can be chained within a scenario to probe planner behaviors around parked vehicles, slow movers, or oncoming traffic. Logging is aligned to the KPIs above so outcomes are comparable across LF/HF layers and re-runnable when planner or control parameters change. 
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-In practice, this has yielded a concise, defensible narrative for planning & control safety: (1) what was tested (formalized scenarios across a structured parameter space); (2) how it was tested (two-layer simulation with a calibrated digital twin and, when necessary, track execution); (3) what happened (mission success, DTC minima, TTC profiles, braking/steering transients, localization drift); and (4) why it matters (evidence that tuning or algorithmic changes move the decision–execution loop toward or away from safety). The same framework has been used to analyze adversarial stresses on rule-based local planners, reinforcing that planning validation must include robustness to distribution shifts and targeted perturbations. 
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-As a closing reflection, the approach acknowledges that simulation is not the world—so it measures the gap. By transporting formally generated cases to the track and comparing time-series behaviors, the program both validates planning/control logic and calibrates the digital twin itself, using discrepancies to guide model updates and ODD limits. That is the hallmark of modern control & planning V&V: scenario-driven, digitally twinned, formally grounded, and relentlessly comparative to reality. 
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