| Both sides previous revisionPrevious revision | |
| en:safeav:handson:uc:shuttle [2026/04/29 16:53] – raivo.sell | en:safeav:handson:uc:shuttle [2026/04/29 17:02] (current) – raivo.sell |
|---|
| The AV Shuttle use case requires a flexible and scalable V&V setup that supports both low- and high-fidelity simulations, OpenSCENARIO-based scenario testing, and full integration with Autoware.Universe stack. The environment must enable automated safety and performance evaluation, containerized deployment, and open-source accessibility suitable for higher education. It emphasizes iterative, hands-on experimentation and long-term sustainability without reliance on commercial tools. | The AV Shuttle use case requires a flexible and scalable V&V setup that supports both low- and high-fidelity simulations, OpenSCENARIO-based scenario testing, and full integration with Autoware.Universe stack. The environment must enable automated safety and performance evaluation, containerized deployment, and open-source accessibility suitable for higher education. It emphasizes iterative, hands-on experimentation and long-term sustainability without reliance on commercial tools. |
| |
| | <note>Cuts from main book - in the book i do not want to have use case specific material</note> |
| |
| ===== Case Study and Safety Argumentation ===== | ===== Case Study and Safety Argumentation ===== |
| 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. | 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. |
| |
| | ===== Case Study and Safety Argumentation ===== |
| |
| | |
| | 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. |
| | |
| | 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. |
| | |
| | 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. |
| |
| |
