Autonomous Vehicle Simulation

We construct a parameterized scenario library anchored to the vehicle's Operational Design Domain. Road geometry is defined using OpenDRIVE networks — intersections, merge lanes, roundabouts, construction zones — with parametric variation in lane widths, curvature radii, and grade changes. Traffic participants are modeled as behavior-tree agents with adjustable aggressiveness, reaction times, and lane-change tendencies, enabling generation of naturalistic traffic flows alongside adversarial edge cases such as sudden braking, jaywalking pedestrians, and emergency-vehicle preemption. Weather and lighting conditions span clear-day baselines through heavy rain, fog, snow, glare, and nighttime with variable street-light density. Each scenario is tagged with ODD dimensions — speed regime, road type, traffic density, environmental conditions — enabling coverage analysis against the target ODD specification. We implement a combinatorial expansion engine that cross-products scenario axes to produce millions of unique test configurations from hundreds of base templates. Scenario definitions are version-controlled and linked to requirements traceability matrices. Deliverables include the scenario library, ODD coverage mapping, expansion configuration, and a scenario browser UI. This library feeds Phase 2's sensor simulation with precisely controlled environmental conditions.

Phase 2 creates high-fidelity virtual replicas of the vehicle's sensor suite. Lidar models replicate the beam pattern, range noise, intensity response, and multi-return behavior of the fleet's specific scanner hardware — Luminar Iris, Hesai AT128, or Ouster OS1 — using DRIVE Sim's physically-based ray-tracing engine. Camera models match the exact lens distortion, rolling-shutter artifacts, HDR response curves, and Bayer-pattern demosaicing of production imagers. Radar models simulate point-cloud returns with appropriate range-Doppler resolution, multipath reflections, and clutter profiles. Each virtual sensor is calibrated against recorded field data: we drive identical scenarios in the real world and in simulation, comparing point-cloud statistics, detection-range curves, and image-quality metrics to minimize domain gap. Sensor placement replicates the vehicle's mounting geometry and extrinsic calibration, ensuring that occlusion patterns and field-of-view overlaps match the physical platform. Deliverables include calibrated sensor-model configurations, a domain-gap assessment report with quantified error bounds, and a regression test pack that validates sensor fidelity after simulation engine updates. These calibrated sensors ensure that Phase 3 SIL/HIL testing exercises the perception stack under realistic signal conditions.

Phase 3 connects DRIVE Sim outputs to the vehicle's software and hardware validation stacks. In Software-in-the-Loop mode, simulated sensor data streams directly into the perception-planning-control pipeline running on developer workstations or cloud VMs, enabling rapid iteration on algorithm changes with full scenario coverage. We configure deterministic replay so that every scenario execution is bit-reproducible, supporting regression testing and root-cause analysis when perception failures occur. For Hardware-in-the-Loop validation, DRIVE Sim outputs are channeled through data-injection hardware into the vehicle's production ECU stack — DRIVE AGX Orin or Hyperion — verifying that the full compute pipeline, including hardware-accelerated inference, meets latency and accuracy requirements under sensor load. SIL and HIL test harnesses share a common results schema, allowing unified pass/fail dashboards and coverage tracking. We implement automated nightly regression runs that execute the full scenario library against the latest perception stack build, flagging performance regressions before they reach the integration branch. Deliverables include SIL/HIL adapter configurations, deterministic-replay infrastructure, regression-test scheduling automation, and a unified results dashboard. This integration pipeline feeds Phase 4's fleet-wide validation and certification workflows.

The final phase aggregates SIL/HIL results into fleet-level validation evidence. We build ODD coverage dashboards that map scenario execution counts against the complete ODD specification, identifying under-tested regions that require additional scenario generation. Statistical analysis computes confidence intervals for key safety metrics — collision rate, minimum time-to-collision distributions, traffic-rule compliance — across the full scenario corpus. Regression tracking monitors how each perception-stack release performs relative to its predecessor across the entire scenario library, with automated gates that block releases exhibiting statistically significant degradation. We generate certification evidence packages aligned to industry frameworks — ISO 21448 SOTIF, UL 4600, and EU AI Act requirements — documenting test methodology, coverage claims, and residual-risk assessments. A release-management workflow enforces sign-off gates: simulation coverage thresholds, regression-pass criteria, and safety-case review before any over-the-air update reaches the fleet. Deliverables include coverage dashboards, statistical safety reports, certification evidence packages, release-gate configurations, and a continuous-validation playbook that keeps the scenario library evolving alongside fleet-collected field data. This closed-loop process ensures that simulation validation remains authoritative throughout the vehicle program's lifecycle.