Multi-sim dashboard for autonomous vehicle perception. Replays Waymo scenes in 3D, proposes ego actions via the OpenEnv model, and continuously spawns counterfactual rollouts to compare "what if the ego chose differently?"

npm install
npm run dev

Open http://localhost:5173. The app defaults to mock data mode (no Waymo files needed).

The OpenEnv model provides actions and rewards for the ego vehicle.

Mock mode (default): Deterministic pseudo-random actions based on scene context. No external API needed.

Real mode: Set an environment variable to point at your OpenEnv endpoint:

VITE_OPENENV_ENDPOINT=http://localhost:8080/predict
VITE_OPENENV_MODE=real

Then in your code, call configureOpenEnv({ mode: "real", endpoint: import.meta.env.VITE_OPENENV_ENDPOINT }).

The client module lives at src/lib/openenvClient.ts and exposes:

• getActionAndReward(input) — single action/reward query
• getCounterfactualVariants(input, count) — N variant actions for branching

Place Waymo Open Dataset parquet files in public/waymo_data/:

• vehicle_pose.parquet
• lidar.parquet
• lidar_box.parquet (optional)
• lidar_calibration.parquet

Or drag and drop files directly onto the simulator.

1. Open http://localhost:5173/sim
2. The sim loads with mock data. Use the scenario selector (top-left) to pick "Near Miss" or "Jaywalker"
3. Press Play. Watch the Autonomy Stack panel (right) update every 3s with OpenEnv actions/rewards
4. Click "Explain last decision" to see the model's reasoning
5. Navigate to /dashboard — the camera grid auto-populates with counterfactual sims
6. Watch new tiles appear every 10s. Each shows a 2D top-down mini-map of the ego's divergent trajectory
7. Click any tile to see full metrics and action stream
8. Go to /graph — the knowledge graph connects runs, actions, metrics, and rewards. Click nodes to inspect
9. Go to /analytics — overview cards, sortable table, timeline chart, and incident feed

• React 19 + TypeScript + Vite
• Three.js + React Three Fiber (3D rendering)
• Zustand (state management)
• react-force-graph-3d (knowledge graph)
• Sonner (toast notifications)
• Lucide React (icons)
• Hyparquet (browser-native Parquet reader)

src/
  pages/           SimPage, DashboardPage, GraphPage, AnalyticsPage
  components/      Scene3D, Timeline, and existing 3D components (kept intact)
  components/ui/   AppShell, Card, Badge (design system)
  lib/             openenvClient, simManager, simTypes
  utils/           parquet, waymoLoader, rangeImage, trajectoryData, scenarioAI
  store.ts         Zustand global state
  theme.ts         Design tokens (colors, typography, spacing)
  mockData.ts      Synthetic scenario generation + LiDAR raytracing

Overflow runs an AV-safety agent fleet on three layers of agent infrastructure:

• ArmorIQ (scripts/armoriq_*.mjs) — governance: every tool call gated by a signed intent token. npm run agent, npm run fleet.
• AgentSpan (scripts/agentspan_*.py) — durable execution: crash-resume, retries, structured output, guardrails, human approval. npm run agentspan, npm run agentspan:fleet.
• Band (scripts/band_agents.py) — cross-agent discovery + @mention coordination. npm run band -- auditor.

Python setup (once): uv venv .venv && uv pip install -r requirements-agents.txt — the agents reuse the OpenAI key in server/.env.

See AGENT_STACK.md for the full story and run guide.