Perception OS
Live multi-modal captures — tracking, lanes, depth, ego-motion. Select a clip below.
Full pipeline · shadow_mode · playback 0.85×
Drift & fusion analytics
10,000-run Monte Carlo model — cliff at 1.6° camera yaw. Charts adapted from NADIR simulation research visuals.
Residual & danger benchmarks
Pilot hardcoded curves — yaw residual gating, vehicle sensor topology, and ADAS danger index by stack profile.
if |Δyaw| > 0.5°: alert() # 41% false positive · highway ACC
tier = changepoint(residual, fleet_prior) # 9% false positive · 4.8m MTTD
Sensor fusion network
Multi-modal ingest through NADIR drift scoring, fusion, evidence graph, and downstream AEB / LKA / shop dispatch.
3D drift explorer
Pseudo-3D vehicle + lane projection — scrub yaw to see lane misalignment before NADIR correction.
Drift — before vs. after NADIR
Drag the slider — without NADIR, lane projection diverges and residuals breach tier CRITICAL.
Camera yaw & lane alignment
AEB — brake cone & TTC
Autonomous driving — path fidelity
Steering — EPS loop stability
Controllability — lateral envelope
Mathematics — residual correction
Extrinsic drift compounds in the Jacobian; NADIR applies SE(3) correction and temperature-compensated residuals.
State estimator — before / after
changepoint hidden · no signed export
σ_post = 0.07° · trace 100% · tier CLEAR
Fleet residual heatmap cinema
Multi-VIN matrix — rows=VIN, cols=time, color=tier/residual. Offline bundled JSON or live org-scoped health-summary.
Cross-modal fusion disagreement
Camera, radar, and LiDAR diverge under glass/collision — fusion core pulses tier CRITICAL.
Evidence bundle assembly
Detect → changepoint → shop dispatch → validation → signed ZIP + Merkle hash.
Ingest → changepoint → dispatch
Telemetry batch ingest with trace IDs, tier promotion, webhook delivery, and prioritized bay queue.