feat(lab): publish full TGS shadow evidence
This commit is contained in:
DCCONSTRUCTIONS
@@ -49,6 +49,7 @@ import { M48R3StaticOccupancyShadowResultView } from "./M48R3StaticOccupancyShad
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import { M48SFixedClassDetectorResultView } from "./M48SFixedClassDetectorResult";
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import { M48TRiskQualityResultView } from "./M48TRiskQualityResult";
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import { M49TgsFailClosedResultView } from "./M49TgsFailClosedResult";
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import { M49TgsFullShadowResultView } from "./M49TgsFullShadowResult";
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export { isAdvancedLaboratoryWorkId };
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export type { AdvancedLaboratoryWorkId };
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@@ -112,6 +113,9 @@ export function AdvancedLaboratoryResult({
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if (workId === "m49-tgs-fail-closed-evidence" && results.m49Tgs) {
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return <M49TgsFailClosedResultView rigLabel={rigLabel} result={results.m49Tgs} />;
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}
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if (workId === "m49-tgs-full-shadow" && results.m49TgsFull) {
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return <M49TgsFullShadowResultView rigLabel={rigLabel} result={results.m49TgsFull} />;
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}
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if (workId === "m47-reference-graph-shadow" && results.m47Graph) {
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return <M47ReferenceGraphResultView rigLabel={rigLabel} result={results.m47Graph} />;
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}
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@@ -0,0 +1,164 @@
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import { useCallback, useEffect, useMemo, useRef, useState } from "react";
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import type {
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RecordedEvidenceSemanticClass,
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RecordedEvidenceSemanticPaletteEntry,
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} from "../../components/laboratory/RecordedEvidenceSemanticMaskOverlay";
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import {
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fetchM49TgsFullShadowSpatialChunk,
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type M49TgsFullShadowResult,
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type M49TgsFullShadowSpatial,
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type M49TgsFullShadowSpatialChunk,
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type M49TgsFullShadowStateCode,
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} from "../../core/laboratory/m49TgsFullShadow";
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import {
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M4ReplayThreatVisual,
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type M4ReplayClassifiedSpatialFrame,
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} from "./M4ReplayThreatVisual";
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const CLASSES: readonly RecordedEvidenceSemanticClass[] = [
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{ id: 1, label: "Ground support" },
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{ id: 2, label: "Non-ground occupied" },
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{ id: 3, label: "Unknown / rejected" },
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];
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const PALETTE: readonly RecordedEvidenceSemanticPaletteEntry[] = [
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{ classId: 1, color: { kind: "token", token: "--nodedc-success-rgb" } },
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{ classId: 2, color: { kind: "token", token: "--nodedc-danger-rgb" } },
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{ classId: 3, color: { kind: "token", token: "--nodedc-warning-rgb" } },
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];
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const CHUNK_FRAMES = 24;
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const RETAINED_CHUNKS = 4;
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function cellState(code: M49TgsFullShadowStateCode): M4ReplayClassifiedSpatialFrame["cellsMapGravityLocal"][number]["state"] {
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if (code === 1) return "ground-support";
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if (code === 2) return "nonground-occupied";
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if (code === 3) return "unknown-rejected";
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return "unobserved";
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}
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function message(error: unknown): string {
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return error instanceof Error && error.message.trim()
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? error.message
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: "Полный TGS spatial frame недоступен.";
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}
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export function M49TgsFullShadowEvidence({ result }: { result: M49TgsFullShadowResult }) {
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const [activeSequence, setActiveSequence] = useState<number | null>(null);
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const [chunks, setChunks] = useState<ReadonlyMap<number, M49TgsFullShadowSpatialChunk>>(
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() => new Map(),
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);
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const [error, setError] = useState<string | null>(null);
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const inFlightRef = useRef(new Map<number, AbortController>());
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const activeChunkStart = activeSequence === null
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? null
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: Math.floor(activeSequence / CHUNK_FRAMES) * CHUNK_FRAMES;
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const activeChunkStartRef = useRef(activeChunkStart);
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activeChunkStartRef.current = activeChunkStart;
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useEffect(() => {
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for (const controller of inFlightRef.current.values()) controller.abort();
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inFlightRef.current.clear();
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setChunks(new Map());
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setError(null);
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return () => {
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for (const controller of inFlightRef.current.values()) controller.abort();
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inFlightRef.current.clear();
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};
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}, [result.resultId]);
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useEffect(() => {
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if (activeChunkStart === null) return;
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const desiredStarts = [activeChunkStart, activeChunkStart + CHUNK_FRAMES]
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.filter((start) => start < result.timeline.frameCount);
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const desired = new Set(desiredStarts);
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for (const [start, controller] of inFlightRef.current) {
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if (desired.has(start)) continue;
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controller.abort();
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inFlightRef.current.delete(start);
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}
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for (const start of desiredStarts) {
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if (chunks.has(start) || inFlightRef.current.has(start)) continue;
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const controller = new AbortController();
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inFlightRef.current.set(start, controller);
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void fetchM49TgsFullShadowSpatialChunk(result.resultId, start, CHUNK_FRAMES, {
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signal: controller.signal,
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})
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.then((chunk) => {
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if (controller.signal.aborted) return;
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setChunks((current) => {
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const next = new Map(current);
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next.set(start, chunk);
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const center = activeChunkStartRef.current ?? start;
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const retained = [...next.keys()]
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.sort((left, right) => Math.abs(left - center) - Math.abs(right - center))
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.slice(0, RETAINED_CHUNKS);
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return new Map(retained.map((key) => [key, next.get(key)!]));
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});
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if (start === activeChunkStartRef.current) setError(null);
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})
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.catch((caught: unknown) => {
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if (!controller.signal.aborted && start === activeChunkStartRef.current) {
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setError(message(caught));
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}
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})
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.finally(() => {
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if (inFlightRef.current.get(start) === controller) inFlightRef.current.delete(start);
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});
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break;
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}
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}, [activeChunkStart, chunks, result.resultId, result.timeline.frameCount]);
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const spatial = useMemo<M49TgsFullShadowSpatial | null>(() => {
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if (activeSequence === null || activeChunkStart === null) return null;
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return chunks.get(activeChunkStart)?.frames.find(
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(frame) => frame.sourceSequence === activeSequence,
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) ?? null;
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}, [activeChunkStart, activeSequence, chunks]);
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const loading = activeSequence !== null && !spatial && !error;
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const classifiedFrame = useMemo<M4ReplayClassifiedSpatialFrame | null>(() => {
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if (!spatial) return null;
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return {
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sourceSequence: spatial.sourceSequence,
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sampleAvailable: spatial.sampleAvailable,
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sourcePointCount: spatial.metrics.eligiblePointCount,
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pointsMapGravityLocalXyzM: [],
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pointClassIds: [],
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cellsMapGravityLocal: spatial.costmap.centersXyM.map((center, index) => ({
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centerXyM: center,
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zBoundsM: spatial.costmap.zBoundsM[index]!,
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state: cellState(spatial.costmap.states[index]!),
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})),
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cellSizeM: spatial.costmap.cellSizeM,
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classes: CLASSES,
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palette: PALETTE,
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};
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}, [spatial]);
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const handleSequenceChange = useCallback((sequence: number | null) => {
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setActiveSequence(sequence);
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}, []);
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return (
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<M4ReplayThreatVisual
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resultId={result.source.linkedVisualResultId}
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showReviewAnchorBoxes={false}
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reviewLabel="4 489 source-paced TGS frames"
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evidenceLabel="M49 · full TGS shadow"
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initialSpatialMode="3d"
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onActiveSequenceChange={handleSequenceChange}
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classifiedSpatialLayer={{
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label: "TGS full shadow · causal rolling 1 s",
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pointLayerLabel: "SOURCE POINTS",
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cellLayerLabel: "TGS COSTMAP",
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expectedAtSequence: true,
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frame: classifiedFrame,
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loading,
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error,
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replacePointCloud: false,
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}}
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/>
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);
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}
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@@ -0,0 +1,90 @@
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import {
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LaboratoryEvidence,
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LaboratoryResultSummary,
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LaboratorySummary,
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LaboratoryWorkTemplate,
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} from "../../components/laboratory/LaboratoryPresentation";
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import type { M49TgsFullShadowResult } from "../../core/laboratory/m49TgsFullShadow";
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import { M49TgsFullShadowEvidence } from "./M49TgsFullShadowEvidence";
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function number(value: number, digits = 1): string {
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return value.toLocaleString("ru-RU", { maximumFractionDigits: digits });
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}
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export function M49TgsFullShadowResultView({
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rigLabel,
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result,
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}: {
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rigLabel: string;
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result: M49TgsFullShadowResult;
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}) {
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const accepted = result.decision.candidateRetained;
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const status = accepted
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? "Source-paced CPU shadow принят; визуальное и integrated-graph качество ещё проверяются"
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: "Source-paced CPU shadow не прошёл performance gate";
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return (
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<LaboratoryWorkTemplate
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summary={(
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<LaboratorySummary
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title="M4.9T5 · полный source-paced TRAVEL TGS shadow"
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description="Один CPU-only процесс прошёл весь recorded timeline RAVNOVES00 в исходном темпе. Камера остаётся владельцем времени; dense source cloud сохраняется, поверх него показывается четырёхсостояний TGS costmap."
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status={status}
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statusTone={accepted ? "success" : "danger"}
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facts={[
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{ label: "Конфигурация", value: `${rigLabel} RIGHT · gravity-aligned LiDAR · causal ${number(result.configuration.historySeconds)} с` },
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{ label: "Timeline", value: `${result.timeline.frameCount.toLocaleString("ru-RU")} кадров · ${result.timeline.availableLidarFrameCount.toLocaleString("ru-RU")} LiDAR · ${result.timeline.missingLidarFrameCount} UNOBSERVED` },
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{ label: "Нагрузка", value: `Worker 006 CPU-only · GPU 0 · ${number(result.timeline.effectiveFps, 3)} source FPS` },
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{ label: "Authority", value: "REPLAY-SIMULATED · navigation/actuation OFF · integrated graph отдельно" },
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]}
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brief={{
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question: "Удерживает ли готовый TRAVEL TGS полный десятигерцовый replay без очереди и потери кадров?",
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approach: "Все 4 489 camera frames планируются по исходным timestamps. Для 3 928 доступных LiDAR frames выполняется causal rolling 1 s; 561 пропуск остаётся полностью UNOBSERVED.",
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principalResult: `${result.timeline.frameCount}/4 489 frames и ${result.pointAccounting.eligible.toLocaleString("ru-RU")} eligible points учтены; TGS p95/p99 ${number(result.performance.candidateTgsMs.p95, 2)}/${number(result.performance.candidateTgsMs.p99, 2)} мс, completion age p99 ${number(result.performance.completionAgeMs.p99, 2)} мс, capacity drops ${result.performance.capacityDropCount}.`,
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limitation: "Это isolated CPU shadow. Он ещё не доказывает качество красных occupied-ячеек, проходимость для конкретного корпуса или регрессию FPS полного world-state graph.",
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}}
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method={{
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completeness: "complete",
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executionClass: "deterministic",
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pipelineId: "travel-tgs-full-source-paced-shadow/v1",
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components: [
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{ kind: "source", name: "RAVNOVES00", version: "4 489-frame recorded timeline", role: "camera-owned source clock + registered LiDAR", identitySha256: result.source.sourcePackSha256 },
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{ kind: "algorithm", name: "TRAVEL GroundSeg", version: "95dc2fbd66a343efd9060c45a5711b6307a950a4", role: "gravity-aligned ground/non-ground separation; AOS OFF", identitySha256: result.configuration.configSha256 },
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{ kind: "algorithm", name: "fail-closed costmap adapter", version: "v1", role: "occupied > rejected > ground > unobserved; no free inference", identitySha256: result.resultId.split("-").at(-1) ?? null },
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],
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}}
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/>
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)}
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evidence={(
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<LaboratoryEvidence
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eyebrow="M4.9T5 VISUAL EVIDENCE · FULL CAMERA TIMELINE + SOURCE CLOUD + TGS COSTMAP"
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title="Полный timeline: dense исходные точки сохранены, TGS-ячейки синхронны каждому кадру"
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kind="recorded-replay"
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resizable
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>
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<M49TgsFullShadowEvidence result={result} />
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</LaboratoryEvidence>
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)}
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result={(
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<LaboratoryResultSummary
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title="Что доказал полный прогон"
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status={status}
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statusTone={accepted ? "success" : "danger"}
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metrics={[
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{ label: "Timeline", value: `${result.timeline.frameCount}/4 489`, hint: `${result.timeline.availableLidarFrameCount} LiDAR + ${result.timeline.missingLidarFrameCount} explicit UNOBSERVED` },
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{ label: "TGS p95 / p99", value: `${number(result.performance.candidateTgsMs.p95, 2)} / ${number(result.performance.candidateTgsMs.p99, 2)} мс`, hint: "чистый candidate stage на CPU" },
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{ label: "Completion age p99", value: `${number(result.performance.completionAgeMs.p99, 2)} мс`, hint: "от source timestamp до готового frame result" },
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{ label: "Capacity drops", value: String(result.performance.capacityDropCount), hint: "кадры не отбрасывались ради темпа" },
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{ label: "Point accounting", value: "100%", hint: `${result.pointAccounting.eligible.toLocaleString("ru-RU")} eligible points` },
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]}
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conclusion={{
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proved: "Полный CPU-only TGS shadow воспроизводимо проходит recorded source clock, сохраняет fail-closed представление и не использует AOS/GPU.",
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notProved: "Не приняты visual traversability, модель корпуса, камера-проекция TGS и нагрузка после встраивания в полный realtime world-state graph.",
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decision: accepted
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? "Кандидат остаётся. Просмотреть полный timeline, затем подключить shadow к realtime graph и измерить общий FPS/latency regression."
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: "Кандидат не встраивать; сначала локализовать performance gate, который не прошёл полный replay.",
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}}
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/>
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)}
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/>
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);
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}
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@@ -109,6 +109,8 @@ export interface M4ReplayThreatReviewAnchor {
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export interface M4ReplayClassifiedSpatialFrame {
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sourceSequence: number;
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sampleAvailable?: boolean;
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sourcePointCount?: number;
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pointsMapGravityLocalXyzM: readonly (readonly [number, number, number])[];
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pointClassIds: readonly (number | null)[];
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cellsMapGravityLocal: readonly {
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@@ -129,6 +131,7 @@ export interface M4ReplayClassifiedSpatialLayer {
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frame: M4ReplayClassifiedSpatialFrame | null;
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loading: boolean;
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error: string | null;
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replacePointCloud?: boolean;
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}
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const EMPTY_REVIEW_ANCHORS: readonly M4ReplayThreatReviewAnchor[] = [];
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@@ -255,8 +258,8 @@ export function M4ReplayThreatVisual({
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if (timelineFrame.activeFrame) lastFrameRef.current = timelineFrame.activeFrame;
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const frame = timelineFrame.activeFrame ?? lastFrameRef.current;
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useEffect(() => {
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onActiveSequenceChange?.(frame?.sequence ?? null);
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}, [frame?.sequence, onActiveSequenceChange]);
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onActiveSequenceChange?.(timelineFrame.activeSequence);
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}, [onActiveSequenceChange, timelineFrame.activeSequence]);
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const lastSpatialFrameRef = useRef<{
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resultId: string;
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frame: M4ThreatTimelineFrame;
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@@ -400,16 +403,18 @@ export function M4ReplayThreatVisual({
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return status === 2 || status === 3 ? classId : null;
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});
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}, [semantic, semanticIntegrityError, showSpatialSemantic, spatialFrame, spatialSemanticFrame]);
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const classifiedSpatialFrame = !displayingBufferedFrame
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&& classifiedSpatialLayer?.frame?.sourceSequence === frame?.sequence
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&& spatialFrame?.sequence === frame?.sequence
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const activeSpatialFrame = spatialFrame?.sequence === timelineFrame.activeSequence
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? spatialFrame
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: null;
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const classifiedSpatialFrame = classifiedSpatialLayer?.frame?.sourceSequence === timelineFrame.activeSequence
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? classifiedSpatialLayer?.frame ?? null
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: null;
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const replaceClassifiedPointCloud = classifiedSpatialLayer?.replacePointCloud ?? true;
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const nominalSensorHeightM = metadata.timeline?.rig.nominalSensorHeightM ?? 0;
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const mapGravityLocalSensorToBodyGround = useCallback((
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point: readonly [number, number, number],
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): readonly [number, number, number] => {
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const basis = spatialFrame?.bodyFrame?.basisMapFromBody;
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const basis = activeSpatialFrame?.bodyFrame?.basisMapFromBody;
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const rotated: readonly [number, number, number] = basis ? [
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basis[0][0] * point[0] + basis[1][0] * point[1] + basis[2][0] * point[2],
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basis[0][1] * point[0] + basis[1][1] * point[1] + basis[2][1] * point[2],
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@@ -418,7 +423,7 @@ export function M4ReplayThreatVisual({
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// TGS evidence is translation-only map-gravity-local with the current LiDAR
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// as its origin. The metric scene uses the body ground projection as z=0.
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return [rotated[0], rotated[1], rotated[2] + nominalSensorHeightM];
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}, [nominalSensorHeightM, spatialFrame?.bodyFrame?.basisMapFromBody]);
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}, [activeSpatialFrame?.bodyFrame?.basisMapFromBody, nominalSensorHeightM]);
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const classifiedPointsBody = useMemo(
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() => classifiedSpatialFrame?.pointsMapGravityLocalXyzM.map(
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mapGravityLocalSensorToBodyGround,
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@@ -776,7 +781,11 @@ export function M4ReplayThreatVisual({
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? splitPrimarySize
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: 100;
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const overlay = metadata.timeline && frame ? (
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const overlaySequence = timelineFrame.activeSequence ?? frame?.sequence ?? null;
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const overlaySessionSeconds = overlaySequence === null
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? null
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: (metadata.timeline?.frameTimesNs[overlaySequence] ?? 0) / 1_000_000_000;
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const overlay = metadata.timeline && overlaySequence !== null ? (
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<div
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className="l3-visual-audit__overlay m4-replay-threat-visual__overlay"
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style={{
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@@ -785,9 +794,9 @@ export function M4ReplayThreatVisual({
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>
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<div>
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<span>RAVNOVES00 · recorded realtime</span>
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<strong>frame {frame.sequence + 1}/{metadata.timeline.frameCount}</strong>
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<strong>frame {overlaySequence + 1}/{metadata.timeline.frameCount}</strong>
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<small>
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+{(frame.sessionSeconds - metadata.timeline.timelineStartSeconds).toFixed(3)} с
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+{((overlaySessionSeconds ?? metadata.timeline.timelineStartSeconds) - metadata.timeline.timelineStartSeconds).toFixed(3)} с
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· {displayingBufferedFrame
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? "держим последний кадр, следующий в буфере"
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: playbackController.playback.playing ? "воспроизведение" : "пауза / seek"}
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@@ -797,24 +806,32 @@ export function M4ReplayThreatVisual({
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<span>Spatial evidence</span>
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<strong>{classifiedSpatialLayer
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? classifiedSpatialFrame
|
||||
? `${classifiedSpatialFrame.pointsMapGravityLocalXyzM.length.toLocaleString("ru-RU")} TGS points · ${classifiedSpatialFrame.cellsMapGravityLocal.length.toLocaleString("ru-RU")} cells`
|
||||
? replaceClassifiedPointCloud
|
||||
? `${classifiedSpatialFrame.pointsMapGravityLocalXyzM.length.toLocaleString("ru-RU")} TGS points · ${classifiedSpatialFrame.cellsMapGravityLocal.length.toLocaleString("ru-RU")} cells`
|
||||
: `${(activeSpatialFrame?.pointCloudSourceCount ?? classifiedSpatialFrame.sourcePointCount ?? 0).toLocaleString("ru-RU")} source points · ${classifiedSpatialFrame.cellsMapGravityLocal.length.toLocaleString("ru-RU")} TGS cells`
|
||||
: "TGS spatial buffer"
|
||||
: `${currentIncrementObstacles.length} current · ${rollingMapObstacles.length} rolling${metadata.timeline.occupancyProvenanceDelivery ? ` · ${lowStepObstacles.length} low-step` : ""}`}</strong>
|
||||
<small>{classifiedSpatialLayer
|
||||
? classifiedSpatialFrame
|
||||
? "map-gravity-local · all eligible points accounted · causal rolling 1 s"
|
||||
? classifiedSpatialFrame.sampleAvailable === false
|
||||
? "LiDAR отсутствует · все ячейки принудительно UNOBSERVED · causal rolling 1 s"
|
||||
: activeSpatialFrame
|
||||
? "map-gravity-local · all eligible points accounted · causal rolling 1 s"
|
||||
: "TGS рассчитан · linked source cloud недоступен для этого кадра"
|
||||
: classifiedSpatialLayer.error ?? `Открываем ${classifiedSpatialLayer.label}`
|
||||
: (
|
||||
<>
|
||||
{spatialFrame
|
||||
? `${spatialFrame.pointCloudSampleCount}/${spatialFrame.pointCloudSourceCount} exact · ${localSurface.pointsBodyXyzM.length} local SLAM / ${localSurface.sourceFrameCount} frames`
|
||||
: "квалифицированный spatial frame ещё не получен"}
|
||||
{frame.worldStateAvailable
|
||||
? " · world-state delivered"
|
||||
: ` · world-state gap (${frame.terminalOutcome})`}
|
||||
{frame
|
||||
? frame.worldStateAvailable
|
||||
? " · world-state delivered"
|
||||
: ` · world-state gap (${frame.terminalOutcome})`
|
||||
: " · world-state frame unavailable"}
|
||||
{accumulatedCameraPoints
|
||||
? ` · camera points ${accumulatedCameraPoints.sampleCount}/${accumulatedCameraPoints.projectedPointCount} · causal ${accumulatedCameraPoints.windowSeconds.toFixed(1)} с / ${accumulatedCameraPoints.sourceFrameCount} frames`
|
||||
: pointCloudOverlay
|
||||
: pointCloudOverlay && frame
|
||||
? ` · camera points ${frame.cameraProjectedSampleCount}/${frame.cameraProjectedPointCount} exact-current · накопление загружается`
|
||||
: showMediaPoints && cameraPointOverlay.error
|
||||
? " · накопленное camera cloud недоступно"
|
||||
@@ -935,12 +952,12 @@ export function M4ReplayThreatVisual({
|
||||
</div>
|
||||
</div>
|
||||
) : null}
|
||||
{spatialFrame && (!classifiedSpatialLayer || classifiedSpatialFrame) ? (
|
||||
{(!classifiedSpatialLayer ? spatialFrame : classifiedSpatialFrame) ? (
|
||||
<LaboratoryMetricEvidenceScene
|
||||
ref={metricSceneRef}
|
||||
pointCloudBodyXyzM={classifiedSpatialFrame
|
||||
pointCloudBodyXyzM={classifiedSpatialFrame && replaceClassifiedPointCloud
|
||||
? classifiedPointsBody
|
||||
: spatialFrame.pointCloudBodyXyzM}
|
||||
: activeSpatialFrame?.pointCloudBodyXyzM ?? []}
|
||||
localSurfaceBodyXyzM={classifiedSpatialFrame ? [] : localSurface.pointsBodyXyzM}
|
||||
obstacles={classifiedSpatialFrame ? [] : sceneObstacles}
|
||||
rig={timeline.rig}
|
||||
@@ -952,13 +969,13 @@ export function M4ReplayThreatVisual({
|
||||
showLocalSurface={classifiedSpatialFrame ? false : showLocalSurface}
|
||||
showRollingMap={showRollingMap}
|
||||
showLowStep={classifiedSpatialFrame ? false : showLowStep}
|
||||
pointSemanticClassIds={classifiedSpatialFrame
|
||||
pointSemanticClassIds={classifiedSpatialFrame && replaceClassifiedPointCloud
|
||||
? classifiedSpatialFrame.pointClassIds
|
||||
: alignedSemanticPointIds}
|
||||
semanticClasses={classifiedSpatialFrame
|
||||
semanticClasses={classifiedSpatialFrame && replaceClassifiedPointCloud
|
||||
? classifiedSpatialFrame.classes
|
||||
: semanticClasses}
|
||||
semanticPalette={classifiedSpatialFrame
|
||||
semanticPalette={classifiedSpatialFrame && replaceClassifiedPointCloud
|
||||
? classifiedSpatialFrame.palette
|
||||
: semanticPalette}
|
||||
classifiedCells={classifiedCellsBody}
|
||||
@@ -979,7 +996,15 @@ export function M4ReplayThreatVisual({
|
||||
: `${classifiedSpatialLayer.label} рассчитан только на 10 контрольных кадров.`)}</span>
|
||||
</div>
|
||||
) : null}
|
||||
{frame && !frame.spatialAvailable ? (
|
||||
{classifiedSpatialFrame?.sampleAvailable === false ? (
|
||||
<div className="m4-replay-threat-visual__pane-status" role="status">
|
||||
Кадр {classifiedSpatialFrame.sourceSequence + 1}: LiDAR отсутствует; все 2 244 TGS-ячейки явно UNOBSERVED.
|
||||
</div>
|
||||
) : classifiedSpatialFrame && !activeSpatialFrame ? (
|
||||
<div className="m4-replay-threat-visual__pane-status" role="status">
|
||||
Кадр {classifiedSpatialFrame.sourceSequence + 1}: TGS costmap показан cell-only; linked source cloud для отрисовки отсутствует.
|
||||
</div>
|
||||
) : frame && !frame.spatialAvailable ? (
|
||||
<div className="m4-replay-threat-visual__pane-status" role="status">
|
||||
{spatialFrame
|
||||
? `На кадре ${frame.sequence + 1} нет body frame; держим spatial evidence кадра ${spatialFrame.sequence + 1}.`
|
||||
|
||||
@@ -112,6 +112,13 @@ const KNOWN_WORKS: Readonly<Record<Exclude<LaboratoryWorkId, `session:${string}`
|
||||
experimentName: "TRAVEL TGS fail-closed traversability evidence",
|
||||
variantName: "M4.9T4 · 10 anchors · causal rolling 1 s · AOS OFF",
|
||||
},
|
||||
"m49-tgs-full-shadow": {
|
||||
profileId: "rig-dual-evidence-virtual-corridor-v1",
|
||||
profileName: (rigLabel) => `${rig(rigLabel)} RIGHT · Camera + gravity-aligned LiDAR`,
|
||||
experimentId: "m49-tgs-full-shadow",
|
||||
experimentName: "TRAVEL TGS complete source-paced shadow",
|
||||
variantName: "M4.9T5 · 4 489 frames · causal rolling 1 s · CPU-only",
|
||||
},
|
||||
"m47-reference-graph-shadow": {
|
||||
profileId: "rig-dual-evidence-virtual-corridor-v1",
|
||||
profileName: (rigLabel) => `${rig(rigLabel)} RIGHT · Camera + LiDAR dual evidence`,
|
||||
|
||||
@@ -26,6 +26,7 @@ function mergeResults(
|
||||
m48s: next.m48s ?? current.m48s,
|
||||
m48t: next.m48t ?? current.m48t,
|
||||
m49Tgs: next.m49Tgs ?? current.m49Tgs,
|
||||
m49TgsFull: next.m49TgsFull ?? current.m49TgsFull,
|
||||
m4Threat: next.m4Threat ?? current.m4Threat,
|
||||
l3: next.l3 ?? current.l3,
|
||||
l31: next.l31 ?? current.l31,
|
||||
@@ -126,6 +127,7 @@ export function useAdvancedLaboratoryCatalog({
|
||||
"m48-static-occupancy-qualification",
|
||||
"m48r3-static-occupancy-shadow",
|
||||
"m49-tgs-fail-closed-evidence",
|
||||
"m49-tgs-full-shadow",
|
||||
].includes(selectedWorkId)
|
||||
&& !indexedResultId
|
||||
) return;
|
||||
|
||||
Reference in New Issue
Block a user