AUTOMOTIVE DATA SOLUTIONS

Automotive data solutions

Complex roads.Traceable perception data.

Connect collection, annotation and quality review, from scenario definition to batch delivery. Organize inspectable, iterative data for camera, video, LiDAR and fusion tasks.

Discuss your automotive project Explore sensor workflows

Multisensor scene · 2D / 3D association

Input setup6 Camera + 1 LiDAR

Task coverage2D / 3D / Tracking

Delivery unitsSequence · Scene · Batch

MULTISENSOR ASSOCIATION VIEW

CONSISTENT TASK STANDARDSShared scene, identity and timing conventions connect multimodal production with reviewable, traceable batches.

BUILT FOR PERCEPTION

Organize data around the perception task

Define what the model needs to recognize and where it will operate, then translate those needs into collection and annotation rules.

01

Complex scenario coverage

Look beyond everyday roads to low light, occlusion, roadworks and mixed traffic.

Use a scenario matrix to define coverage, gaps and additional collection needs.

02

Consistency across sensors and time

Views, frames and sensors need shared conventions.

Calibrate identities, coordinates, timestamps and associations, retaining the review records.

03

Traceable batches and versions

As volume grows, data origins and rule versions must stay clear.

Organize labels, issues, rule versions and delivery manifests by sequence and batch.

SENSOR WORKFLOWS

Four sensor workflows. Clear delivery requirements.

Choose a modality to inspect inputs, tasks and deliverables. Specific formats and acceptance criteria are agreed during the pilot.

CAMERA

Annotations for urban-road objects, lane structure and semantic regions.

Object detection
Lane lines
Road semantics

CAMERA

Understand road objects and structure

Organize objects, lanes and drivable areas into consistent training labels for front and surround-view perception.

Data inputs: Image sequences, camera parameters, scene tags and class definitions.
Task design: 2D objects, lanes, traffic signs, semantic regions and occlusion attributes.
Deliverables: Frame labels, scene index, rule version and issue records.

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SCENARIO COVERAGE

Make edge cases part of the data plan

Allocate 1,200 synchronized keyframe groups across intersections, night scenes, roadworks and mixed traffic, balancing common tasks with edge cases.

01SCENE COVERAGE

Urban intersections

Cross traffic, turning vehicles and pedestrian crossings

400synchronized keyframe groups

Check class and track consistency where pedestrians and cyclists interact.

02SCENE COVERAGE

Night & backlight

Low light, headlight glare and lighting transitions

300synchronized keyframe groups

Define visible boundaries and rules for uncertain observations.

03SCENE COVERAGE

Roadworks

Cones, temporary lanes and nonstandard road edges

200synchronized keyframe groups

Define temporary traffic elements and changes to drivable areas.

04SCENE COVERAGE

Mixed traffic

Close occlusion, two-wheelers and dense objects

300synchronized keyframe groups

Review small objects, partial occlusion and identity recovery across frames.

Adjust coverage, allocation and edge-case definitions to the model task, then review gaps in each batch.

CONNECTED DELIVERY

From scenario requirements to reviewable batches

Carry the same task definition through every stage so collection, annotation and quality review use consistent criteria.

01
Define & collect
Agree on scenarios, modalities, synchronization and metadata; check sample completeness.
Scenario plan / Raw-data index
02
Pilot & calibrate
Use a small batch with edge cases to align classes, geometry, tracks and associations.
Rule version / Boundary examples
03
Produce & review
Work in batches, checking edge cases, associations and consistency; record issues and revisions.
Labels / QA records / Issue log
04
Deliver & iterate
Match manifests to versions and review package structure; carry feedback into subsequent batches.
Batch manifest / Versions / Feedback

CUSTOMER CASE

Multisensor perception on urban roads

AUTO-001 · Urban-road perception

Twelve sequences connect scenario coverage, sensor associations and batch delivery in a consistent urban-road perception dataset.

Scope: Six cameras and one point-cloud stream across intersections, night scenes, roadworks and mixed traffic.
Challenges: Nighttime object boundaries, close occlusion and shared identities between images and point clouds.
Approach: Calibrate edge-case rules first, check 2D / 3D links by sequence, and organize issues by scenario.

Read the full customer case

Urban-road perception and object annotation

AUTO-001 · Urban-road perception

12sequences

1,200paired keyframe groups

4scenario groups

BATCH DELIVERY STRUCTURE

See how a batch is organized

Use the batch index to connect sequences, scenes, synchronized keyframe groups and sensors, making file scope and versions easier to verify.

Sequence and scenario index
Label and association conventions
Rule and calibration version references
Quality and issue record references

BATCH_001

{
  "containsRawData": false,
  "summary": {
    "sequenceCount": 12,
    "pairedKeyframeCount": 1200
  }
}

Download manifest format sampleJSON · Index & version fields · No raw data

RELATED SERVICES

QUESTIONS & ANSWERS

Align the essentials before starting

01Can we use existing data for annotation and quality review?

Yes, the scope can focus on existing data. Start with de-identified samples, data structures, class rules and output formats, then assess missing metadata and conventions.

02What should we prepare for a fusion task?

Prepare synchronized samples, timestamp definitions, coordinate systems, camera calibration and calibration versions. Validate associations and review methods on a pilot before defining production.

03How are edge cases defined and expanded?

Build a scenario list around roads, lighting, weather, road users and key events. Compare it with project data to identify gaps, then assess additional collection or annotation. Refine scene allocation using project feedback.

04Does this guarantee model accuracy or production performance?

Model performance depends on algorithms, training, data distribution and evaluation conditions. We align delivery to agreed coverage and annotation quality, using a pilot to calibrate requirements.

05How is a batch manifest used in data integration?

Use the manifest to parse directories, link sequences and check versions. Training requires the matching source data and labels; field definitions and references are aligned during the pilot.

START WITH YOUR SCENARIO

Start with a specific perception task

Describe your scenarios, modalities and current stage so we can align task boundaries and sample requirements.

Discuss your automotive project Back to homeStart with your scenes, modalities and development stage.