AUTOMOTIVE CASE STUDY
Automotive case study
A road-perception project shows how scene planning, object associations and issue review become an inspectable, traceable project record.
All case studies
01 / PROJECT BRIEF
A perception team wants to examine scene coverage and object identities across sensors in one road-data batch. Sequences provide the structure; rules and review records follow every revision.
02 / SCENE COVERAGE
Keep each scene’s scope and review focus visible. Select a group to explore the breakdown: 12 sequences and 1,200 synchronized keyframe groups in total.
JUNCTIONS
Turning vehicles, pedestrians and roadside occlusion occur together. Review identity consistency across multiple views.
Example recordSEQ-J01 / KF-042 · Check the association ID for a turning vehicle across adjacent views.
NIGHT & BACKLIGHT
Low illumination, reflections and backlight make boundaries uncertain. Keep uncertainty and review evidence in the record.
Example recordSEQ-N02 / KF-067 · One occluded object association remains unresolved and is excluded from passed groups.
ROADWORKS
Temporary barriers, cones and changing drivable space require consistent categories, attributes and scene tags.
Example recordSEQ-R01 / KF-031 · Check a cone category against the rule version and retain the revision record.
MIXED TRAFFIC
Vehicles, cyclists and pedestrians interact in nearby areas. Review identity switches and short-range occlusion.
Example recordSEQ-M03 / KF-088 · Review a cyclist’s identity in adjacent frames and correct the association.
Four scenes cover 12 sequences and 1,200 synchronized keyframe groups, organized by sequence and rule version.
03 / WORKING METHOD
Align rules with representative examples, then annotate, review and revise by sequence. Every issue leads back to a scene and a rule version.
01 / THE APPROACH
Verify timing and coordinate references, then link images, point clouds and tracks with object IDs. Keep ambiguous links pending.
01 / THE APPROACH
Use edge cases to align visibility and occlusion rules. Record uncertainty according to the rules instead of filling gaps by guesswork.
01 / THE APPROACH
Return each issue to its sequence and keyframe group. Record the initial check, rework and recheck separately.
04 / REVIEW RECORD
This stratified review covers 120 groups: 30 from each scene, so sampling fractions differ with scene size. The other 1,080 groups are uninspected and excluded from every pass rate.
| Scene | Sampled | Initial pass | Recheck pass / rework | Currently passed |
|---|---|---|---|---|
| Junctions | 30 | 29 | 1 / 1 | 30 |
| Night & backlight | 30 | 28 | 1 / 2 | 29 |
| Roadworks | 30 | 29 | 1 / 1 | 30 |
| Mixed traffic | 30 | 29 | 1 / 1 | 30 |
| Total | 120 | 115 | 4 / 5 | 119 |
Rates apply to the stated review sample. Batch acceptance follows agreed criteria; the 1,080 uninspected groups are tracked separately.
Explore delivery and quality05 / HANDOVER STRUCTURE
Organize indexes, labels, rule versions and review records into one manifest for receipt, verification and iteration.
Sequence IDs, timestamps, six image references and one point-cloud reference, grouped by scenario.
2D / 3D objects, track IDs and association tables organized against an agreed rule version.
Sensor setup, coordinate and calibration versions, example notes and change records.
Sample denominators, initial and recheck decisions, revision references and open issues.
LET’S DEFINE YOUR DATA TASK
Describe your sensor setup, scene scope and expected outputs to frame a suitable pilot task.