CASE STUDIES
Automotive Lane Detection for Autonomous
A Global Automotive Manufacturer
V7 Labs and SuperAnnotate
Objective
To develop an accurate lane detection model for autonomous vehicles, enabling safe navigation in various weather conditions, including rain, snow, and fog.
Process:
- Data Annotation: Employed V7 Labs and SuperAnnotate to annotate lane boundaries, road signs, and obstacles in diverse driving conditions.
- Human-in-the-loop (HITL) System: Human annotators reviewed AI-generated lane detections, especially in edge cases like faded lane markings or obstructed views.
- Ongoing Review: Human reviewers continually assessed complex scenarios (e.g., poorly marked roads, construction zones) to provide accurate annotations for improving the model.
Challenges:
- Adverse Weather Conditions: AI struggled with lane detection in rain or snow, necessitating manual annotations.
- Complex Road Networks: Human annotators provided accurate labels for complex areas like intersections and merging lanes.
- Large Volumes of Data: Managed large dashcam data volumes through automated pre-processing combined with human verification.
Outcome
Achieved 97% accuracy in lane detection, even in adverse weather. Human intervention was reduced by 50%, enhancing efficiency and lowering manual annotation costs long-term.
