Autonomous Vehicle Driving Dataset

Project Overview:

Objective

Autonomous Vehicle Driving Dataset: The aim is to create a dataset that makes self-driving systems more accurate and efficient by using different types of real driving data.

Scope

The dataset contains various driving situations, environmental conditions, and how vehicles interact to simulate real-life driving accurately.

Vehicle Recognition for Toll Collection

Sources

  • Real Driving Sessions: We gathered information from real driving experiences, including different types of weather and various city and countryside locations.
  • Simulated Environments: Using simulations, we obtained data on uncommon but important driving situations necessary for thorough testing of autonomous driving systems.

Data Collection Metrics

  • Total Data Collected: 100,000 pictures and videos.
  • Data Annotated for ML Training: 120,000 pictures and videos with detailed labels added for machine learning use.

Annotation Process

Stages

  1. Behavioral States: We labeled different actions like changing lanes, stopping, and how fast the vehicle accelerates.
  2. Object Tracking: We carefully marked every moving and still object in the scene, like other vehicles, people walking, and traffic signals.
  3. Scene Segmentation: We divided the scene into parts like roads, lanes, footpaths, and barriers to help understand how the car moves.

Annotation Metrics

  • Annotated Behaviors: We’ve noted down 120,000 driving behaviors in detail.
  • Object Labels: There are 110,000 labels tracking all objects in each scene.
  • Segmentation Maps: We’ve created 100,000 maps showing the layout of different driving areas in detail.
Vehicle Recognition for Toll Collection

Quality Assurance

Stages

  • Continuous Model Testing: We regularly test our dataset to make sure it’s accurate and useful for real-life driving situations.
  • Privacy and Security: We follow strict privacy laws to make sure all the data we collect is anonymous and collected responsibly.
  • Improvement Process: We listen to feedback from how well our dataset works and use it to make our data collection and labeling better.

QA Metrics

  • Behavior Recognition Accuracy: We correctly identified detailed driver behaviors with a high accuracy of 97%.
  • Object Detection Accuracy: We successfully detected and tracked different objects with an accuracy of 95%.
  • Privacy Compliance: We followed all international rules about data protection and privacy, achieving 100% compliance.

Conclusion

The creation of the KITTI Vision Benchmark dataset is a big step forward in self-driving car technology. It gives a thorough and very accurate view of different driving situations, which is important for teaching advanced and safe self-driving systems.

quality dataset

Quality Data Creation

Guaranteed TAT​

Guaranteed TAT

ISO 9001:2015, ISO/IEC 27001:2013 Certified​

ISO 9001:2015, ISO/IEC 27001:2013 Certified

HIPAA Compliance​

HIPAA Compliance

GDPR Compliance​

GDPR Compliance

Compliance and Security​

Compliance and Security

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