Vehicle Driving Behaviors Video Dataset
Project Overview:
Objective
Scope
Collect video clips that demonstrate a wide array of driving behaviors: regular driving, aggressive driving, distracted driving, and more. Annotations will highlight the particular behavior while providing metadata concerning its intensity, context, and potential safety hazards.
Sources
- By utilizing voluntarily contributed dashcam footage from both volunteers and fleet vehicles, we have thoughtfully collected and successfully curated a comprehensive set of real-world driving experiences.
Ethically sourced data from traffic surveillance cameras, obtained with necessary permissions, contributes to a meticulously collected and successfully curated dataset.
With the necessary permissions, we ethically source data from traffic surveillance cameras. Consequently, we contribute to a meticulously collected and successfully curated dataset.
- Engaged in collaborations with driving schools, leading to a carefully collected and successfully curated set of educational resources.
- Moreover, I actively developed and implemented simulated driving scenarios using virtual reality. As a result, I successfully collected a comprehensive dataset for training purposes.
Data Collection Metrics
- Total Video Clips: 150,000
- Regular Driving: 50,000
- Aggressive Driving: 30,000
- Distracted Driving: 25,000
- Defensive Driving: 20,000
- Other Behaviors (e.g., drowsy driving): 25,000
Annotation Process
Stages
- Video Pre-processing: Standardization for resolution and frame rate.
- Behavior Highlighting: Marking the start and end timecodes of specific behaviors.
- Behavior Classification: Then, we categorize the identified behaviors, such as overtaking or phone use.
- Metadata Annotation: Additionally, we capture contextual information, including driving conditions (night, rain), traffic intensity, and potential risks.
- Validation: Finally, expert reviewers and preliminary behavior detection models ensure the annotations are accurate.
Annotation Metrics
- Total Behavior Annotations: 300,000 (some clips may contain multiple behaviors)
- Metadata Annotations: 300,000
Quality Assurance
Stages
Automated Behavior Recognition Verification:To begin with, early detection models cross-check the highlighted behaviors.
Peer Review:Subsequently, a different set of annotators reassess the video clips for comprehensive validation.
Inter-annotator Agreement:Furthermore, a subset of videos undergoes multiple annotations to ensure uniformity in behavior classification.
QA Metrics
- Annotations Validated using Behavior Detection Models: 75,000 (50% of total clips)
- Peer Reviewed Annotations: 45,000 (30% of total clips)
- Inconsistencies Identified and Addressed: 3,000 (2% of total clips)
Conclusion
The Vehicle Driving Behaviors Video Dataset stands as a keystone in the realm of driving safety and autonomous vehicle training. By offering a rich tapestry of driving behaviors across varied contexts, it equips AI models to recognize and respond to real-world driving dynamics. Consequently, this dataset is primed to significantly bolster efforts to make roads safer and advance the capabilities of self-driving technologies.
Quality Data Creation
Guaranteed TAT
ISO 9001:2015, ISO/IEC 27001:2013 Certified
HIPAA Compliance
GDPR Compliance
Compliance and Security
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