Vehicle Driving Behaviors Video Dataset
Project Overview:
Objective
Developing a dataset comprising videos that capture various vehicle driving behaviors is essential for enhancing AI models for advanced driver assistance systems (ADAS), driver monitoring systems, and autonomous vehicle training.
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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