Driver Behaviour Data Collection

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Project Overview:

Objective

Our goal was to compile a comprehensive dataset detailing various facets of driver behavior. This data, encompassing aspects like vehicle speed, acceleration, braking, steering, and environmental conditions, is crucial for advancing research in driver safety, enhancing driver assistance systems, and refining machine learning algorithms.

Scope

We successfully gathered real-world driver behavior data through advanced sensor technology and onboard vehicle systems. Our focus was not just on data collection but also on ensuring the utmost privacy and safety standards.

Sources

Vehicle Sensors: Utilize the sensors available in modern vehicles, such as GPS, accelerometers, gyroscopes, and vehicle speed sensors.
Camera Systems: Install interior-facing and exterior-facing cameras to capture driver behavior and the surrounding environment.
Onboard Diagnostics (OBD) Devices: Access vehicle diagnostics data through OBD-II ports to monitor parameters like engine RPM, fuel consumption, and fault codes.

Data Collection Metrics

Number of Vehicles: We deployed our data collection methodology across 100 vehicles.
Data Collection Period: This project spanned a rich six-month period.
Volume of Data: A staggering 5 TB of data was collected and 3 TB annotated.

Annotation Process

Stages

Vehicle Dynamics: Speed, acceleration, and braking profiles.
Steering Patterns: Comprehensive analysis of steering wheel movements.
Lane Keeping Metrics: Insights into lane adherence behaviors.
Environmental Factors: Weather and road conditions during different driving scenarios.

Annotation Metrics

Weather Conditions: Collect data on weather conditions such as rain, snow, fog, or clear skies.
Road Conditions: Document road surface conditions, traffic congestion, and road type (e.g., highway, city streets).

Quality Assurance

Stages

Data Quality: Implement data quality checks to ensure accuracy and reliability of collected data.
Privacy Protection: Strictly adhere to privacy regulations and obtain informed consent from participants when required. Anonymize data to protect driver identities.
Safety Measures: Ensure that data collection does not compromise driver safety. Implement safety mechanisms to minimize distractions.

QA Metrics

Data Accuracy: Regularly validate data accuracy.
Privacy Compliance: Regularly audit data handling processes for privacy compliance.
Safety Measures: Implement safety protocols to ensure data collection does not endanger drivers.

Conclusion

The Driver Behavior Collection Dataset provides valuable insights into driver behavior and road conditions, making it a valuable resource for road safety research and the development of advanced driver assistance systems. With diverse data sources and stringent privacy and safety measures, it offers a comprehensive dataset while prioritizing data privacy and driver safety.