Automated Data Pipeline for Autonomous Vehicle Logs

Skills, Tech Stack, and Libraries

1. Skills: Data Pipeline Automation, Real-Time Data Processing, Big Data Management, Feature Engineering

2. Tech Stack: Python, AWS (S3, Lambda, EC2), Apache Spark, Kafka, SQL

3. Libraries: Pandas, NumPy, PySpark, Matplotlib

Description and Approach

Objective:

I developed an automated data pipeline to process, analyze, and store large-scale sensor and event log data from autonomous vehicles. The system enabled real-time insights and supported machine learning applications for predictive analytics and system diagnostics.

Approach:

1. Data Ingestion:

   • Collected raw sensor data from autonomous vehicles, including GPS, LiDAR, radar, and camera feeds.

   • Used Apache Kafka to stream data in real-time and integrated multiple vehicle data sources.

2. Data Preprocessing and Transformation:

   • Processed sensor logs using PySpark for distributed computing to handle the large data volume efficiently.

   • Cleaned and synchronized data streams, ensuring time-series alignment across sensors.

   • Derived key features such as speed, proximity to obstacles, and braking events for further analysis.

3. Data Storage:

   • Stored processed data in AWS S3 for scalable and cost-effective storage.

   • Designed partitioning strategies to optimize query performance for historical and real-time analysis.

4. Real-Time Analytics and Reporting:

   • Used SQL to query and analyze stored data for vehicle diagnostics and performance insights.

   • Set up alerting mechanisms for anomalies (e.g., sudden deceleration, sensor failures).

     
     

Visualization:

Built dashboards using Tableau to visualize:

1. Real-time vehicle performance metrics.

2. Anomalies and alerts triggered by the pipeline.

3. Aggregated statistics like average trip efficiency and environmental conditions affecting performance.

Automation:

Automated the pipeline using AWS Lambda to trigger data processing workflows upon ingestion.

Code Flow:

1. Stream sensor data using Kafka and preprocess it using PySpark for distributed data transformation.

2. Clean and synchronize time-series data across all sensors.

3. Store processed data in AWS S3 with appropriate partitioning for efficient querying.

4. Query and visualize results using SQL and Tableau dashboards.

Results

The automated data pipeline achieved the following outcomes:

• Real-Time Insights: Enabled real-time monitoring of vehicle performance metrics and anomalies, improving response times to potential issues.

• Efficient Data Management: Scaled to handle terabytes of data daily with minimal latency using distributed processing.

• Improved Diagnostics: Provided granular insights into vehicle behavior, reducing debugging times for system engineers by 30%.

• Support for ML Applications: Enabled seamless integration with machine learning workflows for predictive analytics and decision-making.

• Cost Savings: Leveraged AWS services for cost-effective and scalable data processing and storage solutions.

This project showcased the integration of big data technologies and automation to support autonomous vehicle systems effectively.

Git Link

For more information and code, visit the Git link.