Enhancing safety on the roads has long been a primary objective for our clients. Collaborating with Agiliway, they embarked on creating an AI-driven application designed to revolutionize the driving experience.
This innovative tool identifies various objects around a vehicle in real-time and processes them using machine-learning models, thereby improving safety and reducing the likelihood of accidents.
In this piece, we delve into the strategies behind crafting intelligent driving safety applications aimed at minimizing road-related risks.
Key Challenges in Designing Advanced AI SolutionsThroughout the development of this application, the Agiliway team encountered several pivotal challenges:
Data Collection: Building a robust ML model requires substantial volumes of annotated data. Typically, tens of thousands of images are essential for reliable training.Object Detection Models: Developing multiple ML models to recognize different object categories demands significant data preparation and annotation efforts.Image Labeling: Leveraging tools like Label Studio for annotating datasets comes with challenges, particularly in ensuring consistency and precision.Privacy Compliance: Implementing mechanisms like automated face blurring to adhere to privacy standards adds complexity to data workflows.Optimal Training Parameters: Determining the ideal number of epochs to balance model performance and avoid overfitting requires meticulous experimentation.Iterative Model Training: Continuously refining models to improve accuracy and results is essential.Performance Benchmarking: Establishing systems for visualizing and evaluating training progress.Accelerating Training: Reducing the 3–5 day model training period by selecting optimal servers that balance cost and efficiency for the client.Tailored SolutionThe primary objective was to enhance driver and passenger safety through computer vision and machine learning. This is especially valuable for families with less experienced drivers. The application includes navigation, trip monitoring, driving analytics, and alerts. Drivers are notified of potential hazards—such as accidents, emergency vehicles, or reckless behavior—while administrators are informed of unsafe driving patterns.
To overcome challenges, the following approaches were implemented:
Streamlined Data Collection: A systematic approach was adopted, combining publicly available datasets and user-generated inputs to compile a sufficient volume of training data.Seamless Data Integration: The system automates map updates using ML algorithms like CatBoost, eliminating the need for manual interventions.Incremental Model Training: Iterative improvements allowed for consistent performance refinements.Enhanced Annotation Processes: Tools like Label Studio and Roboflow were utilized effectively, supported by detailed guidelines to maintain consistent data quality.Privacy Safeguards: Automated face-blurring techniques ensured compliance with privacy regulations.Optimized Epochs: Validation datasets were employed to fine-tune training epochs, balancing accuracy and efficiency.Visualization Tools: Platforms like ML Flow were introduced to track metrics and showcase progress to stakeholders.Benefits of AI-Driven Safety SolutionsThe proposed application delivers notable advantages:
Accident Prevention: Through real-time object detection and hazard alerts, it actively promotes safer driving practices.Parental Monitoring: Features like trip tracking empower parents to oversee young drivers and intervene when necessary.Behavior Insights: Driving analytics fosters constructive feedback and discussions, encouraging better habits.This solution merges cutting-edge AI technologies with practical features tailored to modern needs, all aimed at cultivating safer driving environments for inexperienced drivers.
