Machine Learning-Based Forward Collision Warning System
Assessing the risk of a potential collision, using a camera mounted in the direction of travel.
Challenge
Our client needed an innovative solution that could provide real-time collision warning to drivers. He approached Invatechs with the idea of developing a system that could detect and assess the risk of a collision using machine learning and a camera installed to monitor traffic.
The challenge was to create a user-friendly WAVYN application that would activate as soon as the car starts moving at speeds of 15 km/h and above. The challenge was not only to accurately detect vehicles but also to store the user's driving routes, assess driving quality, and issue immediate audiovisual warnings.
Integrating these technologies and concepts required a comprehensive approach with a focus on usability and reliability. Our team was eager to take on this challenge, driven by the prospect of creating something unique in the field of road safety.
Invatechs had a great opportunity to demonstrate their expertise in machine learning, specifically in implementing TensorFlow. We immediately recognized the potential of this project and began planning the next steps:
Develop a machine learning model using TensorFlow for vehicle detection and recognition.
Integrate the application with OpenStreetMap to store and display user routes.
Implement a QA system to evaluate and reward users for driving performance.
Ensure smooth operation of the application on Android using Kotlin, as well as robust backend support using Firebase and Firestore.
Solution
Invatechs team was tasked with creating an intuitive and intelligent application that enhances road safety through real-time vehicle detection and collision warning. To ensure usability, we developed a design that is easy to understand without special training and focused on implementing features that promote efficient driving and prevent accidents.
We designed the system to activate at appropriate speeds, issuing audiovisual warnings when necessary. With the OpenStreetMap integration, users can view their routes, which adds an extra level of engagement and analysis.
The machine learning integration played a key role in realizing the core functionality of the app. It allows the app to not only detect and assess risks but also to continuously learn and adapt to different driving conditions and scenarios. To achieve our goals, we used various technologies and developed features:
Machine learning using TensorFlow
Using the TensorFlow library, the app detects and recognizes cars using the mobile device's camera. This allows for real-time situational awareness and warns of potential collisions, turning on as soon as a car starts moving at 15 km/h or higher.
Integration with OpenStreetMap
The app stores the history of the user's routes and allows visualizing them on OpenStreetMap. This feature increases user engagement by allowing users to view and analyze their routes.
Forward Collision Warning (FCW)
Designed to track and assess the risk of potential collisions with vehicles traveling ahead using traffic camera technology. Immediate alerting through audiovisual warnings increases driver awareness and safety.
User Experience Design
The app's design caters to drivers with any level of technical proficiency. Intuitive navigation and clear visuals contribute to the app's effectiveness as a traffic safety tool.
Speed sensitivity
The system's sensitivity to vehicle speed allows it to be activated only when necessary, conserving system resources and focusing on relevant driving scenarios.
Technologies & tools
Process
The WAVYN project began with a detailed consultation with the customer to understand their goals and needs for a forward vehicle collision warning (FCW) system. We quickly began work on the MVP, utilizing machine learning to track potential vehicle collisions.
After a two-month development period, we presented a prototype to the customer that included important features such as vehicle detection, hazard notification, and route history. The prototype was approved, and we proposed to integrate additional functionality, such as quality control, to assess the user's driving skills.
Throughout the development process, we followed Agile methodology, maintaining open communication with the customer. Our specialists provided daily reports detailing both completed and upcoming work. Tasks and documentation were managed using tools such as Jira.
After thorough testing and ensuring seamless integration with the necessary mobile and camera technologies, we successfully launched the WAVYN app.
Team
Project duration
Results
To summarize, the Invatechs team has successfully developed a state-of-the-art application that uses machine learning to warn of collisions with vehicles ahead (FCW). By integrating technologies such as TensorFlow and OpenStreetMap, we not only enabled the application to detect and recognize vehicles but also increased user engagement through quality control and route-tracking features.
Our customer plans to make this technology widely available, helping to improve road safety and driving skills worldwide. The collaboration is ongoing, with constant updates and innovations to meet the ever-changing demands of the automotive safety industry.
A comprehensive application utilizing machine learning to improve road safety
Continuous quality assessment that motivates drivers to behave more efficiently
An intuitive system that maps and stores users' routes, offering invaluable insights and a personalized experience
