Computer vision for advanced rider assistance system

An advanced system designed to provide real-time alerts for various road threats, ensuring safer travel for all users.

Industry

Vehicle safety

Type

Computer vision

Time

Ongoing

aras-video-demo-preview

A system for real-time alerts against road threats

For riders

With the help of computer vision, the ARAS is able to provide real-time alerts about potential accidents and road threats.

For fleet management

Computer vision helps organizations using the ARAS monitor their fleet and optimize their logistics operations.

Project Idea

A well-funded startup providing an Advanced Rider Assistance System (ARAS) for motorcycle riders partnered with Yellow to improve their existing solution with computer vision. They aimed to achieve higher model accuracy and optimize resource efficiency.

The client had

Functional solution verified by several major companies like Coca Cola and IBM. Need for computer vision improvement.

What we did

We increased accuracy, reworked the essential models, and enabled a smooth transition to a specialized edge AI environment.

Compliance Strengthening

The AI-powered screening tool aids in compliance efforts by ensuring comprehensive monitoring of money laundering references.

Product Functionality

Proven safety

The solution was tested by thousands of motorcycle riders in both general and extreme weather conditions so the ARAS safety is proven to be effective.

360° detection

AI-based computer vision helps the system analyze real-time footage and detect all possible threats around the vehicle.

Top-tier technology

The ARAS uses computer vision, data analytics, and cloud technologies to provide the best user experience.

Non-intrusive alerts

With the help of timely and nondisturbing alerts, the rider has enough time to react and avoid threats.

Tech Stack

Here’s a list of technologies we worked with on the project.

aras-tech-stack

Development Challenges and Solutions

How our team coped with a range of development challenges.

Enhancing safety monitoring

Challenge: We needed to improve the accuracy of the front collision prevention module’s safety monitoring.

Solution: We used the XGBoost model for the task. The new model is optimized for better threat detection: Compared to the previous version, it now operates with 40 carefully selected features, and the core accuracy metrics increased up to 10%.

aras-enhancing-safety-monitoring

Model optimization

Challenge: The client wanted to upgrade the Detection & Classification module for better efficiency.

Solution: We optimized detection and classification models within the pipeline with the help of transitioning to more advanced architectures and applying targeted optimizations. The latest versions now consume 50% fewer resources while maintaining performance metrics.

aras-model-optimization

Migration to an edge AI solution

Challenge: We needed to help the client migrate their solution from Jetson platform to Hailo AI.

Solution: We assisted in model conversion and adaptation for computer vision tasks. Our team also participated in extensive testing to ensure high performance on the new hardware. The migration aimed at reducing power consumption while maintaining real-time processing capabilities. As a result, it boosted system efficiency enough to enable smooth deployment and high accuracy in resource-constrained environments.

aras-migration-to-an-edge-ai-solution

Results and Future Plans

The model’s accuracy is increased, while resource consumption is optimised.

Experience the system in action during an actual traffic scenario

  • Planned: Experiments with detector and classifier models to continue improving accuracy of detection.

  • Planned: Adapting the software to the new hardware the device has been recently moved to.

  • Planned: Correct device reaction in border cases (for instance, vehicle in the next lane should not trigger an alert). 

This site uses cookies to improve your user experience. If you continue to use our website, you consent to our Cookies Policy