Insights

An advanced fleet management system for trucking companies

Managing a modern trucking fleet isn’t just about logistics—it’s about real-time decisions, driver safety, and quick adaptation. That’s why Avenga partnered with Intel to develop a next-gen proof of concept for a non-ADAS fleet management system. Designed for real-world use, the system supports smarter operations and helps drivers stay focused and protected on the road.

April 30, 2025 5 min read

  • Client Intel
  • Industry Automotive
  • Service Solution engineering
  • Technologies Cloud

Introduction

As a reputable software development vendor with significant expertise in the automotive industry, Avenga has consistently strived to push the boundaries in the areas of transportation and fleet management. 

  • 4

    Detection modules built to track fatigue, distraction, gaze direction, and gestures in real time.

  • 6+

    Technologies used like Intel’s OpenVino and AWS ELK stack to power the platform

With this objective in mind, we conduct thorough research and development activities in the automotive field, focusing on designing and engineering cutting-edge solutions that enhance driving safety and provide trucking companies with a competitive advantage. We have also collaborated with many industry-leading software companies, including Intel, on numerous automotive projects in the automotive and other industries. 

Notably, one of our fleet management initiatives has generated interest from local municipalities in Cordoba, Argentina, where one of our delivery centers is based. The work we have undertaken demonstrates considerable potential in terms of elevating driver monitoring, improving driver safety, and increasing fleet management efficiency. 

Challenge 

With cities worldwide undergoing rapid development, trucking companies are under mounting pressure to enhance fleet management, including tracking and coordinating driver and fleet behaviors. 

To address modern transportation needs, they need sophisticated fleet management tools, which could help streamline processes, prevent dangerous situations and, ultimately, increase their competitive advantage. In light of this, Avenga has decided to engineer an ML-based, non-ADAS tool tailored to these industry-specific requirements.  

Solution

Engineered a proof of concept (PoC) for a sophisticated system that proactively notifies the driver of any potential emergencies or dangerous situations and tracks their behavior. The system’s innovative architecture is designed to leverage the combined capabilities of cloud computing and edge processing, providing a robust and scalable platform for managing and analyzing large volumes of data in real-time.
Through the utilization of Intel’s OpenVino advanced toolkit, we were able to implement highly precise detection capabilities within the platform. The solution’s design incorporates both facial recognition and gesture detection modules, which enable real-time identification of varying levels of driver activity and rapid detection of potential signs of drowsiness or distraction.
Overall, we developed an advanced system for detecting driver fatigue that monitors driver behavior and assesses the risk of fatigue-related incidents. The system leverages facial landmark detection to analyze the driver’s eyes and mouth, identifying signs of fatigue such as prolonged blinks and yawns. Additionally, we added a gaze attention monitoring mechanism that determines whether the driver’s face is centered or not centered and also helps reduce the risk of accidents.
If the system detects that certain behaviors have exceeded their defined limits, it provides the user with two options. The first option is a VU meter that records the number and persistence of these behaviors within specific time frames. The second option is a tool that records the events and allows trucking organizations to store them in the cloud for future driver coaching.
We utilized Amazon Web Services to ensure the required computations could be conducted at the edge, inside the vehicle, thereby eliminating the need for cloud computing and large servers. Furthermore, we skillfully implemented the combination of AWS Elasticsearch, Logstash, and Kibana (ELK) to facilitate rapid data processing and enable the creation of dynamic dashboards, which enable efficient driver tracking, behavior assessment, and support.
Additionally, we engineered a near misses detection system that relies on Al to accurately detect vehicles and pedestrians at intersections in cities. The tool is designed to work with smart cameras and can be used for indoor and outdoor monitoring. It can integrate with external cameras and merge data from multiple sources, which enables it to track objects within predefined areas of interest with high precision. It also can process data at the edge and stream it to the cloud for structuring and analysis.

Process

Discover

An in-depth review pinpointed pressing challenges in the trucking industry. Safety and efficiency gaps were clearly defined. 

Research findings laid the blueprint for innovation. Critical insights drove the vision for a tailored solution. 

Define

Collaboration with Intel sparked a detailed mapping of requirements. Key features for driver monitoring and fleet operations emerged. 

Design criteria focused on simplicity and robustness. The plan aligned technical feasibility with industry needs. 

Develop

Leveraging Intel’s OpenVino and cloud-edge capabilities fueled groundbreaking development. A state-of-the-art system was built for real-time insights. 

Predictive safety features were integrated to proactively mitigate risks. Seamless performance and scalability became core attributes. 

Deliver

The proof of concept (PoC) was finalized and rigorously optimized for real-world use. Fatigue detection and near-miss tracking were finely tuned. 

The well-structured, scalable system elevates driver safety and streamlines operations. A solid foundation for future applications was successfully established. 

Results

  • We delivered a proof-of-concept (PoC) for an end-to-end fleet management solution that can optimize trucking companies’ processes, improve driver safety, and enhance their competitive edge. 
  • This PoC has already gained the attention of the municipal authorities in Cordoba, Argentina, where our delivery center is located, and demonstrated significant potential for real-world applications. 
  • We also engineered a powerful near misses detection system that employs machine learning algorithms to identify potentially hazardous situations at city intersections. 
  • Finally, we achieved the integration of driver management and the near detection systems into Intel’s IoT DevCloud platform.
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Explore what’s possible when safety and smart tech go hand in hand. 

Let’s talk

The PoC we have developed exhibits substantial potential for a wide range of real-world use cases. Specifically, it can assist truck drivers and help trucking companies address the changing transportation demands. 

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