Does every slam on the brakes means that driver is being reckless? Is the sudden change of direction a result of a lack of attention? Or maybe both these situations are life-saving maneuvers? Current telematics solutions offered in UBI (Usage-Based Insurance) and fleet management systems can't tell the difference, as such situations are indistinguishable without more data.
We wanted to tackle this and many similar problems in our proprietary telematics solution - SparkT - by augmenting it with an intelligent camera module that recognizes such types of situations. The National Center of Research and Development awarded us with a scientific grant to support the development of the system.
Context is crucial
The ultimate goal of the project was to develop a system to analyze the user’s driving style in terms of safety, fluidity, and economy. The core functionalities of the system are:
- recognize and video-register potentially dangerous maneuvers,
- augment the accelerometer and GPS data with cartographical information,
- score each user’s trip using the collected and processed multi-parameter data,
The project involved building a monitoring device, a "smart camera", and equipping it with a machine learning engine to elevate the solution. It had to be nimble enough to carry all the operations on the edge device but still provide the demanded accuracy level.
Augmenting our SparkT system with ML-based video recognition features was the last step for our application becoming a first-on-the-market fully contextual driving analysis tool.
Edge Video Recognition
We carried out extensive research to define the most common causes of road accidents and ways to address them in our system architecture.
Dangerous road events are mostly caused by:
- Excessive speed
- Non-compliance with the right of way
- Unwary maneuvers within zebra crossing
- Non-compliance with traffic signs
We developed a complex system that combined ML-powered video analysis with information gathered from accelerometer and GPS. Our solution detects all the previously mentioned events by performing multi-sensorial time-series data analysis.
The goal was to record each potentially dangerous maneuver automatically, as they’d be essential factors affecting the driver’s score. From the insurance company's perspective, it’s crucial to calculate the precise risk of the driver’s dangerous behavior, therefore the analysis should be detailed. For the driver, we wanted to provide visual feedback, which is a key feature to understand where he made a mistake and what the situation was. This way positively influencing driving habits is greatly facilitated, as the presented information is contextual and personalized.
The Machine Learning heart
Building such a multi-featured system while preventing the system’s complexity from overloading the edge device processor was possible due to our telematics experience and many experiments. We decided to build our camera with off-the-shelf components such as TPU processors, to lower the individual cost of a single device, making the solution more ubiquitous.
For the ML modules we tested lots of techniques, and finally chose:
- deep learning methods with convolutional and recurrent neural networks for our video assessment,
- random forest and statistical models to enrich data with contextual cartographical data (e.g., defining road type based on near settlements, speed bumps, or city border outlines),
- heuristics, clusterization, and random forest to define algorithms for hazardous event detection and add their outcomes to the final trip evaluation.
According to Allied Market Research studies, the most rapid growth will pertain to highly advanced, multi-parameter systems in the telematics area. We wanted to fulfill this prediction by building a system that will recognize many types of events and behaviors that are not yet addressed in other solutions on the market. This way our proprietary telematics technology gains a competitive advantage with the broadest, most precise driving style assessment.
All the R&D work is done. As soon as the device is ready, we'll implement our solution on the camera, finishing our first ML-powered IoT module, with a mission to improve road safety by educating drivers and giving them contextual-information-oriented feedback.