Machine Vision (MV) is a suite of sensor technologies that work together to recognize objects and behavior using visual data. Artificial intelligence (AI) enables computers to recognize patterns and make informed predictions about what might happen next. Together, these technologies can detect environments, discover which patterns correlate with risk, and determine potential outcomes.
MV+AI can recognize when a driver is distracted – by a mobile phone, for example – and prompt the driver with an alert before something potentially goes wrong. This is made possible by a comprehensive “training” process: hundreds of thousands of images feed the technology to teach the MV algorithm what distracted driving, and other behaviors look like. This training data is combined with additional information from GPS and a multitude of other sensors to teach the AI how to reliably detect more complex location-based behavior.
Still, the algorithms can’t learn in a vacuum: human judgement plays a vital role in helping the system learn to spot driving conditions and driver behavior. Video is reviewed and the images are tagged to train the AI in knowing which data to consider more relevant. As the MV+AI system continues to learn, it identifies the correlation between behavior and risk more precisely, helping fleet managers identify which behaviors, locations, and drivers pose the greatest potential danger. In this way, MV+AI provide visibility into issues that were possible to see just a few years ago.
MV+AI: An Extra Pair of Eyes
The combination of machine (MV) and artificial intelligence (AI) brings the promise of detecting and anticipating risk, helping drivers avoid collisions and making the world safer. This technology serves as a tireless pair of extra eyes that can help alert drivers to hazards – including their own bad habits – and drive more safely.
But not all MV+AI systems are created equally. Without large amounts of high quality data, even the best algorithms can’t know what to look for. As advanced as machine learning has become, it still requires both billions of data points and expert humans to help train the technology to detect risk.
What Makes Lytx So Precise?
The MV+AI model is only as smart as the data used to train it. That’s why it is important for humans to help: the system can’t become an expert without the help of people who know which data is significant and what can be safely ignored.
Today, Lytx MV+AI technology focuses specifically on the challenges of detecting risk in commercial fleets. Our technology draws from a cache of images collected over 100 billion miles of driving, tagging them for potentially hazardous behaviors and conditions. This combination of human analysis with traditional telematics data provides more insight than machine analysis of raw data alone.
Expanded View Of Risk?
Lytx uses innovative technology to reliably uncover true risk so you can focus on what matters without the distraction of irrelevant events or information. Our focus on training algorithms with the best data provides the most precise results possible, avoiding the false positives that come from subpar or un-curated data.
Our technology is developed with one purpose in mind: delivering a view of fleet risk you can trust. The combination of high data volume and accuracy means that our MV+AI algorithms have better raw materials to work with, helping to deliver more precise results so that you aren’t wading through an ocean of irrelevant information.
Source: https://www.lytx.com
CUT COTS OF THE FLEET WITH OUR AUDIT PROGRAM
The audit is a key tool to know the overall status and provide the analysis, the assessment, the advice, the suggestions and the actions to take in order to cut costs and increase the efficiency and efficacy of the fleet. We propose the following fleet management audit.
