Many fleet safety technology companies are claiming to use artificial intelligence (AI), deep learning, computer vision, and machine learning in their solutions today. In fact, so much so that these terms have become confusing to many. To help cut through the noise, we’ve outlined some essentials to better understand the differences in AI models available in order to choose the best AI-powered risk reduction platform for your business.
AI Dash Cams Are Not Created Equal
There are many ways to implement AI—for example, on the network edge (in this case, in the vehicle), in the cloud, or end-to-end from the edge to the cloud. So let’s break down these different AI models and discuss their capabilities and ultimately how each can assist your fleet safety initiatives.
- On the network edge (in-vehicle) AI processing: AI can be trusted to understand driver behavior, traffic elements, vehicle movement, and critical contextual data across driving ecosystems—but it only works if it can be successfully deployed in the vehicle to help drivers when it matters the most. By running AI-powered algorithms on a device, it enables fleets to understand driver behavior and automatically detect distracted driving and other high-risk behaviors in real-time. Because these AI-powered algorithms run in real-time, you are immediately able to coach drivers with progressive alerts to help them return their focus to the road.
- In the cloud AI processing: Most video telematics and dash cam solutions today require driver video to be uploaded to the cloud for analysis (i.e. for human review) before any distraction determination is made. There are significant shortcomings to this approach because of the time lag, or latency, resulting from data transmission from the vehicle to the cloud and back again which delays real-time alerting. This means the driver doesn’t get a chance to act in time to prevent the incident, and worse, the supervisor knows something has happened even before the driver does (since many systems on the market don’t even let the driver know data was captured).
- Edge-to-cloud implementation: While edge AI processing is purpose-built for real-time collision avoidance systems, Cloud-native software enables rapid iteration for model improvement and offers advantages of high availability, scalability, and reliability. With Nauto, millions of data points from over a billion AI-analyzed video miles are securely stored, meticulously processed, and optimized for driver improvement. From within the cloud, Nauto tests and refines all new and existing convolutional neural network (CNN) model inputs and outputs. This allows us to reach acceptable accuracy levels before deploying over-the-air to create an impact on driver behavior on the edge, in real-time.
What does this mean?
To make the best use of hardware resources on the market today, AI-based driver safety solutions should be implemented across both the edge and the cloud, taking advantage of what each delivers (and does best). This means if your AI Driver and Fleet Safety Platform does not have multi-sensor data fusion, or a multi-tasked convolutional neural network foundation—nor is it optimized for an edge-to-cloud implementation—then it cannot help you predict, prevent, and reduce the occurrence of high-risk events in highly complex driving environments before they happen.
For a deeper dive into the difference in AI models and AI-powered dash cams, check out our latest webinar: AI-Powered Dash Cams Are NOT All Equal