CEO of Everguard, whose entrepreneurial skills have helped him develop teams, products, and new markets across technologies and industries.
While the concept of self-driving automobiles only gained popularity in the last few decades, the vision of a fully autonomous vehicle was first introduced at the World’s Fair in 1939 by industrialist Norman Bel Geddes. Geddes had a dream of “devices which will correct the faults of human beings as drivers,” according to his book Magic Motorways.
Unfortunately, Geddes was on to something. The tragic reality is that today, 94% of serious crashes are due to human error. That is why automakers and tech heavyweights have partnered to bring Geddes’ vision to life. If most serious crashes are due to human error, it only makes sense to eliminate the human component of driving by deploying fully autonomous vehicles. But how?
Applying Sensor Fusion And AI Technology To Maximize Safety
Sensor fusion is the process of combining data from multiple components to generate an output or action that is more accurate and reliable. Sensor fusion, paired with artificial intelligence (AI), gives autonomous vehicles the capabilities for static object detection, moving object detection and tracking, occupancy grid mapping, navigation, and more.
In autonomous vehicles, multiple sensors like cameras, radar, lidar and GPS are continually scanning, collecting and sending data to the main system to be analyzed. As the car moves, data is continuously updated with new inputs from the sensors as they feed the algorithms, or “brain,” of the system. Decisions are made almost instantaneously based on the data the system receives.
For example, a camera placed at the front of the vehicle views a yellow light, but it cannot capture the depth of the object. But when paired with a sensor that has radar capabilities, additional details are delivered on the approximate distance of the light ahead. The system can then make an intelligent decision to continue at the current speed or brake based on the data it receives. These multiple input devices allow AI to take various pieces of data into account and use them to make more reliable and precise decisions.
As the former president of Netradyne, I saw firsthand the power of sensor fusion when used with vehicles, specifically commercial fleets. Now, as the CEO of Everguard, I’ve witnessed how sensor fusion-based AI can transform safety in the workplace.
Driving Changes In The Industrial Workplace
Imagine applying that same ability to sense, process and respond proactively to data from sensor fusion technology via AI in another unpredictable environment: the industrial workplace. Like a car on the highway, workers on a construction site, in a warehouse or on the factory floor face ever-changing conditions. And like driving a car, most workplace injuries and fatalities are the result of human error. Just as sensor fusion-fed AI in autonomous vehicles can help keep drivers safe, for the first time, the same technology allows industrial environments to provide proactive methods for preventing accidents.
Think of a shipping bay equipped with racks, cranes and forklifts. Add to that a mix of hardware, software, wearable devices and other sensors. Workers move through the facility as machinery and mobile equipment hum along. A sensor attached to a forklift continuously sends data to an AI system to tell it when and where the forklift is moving. A camera views a worker — outfitted with a wearable — moving toward the path of the forklift. Both data inputs are simultaneously sent to the system and instantly analyzed. In under one second, an alert is sent to the worker before they reach the danger area in the path of the forklift. The forklift driver might also receive an alert to slow movement to avoid an injury — or worse.
Imagine, in this same facility, an overhead crane with sensors surrounding it at four points to create a geofence. The geofence is essentially an invisible barrier that prevents injuries by keeping employees a safe distance from loads carried overhead. A camera in the area views a worker headed in the direction of the geofence. At the same time, a sensor with location capabilities detects the distance narrowing between the worker and the geofence. This data is processed by AI algorithms, which make the intelligent decision that a risk is present, should the worker continue moving toward the crane. The system instantly sends an alert via a wearable to warn the worker of the danger ahead.
Advancements in the autonomous vehicle industry to overcome the faults of human error are changing how we think about safety not only on the road, but also in the other sectors. With the help of sensor fusion-powered AI technology, industrial workplaces can now be proactive in preventing accidents, instead of implementing reactive methods after the fact.
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