Driving can be an exhilarating experience. Imagine cruising along a coastline in a convertible or driving a sports car at high speeds on the Autobahn. But that’s not an everyday experience for most drivers. Long, boring commutes on the same roads fill our daily drivetime. Self-driving cars may soon change all that.
How Machine Vision Makes Self-Driving Possible
We’re not sure who first had the idea for cars to drive themselves. But we’d like to think it was someone sitting in bumper-to-bumper traffic on their daily commute. All they wanted was some relief from the monotony. Gas. Brake. Gas. Brake. To create a vehicle that could drive the streets on its own, it would need to be able to control its own functions. But it would also need to be able to “see” its environment. Enter machine vision.
Autonomous vehicles use sensory input devices like cameras, radar, and lasers to allow the car to perceive its environment and create a digital 3D map. Besides other vehicles, there are pedestrians, traffic lights, barricades, and more. Some objects move. Some don’t. Some do both. To avoid collisions while driving, the vehicle needs to identify various objects and anticipate their behavior.
Features of Machine Vision for Self-Driving
Autonomous vehicles use sensors and cameras to collect data and create 3D maps in real-time. They can then figure out if the driving space is safe and choose alternate routes if there is a projected collision. Machine vision uses bounding box detection and complex algorithms to monitor the vehicle’s path and periphery.
Machine vision and deep learning technology use segmentation techniques to detect lane lines to stay in the correct lane. Detection of curves and turns makes for a safer, more comfortable ride for passengers. Lane changes can be a challenge for self-driving cars, both when they need to change lanes or when detecting lane changes of other drivers. Turn signal use is inconsistent as well as the speed and clearance offered by some drivers.
Light conditions often differ by route, terrain, and time of the day. Self-driving vehicles must switch between normal and low-light modes. Low-light conditions often result in blurry, dark images which can make driving difficult and unsafe. But machine vision can identify low-light conditions and make adjustments or have the vehicle source data from LiDAR, radars, or thermal cameras.
Machine vision paired with AI-based algorithms is the “eye” of the self-driving vehicle. Modern machine vision can ensure the safety of passengers and deliver a smooth self-driving experience. It hasn’t been perfected yet, but its current pace, safe and reliable self-driving cars will soon be a common sight on your local roads.
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