A decade ago, manufacturers began fitting their vehicles with hi-tech gadgets and electronic sensors to help bring better driving as well as engagement experiences to the mix. In the initial days, the focus from manufacturers was to deliver entertainment and navigation options through infotainment screens and cockpit displays. But now, the automotive sector is racing ahead with the next big set of innovations like autonomous driving vehicles and smarter production facilities. 

These innovations rely on a solid underlying foundation of smart automation technology that spans several layers of operations both within the vehicle and at the production factories. Guiding this ambitious quest for automation today is the power of computer vision. In this context, computer vision refers to the use of machines to process visual data and derive decision-making insights from them. 

Computer vision is at the helm of modern automation initiatives of automotive manufacturers as they attempt to make vehicles that can sense and understand the road ahead and carry people risk-free to any destination. On the operational front, manufacturers are also attempting to reduce inefficiencies and accelerate production to meet demands without compromising on quality.

Let us explore the top 4 ways in which computer vision can drive transformational change in the automotive world today:

Autonomous Driving Vehicles

The biggest impact that computer vision can make in the automotive sector is undoubtedly enabling the rise of safe and functional driverless passenger vehicles. According to a recent McKinsey analysis, the autonomous driving space can create a $300-400 billion revenue opportunity by 2035.

From tech companies to popular car makers, billions of dollars are invested every year into improving driverless technology for passenger vehicles globally. While electronic gadgetry and software controls form a key part of the self-driving experience, the primary driving force behind autonomous vehicles is computer vision. 

Acting as the eye of the vehicle, computer vision empowers vehicles to see and process visual data from cameras mounted on all sides of the vehicle. This enables onboard intelligent AI navigation systems to correctly measure distances and identify and add contextual sense to all objects surrounding the vehicle when it is in motion and when it halts. 

As a result, the vehicle is empowered to move in any direction without the need for a driver, as computer vision tracks every inch of the road in all directions to safely and autonomously steer the vehicle.

Driver Safety Systems

Computer vision benefits from the fact that it not only processes insights from visual data coming from outside the vehicle but also from within. The cameras that track the behavioral characteristics of the driver can be used as an autonomous driver safety system. 

Computer vision enables the car to recognize dangerous action events from the driver, such as sleeping off, lack of concentration, or even distractions like checking mobile phones while driving, the feeling of exhaustion, etc. It can then help warn the occupants of the vehicle to take action. This forms an additional layer of safety mechanisms for modern vehicles and does a pretty good job of averting risky accidents owing to careless driving habits.

Assisted Vehicle Control

We have already seen how computer vision can identify threats and steer vehicles autonomously away from them. The same principles can be used to boost the safety credentials of the vehicle even when driven by a human. 

For example, computer vision-guided safety systems can detect sudden obstacles or collision scenarios in real-time from cameras fitted on the vehicles. The system can be programmed to intelligently apply brakes automatically or slow down the vehicle to prevent a major collision from happening. 

Similarly, computer vision can help build a better cruise control system for vehicles wherein drivers need to only make a minimal effort while cruise control is turned on. Going back to the aforementioned McKinsey study, Advanced Driver-Assistance Systems (ADAS) are predicted to reduce the number of accidents by 15% in Europe. Of course, the level of influence could vary across geographies, but this does lend a good insight into how these systems can alleviate safety issues.

Smarter Automated Factories

The automotive industry needs to work at an accelerated pace to meet demands from markets across the world. While robotic assembly lines and units have been in place for decades, the real game-changer for accelerated vehicle manufacturing is the ability to run autonomous production lines 24 hours a day all through the year without fail. This is where computer vision can play a great role. 

From enabling strict quality control through real-time visual inspection to ensuring faster movement of components down the assembly line without errors, the possibilities of computer vision in automated manufacturing plants are limitless.

The Road Ahead

As consumers increasingly adapt to a world where AI and machine learning is transforming their interactions at all places, the transition of automobiles into autonomous vehicles will be a landmark achievement. Computer vision is vital in enabling and empowering this transition successfully and seamlessly. 

However, the onus is on manufacturers to choose the right technology with the right guidance to commence the change. This is where KamerAI can be a game changer for automotive companies. Get in touch with us to explore the possibilities of computer vision-driven transformations in the automotive sector.