Insights

How Computer Vision is transforming Automotive Industry and Transportation Industry

Post by
Suraj Venkat
Insights

How Computer Vision is transforming Automotive Industry and Transportation Industry

By
Suraj Venkat
|
March 16, 2021
|
3 Mins Read
How Computer Vision is transforming Automotive Industry and Transportation Industry

Recently, Toyota settled a $1.3 billion lawsuit for a defect in its cars that mistakenly accelerated cars even while drivers tried to slow them down!

This defect resulted in six fatalities in the United States. Automotive manufacturers can identify and analyse such quality problems during manufacturing in an accurate manner, resolving them before they take place using computer vision.  

Each year, we see road traffic accidents accounting for a whooping 2.2 percent of global deaths. This stacks around 1.3 million each year, which translates to 3,287 deaths a day.

Around 20 to 50 million individuals get injured seriously in these accidents every year. Human error is central to these accidents.

A poor or an inattentive decision on the road is the difference between an accident and a typical drive. If we identify human errors or lack of attention in real time, we can significantly decrease incidents. This is another area where computer vision can be effective.

In the automotive industry, we see different technologies tested and applied to different vehicles. Self-Driving and Autonomous cars are yet another emerging technology.

Safety Guidelines, Preventative Measures, and Driver Assistance

Computer Vision solutions for safety draw in sensory information from the external environment surrounding you, analysing it for any incoming threats, relevant situations, and obstacles that you might encounter as you drive. 

Let's consider merging, changing, and unanticipated lane departure, for example. Studies that were conducted by the IIHS or the Insurance Institute for Highway Safety explain that the LDW or lane departure warning system has the potential to dodge and assuage around 37,000 crashes. If you find yourself veering off or swerving off the road as you drive, the LDW is picking up signals and issuing you an alert. This alert is based on visual, auditory, or vibrational inputs. 

Reducing distracted driving is another area in which computer vision is very helpful. The technology can help tackle distracted or drowsy driving. A research department estimated that over three thousand fatalities related to automobiles are a result from driver distractions. Further, 100,000 crashes, 1,500 fatalities, and 40,000 injuries take place round the year because of driver drowsiness. 

With the help of computer vision, you can reduce distracted driving. By measuring important features like monitoring your eye state and eye gaze, CV can reduce distracted driving.

Outside of cars, computer vision can help us cut down accidents for pedestrians as well. We see that over 39,000 pedestrians die each year because of road traffic incidents.

The Future of Transportation and Integrated Computer Vision

When talking about transportation, another area where CV can be used is for streamlining traffic flow. We may use sensory technologies and computer vision for building innovative processes for traffic management.

Problems of Yesterday, Solutions of Today

Safety Concerns 

The most highlighted concerns with self-driving cars are passenger safety, and pedestrian and commuter safety.

Another area of concern with autonomous vehicles is cybersecurity and data safety. Communication taking place in the autonomous parts of the car must be secured and private for avoiding hijacking by hackers. 

Blurry Marking of Lanes 

Most road markings are made clear for  optimum driving experience. However, not all are made with clear marking which may result in higher risk while driving for autonomous cars. Basic solution to this problem requires signs on the road that are computer vision friendly. 

Identifying Minor Obstacles 

Current systems can identify large objects. However, for comfortable and safe driving, they need to avoid the hurdles like potholes. Also, unanticipated obstacles, might cause serious trigger errors and problems in autonomous vehicle systems. 

Artificial Intelligence in the system needs to be able to detect these conditions and then either slow or stop the vehicle. It will be made possible only when one trains the algorithm with enough data and through better innovations on the visual sensors. 

Indeed, while the machine can solve human errors, it may make errors itself. There is research and progress on all fronts to mitigate these challenges associated with AI for self-driving cars.

Final Words

We can safely say that computer vision is becoming a powerful enabler for the whole automotive industry right from manufacturing all the way to increasing comfort, efficiency, and most importantly, safety. There is a need for more research to solve some challenges in automotive and transport industries.

Tektorch.AI helps automotive industry build Computer Vision Solutions for Quality Control, they can also help you build unique solutions for passenger and pedestrian safety. Further, if you are part of a transportation company or say, a government department dealing with transportation, Tektorch.AI can help you too do not hesitate to book a strategy session below

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