Why do we call it inevitable? Sooner rather than later, we will have technology-powered vehicles moving around the streets (some of them self-driven). Electric mobility, driverless cars, automated factories, and ridesharing—these are just a few of the major disruptions the auto industry faced even before the COVID-19 crisis. Technology especially Artificial Intelligence will be a major disruptor in the automotive industry with a Mckinsey report predicting that we could see fully autonomous cars accounting for up to 15 percent of passenger vehicles sold worldwide in 2030.
With technology at the forefront of future automobiles, our latest weekly blog shifts focus towards the intersection of mobility & advanced technology.
With advances in artificial intelligence and autonomous technology, automakers are rapidly turning to driverless vehicles. The development of e-scooters is a decisive stride towards long-term sustainability. Electric scooters define as the future of urban mobility. They are eco-friendly, noise-free, and pollution-free. As these scooters are already starting to gain traction, many e-scooter makers are now building semi-autonomous e-scooters that can drive autonomously with the least human intervention.
We expect the 2-wheelers to make quite a splash!
An artificially intelligent machine (such as an autonomous vehicle) is capable of taking what it has learned and figure out how to cope with a similar but not identical scenario in much the same way as a human driver. The more diverse the information an AI system is fed, the more capable it will be of adapting to slightly different scenarios.
As mentioned by the writer, Jesse Crosse, it is probably fair to say that the concept of self-driving cars is one of the most controversial subjects in the automotive industry.
Automated or self-driving vehicles are about to change the way we travel and connect with one another -> U.S. Secretary of Transportation Elaine Chao
Let us consider collision avoidance systems in self-driving cars. If visual input to on-board cameras could be trusted entirely, an AI system could directly map that input to an appropriate action — steer right, steer left, or continue straight — to avoid hitting a pedestrian that its cameras see in the road. But what if there’s a glitch in the cameras that slightly shifts an image by a few pixels? If the car blindly trusted so-called “adversarial inputs,” it might take unnecessary and potentially dangerous action.
A new deep-learning algorithm developed by MIT researchers is designed to help machines navigate in the real, imperfect world, by building a healthy “skepticism” of the measurements and inputs they receive.
This algorithm has the potential to help autonomous vehicles navigate in the real world
Suppose one could develop an AI application without having to lift a finger.
To some degree that is the goal of Automated Machine Learning, known as AutoML, which consists of an automated means to build on a Machine Learning application, requiring minimal by-hand effort on someone's part.
The use of Machine Learning is a crucial element to the advent of self-driving cars. Partially due to the maturity of using ML already, there is not yet much rapt attention going toward using AutoML for self-driving cars
In the end....
Digitization, increasing automation, and new business models have revolutionized other industries, and automotive will be no exception. These forces are giving rise to four disruptive technology-driven trends in the automotive sector: diverse mobility, autonomous driving, electrification, and connectivity. This decade will see a revolution in the automotive industry and we can’t wait to see what happens!
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