While officials in the U.S. and Europe are pressured by the market to regulate autonomous vehicles, in China it's the contrary. The demand for autonomous mobility-as-a-service is enormous in China, and the market could reach $2.5 trillion by 2030, according to Seeking Alpha.
There are a few factors that contribute to this. China has a very large non-driving population compared to the U.S. and Europe, with high vehicle prices relative to average income. This fuels demand for alternative modes of transportation. In addition, those that do own a car in China are limited by strict environmental regulations as to when and where they can use it. These regulations are a result of the severe air pollution and extreme traffic congestion that plague China's crowded cities. Another major problem is the number of road casualties, which autonomous cars have the potential to significantly reduce.
Will autonomous vehicles aid in solving China's traffic jams, air pollution, and road casualties? (Source: pixabay.com)
Is a top-down approach the best way to get autonomous vehicles on the road?
These circumstances underpin the Chinese government's strong motivation to accelerate the adaptation of self-driving vehicles. The government is setting forth a roadmap to autonomous cars that will dictate the technical standards, thereby creating a unified system. This will enable car manufacturers to develop their own unique vehicles, while ensuring that they all 'speak the same language' and adhere to the same regulations.
This top-down approach is in stark contrast to the situation in the U.S., where each company is developing its own technology, without regard to unification or standardization. Meanwhile, regulators in the U.S. are eager but hesitant. They are simultaneously struggling to catch up, while — at the same time — being cautious not to interfere. In this context, China's top-down approach has some very clear advantages. A powerful and decisive government-backed roadmap could be the edge that enables China to achieve fully autonomous cars before the U.S. or Europe. This would be a significant accomplishment considering that China has been behind the others technologically, at least until recently.
Will Baidu's Apollo platform become the “Android of the autonomous driving industry”?
One of the most important facilitators of the roadmap is the Chinese internet giant, Baidu. This company, often referred to as “China's Google,” has instigated a program called Apollo, which it claims will become the “Android of the autonomous driving industry, but more open and powerful”.
Google's Android devastated the competition in the battle of the smartphone platform. Today, it is the most ubiquitous smartphone operating system, dominating close to 90% of the global market. Will an autonomous vehicle platform be able to reach such levels of success? Already, the program has dozens of partners, from carmakers and tech companies to universities. So, what is Baidu's secret for staying ahead technologically and being able to compete with the leading innovators around the globe?
Can Baidu harness the power of open collaboration to catapult its technology ahead of the crowd? (Source: Li Yang on Unsplash)
Collaboration on open platforms is key to success in autonomous driving
Apparently, it's the opposite of a secret: it's an open platform. Perhaps the key to Android's success was the fact that it was open and benefited from an enormous developer community. Open platforms have also played a pivotal role in the advances of artificial intelligence, like the open source deep learning platforms Caffe, TensorFlow, and others. It makes sense that the same approach will be beneficial for the acceleration of autonomous vehicles. This is exactly the model that Baidu is betting on with Apollo. On the Apollo website, the most prominent statements are about the open nature of the project. For example, Apollo claims to “promote open capability, share resources, accelerate innovation, and sustain mutual benefit.”
It is difficult to predict when and how we will reach the full capability and mobility models that autonomous driving can offer, but it is certain that — on the path — we will develop safer vehicles and gain addition capability of partial automation. As we approach the potential of the huge market for autonomous vehicles in China, we will also see proliferation of active safety across vehicles in the market, all of which will be equipped with intelligent vision sensors. This poses a huge opportunity for chip-makers that can design solutions integrating imaging processing capabilities and intelligent vision powered by deep learning. The key to the success of such solutions will be efficiency and cost-effectiveness.
The CEVA-XM6 has been developed specifically for this market by targeting high-performance vision processing using very low power and optimized for cost-effective production. Accompanied by the CDNN toolkit, this solution offers the fastest time-to-market for deep learning imaging and vision-embedded applications. You can find out more about intelligent embedded vision on the CEVA website.
Jeffrey VanWashenova is the director of Automotive Market Segment, CEVA. Jeff is using his 15 years of experience in the automotive industry to expand CEVA’s imaging & vision product line into a variety of automotive applications. He holds a B.Sc. in Electrical Engineering and a Masters of Business Administration from Michigan State