How autonomous vehicles are driven by data
Understanding how to capture, process, activate, and store the staggering amount of data each vehicle is generating is central to realizing the future of autonomous vehicles (AVs).
Autonomous vehicles have long been spoken about as one of the next major transformations for humanity. And AVs are already a reality in delivery, freight services, and shipping, but the day when a car is driving along the leafy suburbs with no one behind the wheel, or level five autonomy as it’s also known, is still far off in the future.
While we are a long way off from having AVs on our roads, IHS Markit reported last year that there will be more than 33 million autonomous vehicles sold globally in 2040. So, the revolution is coming. And it’s time to be prepared.
Putting some data in the tank
As with so many technological advancements today, data is critical to making AVs move intelligently. Automakers, from incumbents to Silicon Valley startups, are running tests and racking up thousands of miles in a race to be the leader in this field. Combining a variety of sensors to recognize their surroundings, each autonomous vehicle uses radar, lidar, sonar and GPS, to name just a few technologies, to navigate the streets and process what is around them to drive safely and efficiently. As a result, every vehicle is generating a staggering amount of data.
According toa report by Accenture, AVs today generate between 4 and 6 terabytes (TBs) of data per day, with some producing as much as 8 to 10 TBs depending on the number of mounted devices on the vehicle. The report says that on the low end, that means the data generated from one test car in one day is roughly the equivalent to that of nearly 6,200 internet users.
While it can seem a little overwhelming, this data contains valuable insights and ultimately holds the key in getting AVs on the road. This data provides insights into how an AV identifies navigation paths, avoids obstacles, and distinguishes between a human crossing the road or a trash can that has fallen over in the wind. In order to take advantage of what this data can teach us though, it must be collected, downloaded, stored, and activated to enhance the decision-making capabilities of each vehicle. By properly storing and managing this data, you are providing the foundation for progress to be made securely and speedily.
Out of the car, into the ecosystem
The biggest challenge facing AV manufacturers right now is testing. Getting miles on the clock and learning faster than competitors to eliminate errors, reach deadlines, and get one step closer to hitting the road. Stepping outside of the car, there is a plethora of other elements to be considered from a data perspective that are critical to enabling AVs.
Not only does data need to be stored and processed in the vehicle, but also elsewhere on the edge and some of it at least, in the data center. Test miles are one thing, but once AVs hit the road for real, they will need to interact in real-time with the streets they are driving on. Hypothetically speaking, you might imagine that one day gas stations will be replaced by mini data centers on the edge, ensuring the AVs can engage with their surroundings and carry out the processing required to drive efficiently.
Making the roads safer
While it might seem that AVs are merely another technology humans want to use to make their lives easier, it’s worth remembering some of the bigger benefits. The U.S. National Highway Traffic Safety Administration has stated that with human error being the major factor in 94% of all fatal accidents, AVs have the potential to significantly reduce highway fatalities by addressing the root cause of crashes.
That’s not to say humans won’t be behind the wheel at all in 20 years, but as artificial intelligence (AI) and deep learning (DL) have done in other sectors, they will augment our driving experience and look to put a serious dent in the number of fatal road accidents every year, which currently stands at nearly 1.3 million.
Companies in the AV field understand the potential that AI and DL technology represents. Waymo, for example, shared one of its datasets in August 2019 with the broader research community to enable innovation. With data containing test miles in a wide variety of environments, from day and night, to sunshine and rain, data like this can play a pivotal role in preparing cars for all conditions and maintaining safety as the No. 1 priority.
Laying the road ahead
Any company manufacturing AVs or playing a significant role in the ecosystem, from edge to core, needs to understand the data requirements and implement a solid data strategy. By getting the right infrastructure in place ahead of time, AVs truly can become a reality and bring with them all the anticipated benefits, from efficiency of travel to the safety of pedestrians.
Most of the hardware needed is already there: radars, cameras, lidar, chips and, of course, storage. But understanding how to capture, process, activate, and store the data created is central to realizing the future of AVs. Data is the gas in the proverbial tank, and by managing this abundant resource properly, you might just see that fully automated car in your neighborhood sooner than expected.
Author: Jeff Fochtman