Autonomous cars would generate, collect and analyze reams of data for a host of purposes, ranging from basic navigation to other complex functionalities like traffic management, speeding enforcement and knowhow about driving circumstances. Data as the ‘new oil’ analogy fits perfectly with autonomous vehicles. It is estimated that in the foreseeable future, autonomous vehicles, in the US alone, would generate over 300 TB data each year.
With the overall requirement of a plethora of data, there arises an inescapable question: who will have access to this massive data?
Reading between the lines
When we talk about data in the context of autonomous vehicles, it is noteworthy that the data should be classified into two categories: Data collected and processed by the vehicle using LiDAR sensors, HD maps, neural networks etc and the data generated by the car.
As to who will have access to data is highly speculative, as of now, but it will soon become a big issue. Whether carmakers will access that data individually or some consortium will hold it for further tech advances and research purposes, remains to be seen. However, one thing is certain: Autonomous car industry would have to figure out a way to answer the data conundrum.
Coming to the topic of similarities in data control patterns between autonomous car companies and big tech players (most of whom sell user data to third party aggregators), it must be understood that autonomous vehicle ecosystem is still nascent and a lot of work is in the pipeline. There are many concerns pertaining to technology, ownership, behavioral patterns and other miscellaneous issues that have yet not been addressed and the overall framework is amorphous. For instance, regulations pertaining to autonomous vehicles would be another issue. With the current scenario, the gap between the accelerated technological growth in autonomous cars and vehicle regulations that are unusually slow in adaption would give sleepless nights to regulators.
What we are talking about would be momentous – arguably the biggest revolution in the automobile industry since the mass production of Ford Model T.
So naturally, a lot of concerns would arise and gradually they would be allayed.
Also Read: How safe are autonomous cars?
Need for a common enabling framework
Collaboration and standardization would drive the industry forward. Just a month back, Lyft released the world’s largest freely available datasets to boost autonomous vehicle research. Prior to this, Waymo released its high-quality multimodal sensor data set for autonomous driving at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
Both Alphabet and Lyft, who are among the pioneering autonomous vehicle players, making their datasets freely available, is indicative of the larger trend that I said would foster growth in the industry: collaborative approach and standardization of frameworks. Most of the big players state the democratization of autonomous technology and creating a conducive framework as among their long-term goals.
There is another big debate that is raging in the autonomous industry after Elon Musk dropped a bombshell on LiDARs – avowing to prefer neural networks and Deep Learning algorithms instead. It can’t be a mere coincidence that just months after Musk’s outburst, Apple announced the acquisition of Drive.AI( one of the few startups that handful of few companies that use Deep Learning to view obstructions on the road, and not LiDAR’s.)
Whether LiDAR would be the way ahead, or will Neural networks supersede them remains to be seen. There is a lot of churning going on in the segment of autonomous vehicles, and there is still a long way to go in formalization, standardization and technological mainstreaming.
Lingering privacy question
I don’t think that autonomous vehicles and the way they will handle data have a major influence in the ongoing discourse on user privacy. The debate on data privacy occupied the center stage post-Cambridge Analytica and then the implementation of GDPR. These were tech companies, social media networks, and service providers etc who had made selling data not only profitable, but kind off a revenue stream. It has no parallels with autonomous vehicles, in my reckoning. The business model of autonomous vehicles would be diverse and the opinion is still contested whether it would lead to a decline in the traditional car ownership patterns or boost it.
Another factor that plays a key role when we talk about data privacy apropos autonomous vehicles is public perception and lingering suspicion and distrust. A significant majority of people are still apprehensive about autonomous vehicles and would think twice before sitting in a fully autonomous vehicle. This is an uphill task. The companies and other stakeholders are working actively to not only put these anxieties to rest but also disseminate the advantages of autonomous vehicles.
In this case, any negligent handling of data or even accusation of data breach has the capacity to tarnish their reputation beyond repair and lead to the plunging of already low consumer acceptance. No autonomous vehicle company would like to be ensnared in this double whammy!