26. January 2023
Digitization, automation, and connectivity continue to transform the world around us – and the automotive industry is no exception. And for a good reason. The World Health Organization estimates up to 1.35 million people die in road accidents every single year, with human error among the most common causes. Rolling out AI and machine learning represents the significant next step in road safety. But it may not be as straightforward as it sounds. Anonymization may be the element that keeps the automotive industry move forward as it ensures privacy and fosters innovation.
Our new report looks at why data is essential to the pursuit of innovation. Why privacy standards threaten to slow down and even block this progress. And how Deep Natural Anonymization (DNAT) clears the way forward. Here’s a sneak peek if you’re interested to know more…
AI now comes as standard
Most modern vehicles incorporate artificial intelligence in the form of advanced driver-assistance systems (ADAS). These systems rely on AI-powered cameras and sensors to identify other vehicles, potential hazards, pedestrians, and even the facial expressions of driver and passengers. And this is just the beginning because a whole new level of AI is on the way as the concept of self-driving vehicles accelerates toward reality. It is no longer a question of if, but when the roads of the future will be navigated by autonomous vehicles (AV).
The next milestone: autonomous vehicles
McKinsey estimates that up to 15 percent of all new cars sold in 2030 could be fully autonomous. In our new report, we discuss the role of video in the journey to full vehicle autonomy, from QA & Vehicle Validation of ADAS functionalities to Certification to the Recording of Incidents. And we also look at how the collection of personal data risks being hugely impacted by a growing framework of regulatory standards – foremost among which is the famous (and infamous) GDPR.
Progress is powered by data
The development of AVs generates, analyzes, and stores huge amounts of personal data. According to Intel CEO Brian Krzanich, in a discussion at Automobility LA, autonomous vehicles could generate over 300 TB of data every single year in the foreseeable future. And he was only talking about the US market. Yet this comes in an era when privacy matters more than ever – as reflected by strict legislation.
GDPR: a tricky road to navigate
The GDPR was specifically developed to protect personal data, and so any information that can be used to identify an individual falls under its remit. That includes the data collected in the development of AVs, since much of this information makes it possible to identify drivers and their passengers in addition to other vehicles and passers-by. Failure to adhere to these standards can be costly, with the GDPR empowered to levy substantial fines on organizations that fail to meet its standards. In fact, as you’ll find out, total fines for breaches of the GDPR exceeded one billion euros in summer 2021.
And here’s the catch-22 at the heart of our paper
The collection, use, and storage of data require the express written consent of subjects. Yet it is unfeasible to expect OEMs to track down every other driver, pedestrian or cyclist recorded as secondary data subjects in order to gain this consent. So our report discusses this one absolutely vital question: do we face a situation in which data protection laws threaten to put the brakes on innovation, since much of this innovation is powered by video data?
The cost of blocking innovation
According to Bitkom, Germany’s digital association, over 75% of the 502 companies surveyed agreed that innovation projects have failed due to the legal obligations imposed by the GDPR. And 86% have halted projects due to uncertainties in dealing with the regulation. Download our whitepaper to discover the three choices left to OEMs who continue with their innovation projects while remaining compliant with legislation and how anonymization provides the answer – up to a point.
Are traditional anonymization techniques enough?
Yes, conventional anonymization techniques prevent data from being identified and ensure that it can be safely used in compliance with the GDPR and similar regulatory frameworks. Yet as you’ll see, this comes at the price of failing to preserve the accuracy and integrity of the original data. This impact on data quality in turn compromises its compatibility with machine learning and analytics.
Why DNAT clears the road to innovation
And finally, our report will explain how Deep Natural Anonymization (DNAT), eliminates this trade-off. Based on generative AI, this unique technology creates synthetic faces and replica license plates that prevent the original subjects from being recognized.
At the same time, DNAT preserves the quality and integrity of the original data and, in doing so, retains attributes such as age and gender to preserve semantic segmentation. This makes it the only anonymization technique capable of powering analytics and machine learning, and ensuring privacy in automotive industry.
DNAT offers a new direction. By ending the trade-off between privacy and video analytics, it empowers companies to innovate safely and responsibly.
Interested in learning how anonymization can help your business accelerate time to market and drive your ADAS development projects forwards? Let’s have a chat.