15. March 2023
The World Health Organization estimates that road accidents result in the deaths of up to 1.35 million people every year. And with human error among the most common causes, no wonder the auto industry has moved to make advanced driver assistance systems (ADAS) a standard feature on most modern vehicles.
Powered by AI and machine learning, the development of these systems involves the generation, analysis, and storage of huge amounts of video data. Experts claim that a vehicle at the lower end of the autonomous spectrum can produce around 3 Gbit/s of data, which amounts to about 1.4 terabytes every hour. Moreover, at higher levels of autonomy, the total sensor bandwidth will amount to 40 Gbit/s or 19 terabytes per hour.
Yet this opens up Pandora’s box of challenges that can potentially block the road to progress. In this blog, we’ll go through the 7 top challenges faced by the automotive industry in relation to video data – and the ways in which they can impact a business.
Challenge #1: Data privacy
The video data collected by automotive companies contains sensitive information about drivers and passengers. With consumers growing increasingly aware of privacy and security issues, the reputation of auto companies is now on the line. They need to take responsibility for avoiding data breaches and preventing information from falling into the wrong hands. They must also address the widespread lack of trust in their perceived ability to protect these huge volumes of data.
Challenge #2: Data storage and management
The massive amount of video data collected by automotive companies leads us to the second significant challenge, namely the equally massive resources required to store and organize this information securely and effectively. Otherwise, unauthorized access becomes a matter of when, not if. And painfully slow retrieval times risk wasting valuable resources and putting the brakes on progress.
Challenge #3: Data quality
The quality of video data collected can vary wildly depending on various factors, such as lighting conditions, weather, and camera angles. This makes it difficult to maintain the accurate, consistent, and high quality data that AI and machine learning require to perform as effectively as they’re supposed to.
Challenge #4: Data processing
To be of any real use in testing and validation, video data in automotive industry needs to be processed in real-time. Yet processing such vast amounts quickly and accurately is no mean feat, and requires expensive high-performance computing resources and algorithms.
Challenge #5: Integration with existing systems
Needless to say, raw video data alone isn’t much use until it is integrated with advanced driving assistance systems such as sensors, navigation, and controls. Yet ensuring a consistent data exchange between systems represents a whole new layer of complexity, especially because there’s always the potential for discrepancies between systems.
Challenge #6: Regulatory compliance
As we have covered in our guide to GDPR, automotive companies must also comply with strict regulations on the collection, storage, and use of video data. Keeping up with and correctly implementing different regulations in different regions represents a huge challenge. These laws are constantly evolving, and as a result, automotive companies need to regularly update their processes and systems to stay in line – especially when non-compliance can result in a loss of trust, large fines, and even legal action.
Challenge #7: Cost
As you will have already gathered, the collection, storage, and processing of video data represent a significant cost for automotive companies. Technical resources, IT resources, legal resources, hardware, software, and staff. We’re talking about a serious investment. Companies don’t simply need to allocate and manage a significant budget but also balance these costs with the end benefits.
The impact of these challenges on a business
Now that we’ve been through the seven top challenges faced by the automotive industry in collecting large volumes of video data let’s quickly review the real-world, bottom-line impact they can have on the business.
Reduced efficiency: Inadequate storage capacity, slow data retrieval times, and difficulty in processing video data can reduce the efficiency of testing and validation processes.
Decreased accuracy: Low-quality video data, in addition to the difficulties associated with the integration of data with other systems, can diminish the accuracy of testing and validation results.
Increased costs: The high costs associated with collecting, storing, and processing video data will have an impact on the overall budget and gnaw away with profitability.
Legal consequences: Non-compliance with regulations related to the collection, storage, and use of video data can result in significant fines (up to 4% of global revenues), not to mention legal action. These can have a significant impact both on reputation and financial stability.
Eroded trust: Concerns over data privacy and a lack of trust in a company’s ability to protect personal data can severely damage a reputation and shred customer loyalty. In fact, studies have shown that up to 81% of consumers stop engaging with a brand after a data breach.
Delayed product development: The challenges associated with collecting video data can delay—and even put a halt to—the development of advanced and autonomous driving systems, which may in turn impact the competitiveness of the company.
These seven major challenges demonstrate the potential to significantly affect growth and success within the automotive industry. So how do companies ensure they keep innovation in the fast lane?
As the fastest, most accurate anonymization technology available, brighter AI helps many of the world’s most innovative brands to overcome these common challenges. They enjoy significant benefits from day one with simple integration, instant go-to-market times, and zero capex – making it way more cost-effective than an in-house team. And as the only fully compliant software of its kind, brighter AI also guarantees that you stay in line with global data regulations, freeing you from the risk of large fines and reputational damage.
Learn more about how leaders in the automotive industry are using brighter AI’s Deep Natural Anonymization to drive innovation in our whitepaper.
