Opinion: Web3 brings equality to the self-driving car industry, surpassing Waymo.

Disclaimer: The author of this article expresses their own views and opinions and does not represent the views and opinions of crypto.news’ editorial team.

For many years, self-driving cars have been a popular topic in science fiction movies. However, despite the advances in technology, they still remain largely in the realm of fantasy.

Although the self-driving car industry is making progress and generating excitement with new initiatives, there are concerns about the slow pace of development. Major players like Google’s Waymo, Apple, and General Motors dominate the headlines, but one would expect more disruptive innovators in such a groundbreaking market.

The truth is that some of the key technologies required for self-driving cars heavily favor centralization and large corporations. This is evident upon closer examination.

The key to a self-driving car’s functionality lies in its ability to process data. Simply attaching a camera to a car or connecting it to an onboard computer does not magically enable the vehicle to drive. From the computer’s perspective, the camera feed is just another stream of data. Similar to the human brain, which extracts actionable insights from visual signals, the computer needs its own form of vision.

Computer vision, a subfield of artificial intelligence (AI) or machine learning (ML), allows a self-driving car to “see” and understand the world around it. AI algorithms are used to process data from various sensors, such as LiDAR, to enhance the vehicle’s navigation capabilities. However, training these models requires massive amounts of data.

Companies in the self-driving car industry have struggled to acquire real-world data to train their models, as simulated datasets can only go so far. Real-world data, including factors like weather conditions and regional specifics, is essential for making self-driving cars safe and reliable. That’s why you may see driverless taxis cruising around San Francisco without passengers—they are collecting data.

The challenge of collecting large-scale, high-quality datasets quickly enough to stay competitive is a major obstacle for the self-driving car industry. This obstacle tilts the playing field in favor of large centralized entities, while new entrants face data challenges that hinder their progress. This creates an oligopoly that limits competition and innovation, ultimately impacting everyday consumers.

However, there is a solution: the web3 paradigm. The solution lies in leveraging the thousands of vehicles that traverse city roads every day, collecting vast amounts of data. With the right incentives, drivers themselves can contribute to labeling this data. Similar to CAPTCHA tests that people complete to access websites or services, drivers can label data while driving.

By accumulating this data into comprehensive datasets, both startups and established enterprises can access real-world learning materials for their models. These datasets can be tailored to specific locations, scenarios, and conditions. However, to unlock access to this data, the industry needs a new data paradigm.

This paradigm should utilize blockchain as a shared and neutral infrastructure and transaction layer to prevent the emergence of another closed ecosystem. It should also prioritize self-sovereign data and identities for both drivers and vehicles, giving them control over their data and privacy.

Self-sovereign identities will function as web3 wallets, storing cryptographic proofs of user attributes issued by trusted entities like authorities or car manufacturers. Data consumers can then use these proofs to verify the data that drivers choose to sell. This concept is not far-fetched, as both web2 and web3 companies are already working on blockchain-powered mobility infrastructures.

This self-sovereign data paradigm will transform drivers into active participants in the digital mobility space, allowing them to monetize the data they generate during their daily commutes. It will also address the dataset challenge in the self-driving car industry, providing equal access to a shared market for raw data and giving the industry a much-needed boost.

Despite the promises of fully autonomous self-driving cars, the challenge of collecting training datasets remains a significant barrier. Embracing the web3 data paradigm offers the industry the best chance of accessing an unlimited pool of training data while fostering healthy competition.

This article was co-authored by Sheridan Johns, co-founder of Ocean Protocol, and Leonard Dorlöchter, co-founder of peaq.

Sheridan Johns is the head of the ecosystem for Ocean Protocol at BigchainDB and one of the founding team members of Ocean Protocol. Leonard Dorlöchter is a co-founder of peaq, a blockchain for real-world applications. Both authors have extensive experience in the blockchain industry and are passionate about building disruptive products and ecosystems.

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