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With Lyft selling its self-driving division, the market will be dominated by a few rich companies

todayMay 1, 2021 2

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News broke this week that Woven Planet, a Toyota subsidiary, will acquire Level 5, Lyft’s self-driving unit, for $550 million. The transaction, which is expected to close in Q3 2021, includes $200 million paid upfront and $350 million over a five-year period.

Toyota will gain full control of Lyft’s technology and its team of 300. Lyft will remain in the game as a partner to Toyota’s self-driving efforts, providing its ride-hailing service as a platform to commercialize the technology when it comes to fruition.

The Toyota–Lyft deal is significant because it comes on the back of a year of major shifts in the self-driving car industry. These changes suggest the autonomous vehicle market will be dominated by a few wealthy companies that can withstand huge costs and very late return on investment in a race that will last more than a few years.

The costs of self-driving car technology

Costs remain a huge barrier for all self-driving car projects. The main type of software powering self-driving cars is deep reinforcement learning, which is currently the most challenging and expensive branch of artificial intelligence. Training deep reinforcement learning models requires expensive compute resources. This is the same technology used in AI systems that have mastered complicated games such as Go, StarCraft 2, and Dota 2. Each of those projects cost millions of dollars in hardware resources alone.

However, in contrast to game-playing AI projects, which last between a few months to a few years, self-driving car projects take several years—and maybe more than a decade—before they reach desirable results. Given the complexities and unpredictability of the real world, designing and testing the right deep learning architecture and reward, state, and action space for self-driving cars is very difficult and costly. And unlike games, the reinforcement learning models used in driverless cars need to gather their training experience and data from the real world, which is fraught with extra logistical, technical, and legal costs.

Some companies develop virtual environments to complement the training of their reinforcement learning models. But those environments come with their own development and computing costs and aren’t a full replacement for driving in the real world.

Equally costly is the talent needed to develop, test, and tune the reinforcement learning models used in driverless cars.