Five years ago marked a peak for one of the predictable cycles of hyperbole for “self-driving” cars. At the time, virtually every major mo

‘Self-Driving’ Cars Begin to Emerge from a Cloud of Hype

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2021-09-25 15:00:09

Five years ago marked a peak for one of the predictable cycles of hyperbole for “self-driving” cars. At the time, virtually every major motor vehicle manufacturer and high-tech company predicted widespread deployment of automated driving systems (ADS) by 2020, which would purportedly lead to rapid obsolescence of conventional human driving.

With the benefit of hindsight, it has become obvious that the prevailing view during that period was false, with no more than a handful of advanced prototype vehicles having been driven on public roads by last year without the need for onboard safety drivers to intervene when the automation systems needed human help. The term “self-driving” has lost its original intended meaning because the driving assistance feature on the cars that have been labeled “full self-driving” cannot maneuver without constant human supervision, and “cars” are far less relevant for automation today than trucks, buses and shared-ride vans.

By 2018, the CEOs of the major companies that had invested most heavily in ADS (Waymo, General Motors, Ford, Aurora) were starting to make public statements tempering their earlier optimism by pointing out that the rollout of automated driving would be incremental, beginning with operations under constrained conditions in tightly restricted locations. At the pace they are now going, it will require decades to expand to anything approaching nationwide deployment. The organizational learning curve and costs have been much longer and higher than expected. After investing at least a decade and billions of dollars in ADS development, the companies have learned that the technical requirements to support widespread use of the technology are far more complicated than they had originally envisioned. At the same time, companies such as Tesla and less mature start-ups that continue to plug faster and wider-scale deployments are those that are still working their way up a learning curve and have not yet realized how far they are from their goal.

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