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Impact of self-driving cars

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Through Air pollution, Noise pollution and Google

Some experts claim substantial reduction in traffic collisions and the resulting severe injuries or deaths. United States government estimates suggest 94% of traffic collisions have humans as the final critical element in crash, with one study estimating that converting 90% of cars on US roads to AVs would save 25,000 lives per year. Other experts claim that the number of human error collisions is overestimated and that self-driving cars may actually increase collisions.

Self-driving cars are speculated to worsen air pollution, noise pollution, and sedentary lifestyles, to increase productivity and housing affordability, reclaim land used for parking, cause greater energy use, traffic congestion and sprawl. The impact of self-driving cars on absolute levels of individual car use is not yet clear; other forms of self-driving vehicles, such as self-driving buses, may actually decrease car use and congestion.

AVs are anticipated to affect the healthcare, insurance, travel, and logistics fields. Auto insurance costs are expected to decrease, and the burden of cars on the healthcare system to reduced. Self-driving cars are predicted to cause significant job losses in the transportation industry.

Automobile industry

A McKinsey report has forecast that AVs could reach $300 to $400 billion in revenue by 2035. The industry has attracted multiple car manufacturers, most notably General Motor's subsidiary Cruise and Tesla. Ford and Volkswagen invested billions in Argo AI but withdraw from the market by 2022, instead focusing on semi-autonomous driving (L2+, L3 under SAE classification). Notably, non-car manufacturers have also investigated and speculated about self-driving cars, including Google subsidiary Waymo, among others.

To help reduce the possibility of safety issues, some companies have begun to open-source parts of their driverless systems. Udacity for instance is developing an open-source software stack, and some companies are having similar approaches.

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