Self-driving technology could help mobilize individuals who are unable to drive themselves, such as the elderly or disabled. Many thousands of people die in motor vehicle crashes every year in the United States (more than 30,000 in 2015) self-driving vehicles could, hypothetically, reduce that number-software could prove to be less error-prone than humans-but cybersecurity is still a chief concern.Įquity is another major consideration. More information is needed to fully assess how they’ll impact drivers, the economy, equity, and environmental and public health. The costs and benefits of self-driving cars are still largely hypothetical. Most prototypes do not currently have this capability.

Self-driving cars can be further distinguished as being “connected” or not, indicating whether they can communicate with other vehicles and/or infrastructure, such as next generation traffic lights. Partially-autonomous vehicles may require a human driver to intervene if the system encounters uncertainty fully-autonomous vehicles may not even offer a steering wheel. Hard-coded rules, obstacle avoidance algorithms, predictive modeling, and “smart” object discrimination (ie, knowing the difference between a bicycle and a motorcycle) help the software follow traffic rules and navigate obstacles. Software then processes those inputs, plots a path, and sends instructions to the vehicle’s “actuators,” which control acceleration, braking, and steering. Uber’s self-driving prototypes use sixty-four laser beams, along with other sensors, to construct their internal map Google’s prototypes have, at various stages, used lasers, radar, high-powered cameras, and sonar. While design details vary, most self-driving systems create and maintain an internal map of their surroundings, based on a wide array of sensors, like radar. Various self-driving technologies have been developed by Google, Uber, Tesla, Nissan, and other major automakers, researchers, and technology companies.

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