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The Drive for Safety

How Self-Driving Cars Can Save Lives

Self-driving vehicles (SDVs) have the potential to save nearly 1.3 million lives from car crashes every year – and technology innovations are essential to make self-driving cars a reality.

Centuries of technological advancement are contributing to the creation of a fully self-driving car, and some of the most important core technologies involved are:

  • Sensors
  • Navigation
  • Connectivity
  • Artificial intelligence

Because SDVs rely on more than just traditional vehicle mechanics and a combustion engine, automakers are forming vital partnerships with technology companies — and some technology companies are building their own self-driving cars.

“If you want to build a truly autonomous car, this is a task for more than one player,” said Amnon Shashua, chief executive of Mobileye, a  supplier of mapping and vision-based sensing systems and CTA member company, quoted in a January 2017 Reuters article. “The technological challenges are immense … I would compare it to sending a man to the moon.”

 

We’ll talk more about these technologies and the companies that are driving innovation in these areas on related pages.

As with any emerging technology, practical matters can also present challenges, and we’ll need to address security concerns, modify or create policies and regulations, and earn consumers’ trust before we can achieve the promise of self-driving cars to help us reach zero deaths from traffic accidents.  

Drivers Cause Most Car Crashes

Driver error is the cause of 94 percent of car crashes within the United States, according to the National Motor Vehicle Crash Causation Survey conducted by the National Highway Traffic Safety Administration. The types of mistakes drivers make that cause accidents fall into four general categories:

 

Recognition errors: Drivers can lose focus or be distracted for a number of reasons, including by cellphones, talking to other passengers, eating or even being lost in thought. Inadequate surveillance – not looking or seeing what is “essential to safely complete a vehicle maneuver,” – is the other type of recognition error. Combined, these are the number one source of driver-related crashes.

As General Motors’ Chief Technical Architect Andrew Farah recently said, unless you’re a racecar driver, there’s probably something else you’d rather be doing than driving.

Decision errors: These include driving too fast for conditions, making false assumptions about the actions of other drivers, doing something illegal, or misjudging a gap or the speed of other cars.

Often associated with aggressive driving, these types of accidents account for 34 percent of all driver-caused accidents.

Performance errors: These kinds of driving errors include overcompensating (e.g., oversteering) and poor directional control and make up 10 percent of driver-caused crashes.

Non-performance errors: Tired drivers are the most common cause of accidents in this category. Although this accounts for only 3.2 percent of driver-caused accidents, one study showed more than quarter of people have driven when they had a hard time keeping their eyes open. 

Preventing Crashes by Taking the Driver Out of the Equation

Engineers have already stepped in and created driver assistance technologies to address some common sources of decision and performance errors. Blind-spot and collision warning systems, auto-braking systems, and lane-centering assistance, for example, already come standard are or options in many new cars

According to CTA research, factory-installed automotive technology in the United States, from driver-assist features to entertainment systems, is projected to contribute $15 billion (12 percent increase) in revenue to the industry as a result of overall automotive sales performing well, a rising tide of tech integrated into all vehicles and the increasing density of tech installed in each vehicle.

But driver distraction and impatience, which contribute to a majority of driver-caused crashes, aren’t single-feature fixes. SDVs' evolving artificial intelligence capabilities always have an eye on the road and know the precise location of the surroundings. 

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