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driver assistance

Contributor(s): Matthew Haughn

Driver assistance, also known as advanced driver-assistance systems (ADAS), are technologies used to make motor vehicle travel safer by automating, improving or adapting some or all of the tasks involved in operating a vehicle.

Driver assistance serves to make travel comfortable and easier, while also increasing car and road safety. While some systems help with the task of driving, others alert the driver to errors or hazards, such as lane departure detection and drowsiness detection. Aside from vehicle control, driver assistance can also refer to secondary driving tasks such as location finding, route planning and obstacle detection.

Driver assistance involves both simple systems such as anti-lock braking and complex systems like the fully autonomous hardware and software operating a driverless car. More complex systems that take control of the car include technologies such as machine vision, artificial intelligence (AI), radar, lasers and various position-detecting technologies.

Examples of driver assistance systems include:

  • Drowsiness detection
  • Lane departure detection
  • Adaptive cruise control
  • Anti-lock braking systems
  • Global Positioning System (GPS)
  • Globalnaya Navigatsionnay Sputnikovaya Sistema (GLONASS)
  • Accident prevention systems
  • Automatic braking
  • Blind spot detection
  • Descent assistance
  • Intelligent speed control
  • Adaptive light control
  • Night vision

Driver assistance is a developing field. In particular, advanced driving assistance poses some unanswered questions, both technical and ethical. While technical concerns can be approached with improvements to new technologies like AI and machine learning, ethical concerns can be harder to tackle. Difficult situations like occupant versus pedestrian safety bring up moral questions about the future of autonomous driving systems. Tech companies like Google have formed partnerships with car manufacturers like General Motors and Ford to lead ongoing research, investigations, or discussions about these ethical dilemmas. Academic projects, such as MIT's Moral Machine, have also taken on the issue in hopes that gathering human perspectives will guide the moral decisions made by artificial intelligence.

This was last updated in October 2017

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