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Concept
Lane centering
Résumé
In road-transport terminology, lane centering, also known as auto steer or autosteer, is an advanced driver-assistance system that keeps a road vehicle centered in the lane, relieving the driver of the task of steering. Lane centering is similar to lane departure warning and lane keeping assist, but rather than warn the driver, or bouncing the car away from the lane edge, it keeps the car centered in the lane. Together with adaptive cruise control (ACC), this feature may allow unassisted driving for some length of time. It is also part of automated lane keeping systems.
Starting in 2019 semi-trailer trucks have also been fitted with this technology.
Lane departure warning generates a warning when the vehicle crosses a line, while lane keeping assist helps the vehicle to avoid crossing a line, standardized in ISO 11270:2014, and lane centering keeps the vehicle centered in the lane and almost always comes with steering assist to help the vehicle take gentle turns at highway speeds.
In farming, "machine autosteer" is a technology which make automated steering and positioning of a machine in a landscape.
The first commercially available lane centering systems were based on off-the-shelf systems created by Mobileye, such as Tesla Autopilot and Nissan ProPilot, although Tesla switched to an in-house design when Mobileye ended their partnership. A handful of companies like Bosch, Delphi, ZF and Mobileye provide sensors, control units, and even algorithms to car makers, who then integrate and refine those systems.
While not directly attributable to lane centering, crash rates on the Tesla Model S and Model X equipped with the Mobileye system were reduced by almost 40% while Tesla Autopilot was in use.
The lane detection system used by the lane departure warning system uses image processing techniques to detect lane lines from real-time camera images fed from cameras mounted on the automobile. Examples of image processing techniques used include the Hough transform, Canny edge detector, Gabor filter and deep learning.
Source officielle
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