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Autonomous Driving
| Applications | |
|---|---|
| Title | Autonomous Driving |
| Author(s) | GMV. |
| Level | Medium |
| Year of Publication | 2011 |
The main drivers for achieving autonomous driving is the reduction of traffic accidents by eliminating human error, increasing road capacity and traffic flow by reducing distance between cars and making use of traffic management information, relieving the car occupants from driving and navigation activities and allowing them to engage in other activities or rest[1].
A driverless car will have be combinations of several techniques among which GNSS that will attain the objective of taking a land vehicle from point A to point B using public roads. In autonomous driving GNSS can be used for navigation being used to determine the vehicle location, vehicle velocity and current. This information will then be used to decide the vehicle route using digital maps. If the accuracy is good enough GNSS can be used also for lane determination and even for attitude determination. If the location information is shared among cars, GNSS could theoretically be used for short-range situation awareness although it is not expected that GNSS will be used in the future as sole means of information for short-range situation awareness.
Application Architecture
Application Characterization
A restricted form of Autonomous driving is Advanced Driving Assistance Systems (ADAS). ADAS combines vehicle capabilities to improve mobility and active safety. GNSS will provide important additional data to ADAS on the vehicle’s environment. ADAS then warns the driver of imminent danger or takes full or partial control over the vehicle. For instance, the speed could be reduced by ADAS under bad visibility conditions if the car approaches a tight turn too fast. This function will be possible with accurate position data of guaranteed integrity.
Application Examples
Notes