Self-driving vehicles are one of the most technologically-advanced cars in which humans are not required for controlling or operating it. The software programs and various sensors integrated into it takes care of that task. Hence, sometimes these specialized vehicles are also referred as autonomous, self-driven or “driverless” cars.
How do they work?
Self-driving vehicles have the potential to effectively sense its environment and accordingly navigate without any need of human aid or intervention. To make this a reality, vehicles are equipped with wide range of various specialized devices and sensors such as GPS unit, a navigation system, laser rangefinders, radars, and video cameras. The GPS caters to the need of position-related information to localize itself. This data gets further refined via a plethora of sensors attached so as to create a three-dimensional image of its environment.
The primary information collected by various sensors and installed devices is not comprehensive and also contains noise. Hence, to achieve precision, this primary information is refined by filtering data which eliminates noise. After that, it is combined with information gathered by other data sources to further augment the original image. Based on all these data feed, final navigation decisions are taken up by the control system. Now let’s dive into some technical aspects.
It is the primary step, in which vehicle builds a map of its vicinity and precisely locates itself in that map. To do this task, vehicles take the help of sensor fitted into it. They include multiple cameras, laser rangefinders etc. Similar to the way how a Sonar works, through series of laser beams, a laser rangefinder examines its surrounding and by tracking the time taken by these beams to travel to the various nearby objects and back, distance from these object is determined. This data is further augmented and refined by the data captured by video cameras, which extract accurate scene colors and precisely tell about the related depth information. This data helps vehicles in building up a three-dimensional map of its surrounding.
These vehicles are fed with the information encompassing current and predicted location data of all dynamic (e.g. pedestrians and other moving vehicles) and static (e.g. street board signs, buildings etc.) obstacles present in their proximity. Obstacles are identified on account of how well they are similar to the internal library data containing a plethora of predefined shapes and motion descriptors. For tracking the predicted future path of a moving object, these self-driving vehicles make use of the Probabilistic model. All these techniques make vehicle competent in identifying and appropriately avoiding obstacles.
The ultimate objective of path planning is to effectively use all the information recorded in these autonomous vehicles to steer them to its final destination while obeying all the rules of the road and avoiding all obstacles.
All these processes mentioned above are continuously repeated till the vehicle safely reaches its final destination.
Though significant progress has been made in the past decades, to make self-driving car a reality, yet there exist several technological barriers which need to be addressed before making these vehicles safe for road use. On the positive side, the quantum of road and traffic data available for these vehicles has considerably increased due to the incorporation of newly developed sensors which collect, greater and precise data. Even the algorithms processing this data are being constantly improved to make them highly efficient. With advancement in technology, the self-driving cars would certainly emerge out as one of the best gifts to mankind.