Role of GPS Tracking Systems in Self-Driven Cars


Self-driving cars are the newest trend in the automobile sector, and there’s a lot of excitement and fear amongst the general population about it. Experts state that these autonomous cars will be soon be seen on the road by 2020 and the majority of them will be out by 2050. There are many companies like Apple, Google, Tesla, Uber, Ford, GM, and others, which are making a break-through in the market, by building their own version of electric cars and autonomous vehicles.

However, accomplishing this feat requires a combination of very advanced technologies performing in synchronization with great accuracy, precision and without errors. Multiple automation frameworks for vehicles were developed to address various situations encountered by a vehicle on the road. One of these systems is the tracking system that sets the precise location of the vehicle. They utilize a digital road map, which detects the road limits and curb them using a Light Image Detecting and Ranging (LIDAR) sensor, to keep the vehicle focused between the road limits.


This also brings us to an interesting question:


What does GPS have to do with self-driving cars?


Well, the answer would be: Every. Single. Thing.

The tracking system in self-driving cars use GPS data to roughly evaluate the vehicle’s location area and with the help of a laser scanner it can easily monitor the surroundings of the vehicle, and roughly estimate the vehicle’s location by coordinates and improve it by the relative positional changes of surrounding items. Likewise, the GPS tracking systems position the vehicle by adhering to special lines or markers. Other automation frameworks also include collision avoidance systems. When facing a possible collision, a driver may have two choices, either to slam the brakes or to steer. A collision avoidance system can control the braking of the vehicle to either stop the vehicle before reaching the obstacle or maintain a safe distance from other vehicles.


One might say, progresses in the self-driving vehicle industry are about dealing with huge amounts of information. The hardware equipment in self-driving cars creates enormous piles of data since it’s crucial to know precisely where a vehicle is and what’s around it for safety.

Sensors in a vehicle may include LIDAR, GPS, IMU (Inertial Measurement Unit), a radar for detecting other objects and vehicles, and a camera that captures the environment around.


The challenge is taking in all this information, blending it, and processing it quick enough to be able to make split-second choices, like whether or not to shift into another lane when an accident seems imminent.


Since the majority of this hardware creates so much information, and because it’s so expensive, a full sensor apparatus can easily cost $100k per vehicle or above.

Maps for self-driving cars rely upon specially equipped mapping vehicles, which are ultimately loaded into the consumer cars that navigate by ceaselessly using their own sensor array to compare the map to the actual surrounding environment and instruct the car where to go safely.


With GNSS and GPS technologies and numerous different developments growing across the globe consistently, it seems as if a driver-less fleet is set to become mainstream in the following decade or so, probably much sooner than anticipated.

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