How Self- Driving Vehicles Perceive Intersection Structures On The Road

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Before we dive into how these autonomous vehicles see intersection on the road, let's look at the first what is Ego vehicle or car? Here, ego vehicle is a vehicle equipped with an autonomous driving system and ego vehicle driver is a driver who operates the ego vehicle. 



The ego-vehicle driver (a person) does not need to continuously monitor the ego vehicle but should be required to regain control of the driving task from the ADS (called delegation of driving authority), especially due to the limitations of the ADS functions.


Today we are going to learn about how these self driving vehicles perceive intersections on the roads using deep neural networks so questions like how many lane lines are there in each direction, where are the intersection entry and exit lines?, and application for this is autonomous intersection handling in both semi urban and urban scenarios can be answered. 



The Deep Neural Network or DNN is able to detect and classify different types of intersection structure features including intersection entry line points for the ego vehicles shown in red, intersection entry line points for other cars shown in yellow and intersection exit line points for all cars, shown in green. The DNN also detects non drivable lane lines, as shown in black.


As we can see that the perception is robust to both partial and full occlusions and that the DNN is able to predict both painted and inferred intersection structure lines. We also note that these are all per frame DNN detection results with no tracking or fusion of any kind applied. 


The ego car can determined where to stop for the intersection based on closest intersection entry line and can decide how to exit intersection using all of the intersection exit line information. 


So in this case, the DNN correctly predicts that the ego car could exit the intersection by either proceeding straight through, taking a left or right turn, or making a U-turn. 


And this perception information can be used to extrapolate how many lanes and which type of lanes these intersection lines has. 


These DNN perception results can be used in several different ways for autonomous intersection handling. They can be used to generate paths for navigation of the intersection. 


It can also be used to create a map of intersection structure and combined with previously mapped results, where it can available to create additional diversity and redundancy.

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