Direct Image Based Traffic Junction Crossing System for Autonomous Vehicles

One of the most common traffic scenario when navigating in urban area is the traffic junction. Crossing a traffic junction is not trivial for an autonomous vehicle as it needs to perform both scene understanding and decision making tasks. In this work we introduce a two-stage vision-based system for an autonomous vehicle that is capable of deciding when to cross a traffic junction safely. The first stage of the system consists of various convolutional neural network (CNN) models that are utilized to obtain information about the traffic junction. The information is then used in the second stage of the system to decide whether to cross the traffic junction. Here, it is represented as affordances and directly used by a Bayesian network to infer the final decision without the need for an environment model. The Bayesian network models the decision making process by taking into consideration the traffic rules associated with a traffic junction and avoiding collision with another traffic participant entering the traffic junction. We evaluated the feasibility of the system as well as the various components within it using real world data and achieved encouraging accuracy results. The results show the potential of the system to help autonomous vehicles to cross a traffic junction safely.