Stereo vision based environment analysis and perception for autonomous driving applications

Environment analysis and perception are two of the most important tasks in various application areas involving stereo vision. Specifically to autonomous driving applications these tasks are related to systems awareness and are direct linked to the decision process. This research proposes an architecture for environment analysis and perception with strait forward replicable design. This architecture is structured in three parts: input data, environment analysis and environment perception. Starting from a disparity map the architecture employs different representations that allow for ground area and obstacles detection. For free space assessment a polar grid is built allowing for better interpretation of the free space direction. In the end obstacle classification adds new information to the obstacles position and orientation. Classification is done using existing trained neural classifiers.