Detecting Driveable Area for Autonomous Vehicles
This addresses a critical perception task for autonomous driving systems, but appears incremental as it applies an existing method to a specific dataset.
The paper tackled the problem of detecting driveable areas for autonomous vehicles, specifically differentiating between the current lane and alternative lanes, using a Mask R-CNN trained on the BDD100k dataset, but no concrete results or numbers were reported.
Autonomous driving is a challenging problem where there is currently an intense focus on research and development. Human drivers are forced to make thousands of complex decisions in a short amount of time,quickly processing their surroundings and moving factors. One of these aspects, recognizing regions on the road that are driveable is vital to the success of any autonomous system. This problem can be addressed with deep learning framed as a region proposal problem. Utilizing a Mask R-CNN trained on the Berkeley Deep Drive (BDD100k) dataset, we aim to see if recognizing driveable areas, while also differentiating between the car's direct (current) lane and alternative lanes is feasible.