Scene Understanding Networks for Autonomous Driving based on Around View Monitoring System
This addresses cost reduction and scene understanding for autonomous driving systems, but appears incremental as it builds on existing AVM and detection methods.
The paper tackles scene understanding for autonomous driving by proposing an end-to-end solution using Around View Monitoring (AVM) camera systems to reduce sensor costs, achieving joint detection of objects, curbs, and safe drivable areas with distance calculations to obstacles.
Modern driver assistance systems rely on a wide range of sensors (RADAR, LIDAR, ultrasound and cameras) for scene understanding and prediction. These sensors are typically used for detecting traffic participants and scene elements required for navigation. In this paper we argue that relying on camera based systems, specifically Around View Monitoring (AVM) system has great potential to achieve these goals in both parking and driving modes with decreased costs. The contributions of this paper are as follows: we present a new end-to-end solution for delimiting the safe drivable area for each frame by means of identifying the closest obstacle in each direction from the driving vehicle, we use this approach to calculate the distance to the nearest obstacles and we incorporate it into a unified end-to-end architecture capable of joint object detection, curb detection and safe drivable area detection. Furthermore, we describe the family of networks for both a high accuracy solution and a low complexity solution. We also introduce further augmentation of the base architecture with 3D object detection.