Scene Understanding Networks for Autonomous Driving based on Around View Monitoring System

TL;DR

This paper presents an end-to-end camera-based system for scene understanding in autonomous driving, focusing on safe drivable area detection, obstacle proximity, and object detection, aiming for cost-effective and integrated solutions.

Contribution

It introduces a unified architecture for obstacle, curb, and drivable area detection using AVM systems, including high accuracy and low complexity network variants, with added 3D object detection capabilities.

Findings

Effective drivable area delimitation from camera data

Unified architecture for multiple scene understanding tasks

Enhanced 3D object detection integration

Abstract

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.