Towards Robust Autonomous Driving: Out-of-Distribution Object Detection in Bird's Eye View Space

Abstract

In autonomous driving, understanding the surroundings is crucial for safety. Since most object detection systems are designed to identify known objects, they may miss unknown or novel objects, which can be dangerous. This study addresses Out-Of-Distribution (OOD) detection for vehicle-like unknown objects within the Bird's Eye View (BeV) space, a top-down representation of the environment that provides a comprehensive spatial layout crucial for scene understanding. Enhancing the model's adaptability to unfamiliar objects, we present two novel methods for detecting unknown objects in BeV space. Specifically, we introduce random patches and OOD objects in the environment to help the model identify both known objects, such as vehicles, and OOD objects. We also introduce a new dataset, NuScenesOOD, derived from the NuScenes dataset, which augments vehicles with patterns and shapes to challenge the model. Additionally, we address challenges such as patch size inconsistency and occlusion from moving frames in BeV space. Our method targets vehicle-shaped anomalies in the planar driving space, maintaining high accuracy for known and enhancing detection of unknown objects. This research contributes to making future autonomous vehicles safer by improving their ability to detect diverse vehicle like OOD objects in their environment.