Delving into the devils of bird's-eye-view perception: A review, evaluation and recipe
Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending
and drawing extensive attention both from industry and academia. Conventional …
and drawing extensive attention both from industry and academia. Conventional …
Explainability of deep vision-based autonomous driving systems: Review and challenges
This survey reviews explainability methods for vision-based self-driving systems trained with
behavior cloning. The concept of explainability has several facets and the need for …
behavior cloning. The concept of explainability has several facets and the need for …
Planning-oriented autonomous driving
Modern autonomous driving system is characterized as modular tasks in sequential order,
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …
Bevformer: Learning bird's-eye-view representation from multi-camera images via spatiotemporal transformers
Abstract 3D visual perception tasks, including 3D detection and map segmentation based on
multi-camera images, are essential for autonomous driving systems. In this work, we present …
multi-camera images, are essential for autonomous driving systems. In this work, we present …
Neural fields in visual computing and beyond
Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …
computing problems using methods that employ coordinate‐based neural networks. These …
Safety-enhanced autonomous driving using interpretable sensor fusion transformer
Large-scale deployment of autonomous vehicles has been continually delayed due to safety
concerns. On the one hand, comprehensive scene understanding is indispensable, a lack of …
concerns. On the one hand, comprehensive scene understanding is indispensable, a lack of …
Transfuser: Imitation with transformer-based sensor fusion for autonomous driving
How should we integrate representations from complementary sensors for autonomous
driving? Geometry-based fusion has shown promise for perception (eg, object detection …
driving? Geometry-based fusion has shown promise for perception (eg, object detection …
Learning from all vehicles
D Chen, P Krähenbühl - … of the IEEE/CVF Conference on …, 2022 - openaccess.thecvf.com
In this paper, we present a system to train driving policies from experiences collected not just
from the ego-vehicle, but all vehicles that it observes. This system uses the behaviors of …
from the ego-vehicle, but all vehicles that it observes. This system uses the behaviors of …
Polarformer: Multi-camera 3d object detection with polar transformer
Abstract 3D object detection in autonomous driving aims to reason “what” and “where” the
objects of interest present in a 3D world. Following the conventional wisdom of previous 2D …
objects of interest present in a 3D world. Following the conventional wisdom of previous 2D …
Model-based imitation learning for urban driving
An accurate model of the environment and the dynamic agents acting in it offers great
potential for improving motion planning. We present MILE: a Model-based Imitation …
potential for improving motion planning. We present MILE: a Model-based Imitation …