3D object detection for autonomous driving: A comprehensive survey
Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …
Deep learning for 3d point clouds: A survey
Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …
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 lidar-camera via spatiotemporal transformers
Multi-modality fusion strategy is currently the de-facto most competitive solution for 3D
perception tasks. In this work, we present a new framework termed BEVFormer, which learns …
perception tasks. In this work, we present a new framework termed BEVFormer, which learns …
Bytetrack: Multi-object tracking by associating every detection box
Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in
videos. Most methods obtain identities by associating detection boxes whose scores are …
videos. Most methods obtain identities by associating detection boxes whose scores are …
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 …
Multi-modal fusion transformer for end-to-end autonomous driving
How should representations from complementary sensors be integrated for autonomous
driving? Geometry-based sensor fusion has shown great promise for perception tasks such …
driving? Geometry-based sensor fusion has shown great promise for perception tasks such …
St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning
Many existing autonomous driving paradigms involve a multi-stage discrete pipeline of
tasks. To better predict the control signals and enhance user safety, an end-to-end approach …
tasks. To better predict the control signals and enhance user safety, an end-to-end approach …
Beverse: Unified perception and prediction in birds-eye-view for vision-centric autonomous driving
In this paper, we present BEVerse, a unified framework for 3D perception and prediction
based on multi-camera systems. Unlike existing studies focusing on the improvement of …
based on multi-camera systems. Unlike existing studies focusing on the improvement of …
Learning from all vehicles
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 …