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 …
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 …
Voxelnext: Fully sparse voxelnet for 3d object detection and tracking
Abstract 3D object detectors usually rely on hand-crafted proxies, eg, anchors or centers,
and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be …
and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be …
A survey on trajectory-prediction methods for autonomous driving
In order to drive safely in a dynamic environment, autonomous vehicles should be able to
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …
Query-centric trajectory prediction
Predicting the future trajectories of surrounding agents is essential for autonomous vehicles
to operate safely. This paper presents QCNet, a modeling framework toward pushing the …
to operate safely. This paper presents QCNet, a modeling framework toward pushing the …
Vectormapnet: End-to-end vectorized hd map learning
Autonomous driving systems require High-Definition (HD) semantic maps to navigate
around urban roads. Existing solutions approach the semantic mapping problem by offline …
around urban roads. Existing solutions approach the semantic mapping problem by offline …
A survey on multimodal large language models for autonomous driving
With the emergence of Large Language Models (LLMs) and Vision Foundation Models
(VFMs), multimodal AI systems benefiting from large models have the potential to equally …
(VFMs), multimodal AI systems benefiting from large models have the potential to equally …
End-to-end autonomous driving: Challenges and frontiers
The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …
Pivotnet: Vectorized pivot learning for end-to-end hd map construction
Vectorized high-definition map online construction has garnered considerable attention in
the field of autonomous driving research. Most existing approaches model changeable map …
the field of autonomous driving research. Most existing approaches model changeable map …
V2x-seq: A large-scale sequential dataset for vehicle-infrastructure cooperative perception and forecasting
Utilizing infrastructure and vehicle-side information to track and forecast the behaviors of
surrounding traffic participants can significantly improve decision-making and safety in …
surrounding traffic participants can significantly improve decision-making and safety in …