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 …
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 …
Drivevlm: The convergence of autonomous driving and large vision-language models
A primary hurdle of autonomous driving in urban environments is understanding complex
and long-tail scenarios, such as challenging road conditions and delicate human behaviors …
and long-tail scenarios, such as challenging road conditions and delicate human behaviors …
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 …
Reasonnet: End-to-end driving with temporal and global reasoning
The large-scale deployment of autonomous vehicles is yet to come, and one of the major
remaining challenges lies in urban dense traffic scenarios. In such cases, it remains …
remaining challenges lies in urban dense traffic scenarios. In such cases, it remains …
Neat: Neural attention fields for end-to-end autonomous driving
Efficient reasoning about the semantic, spatial, and temporal structure of a scene is a crucial
prerequisite for autonomous driving. We present NEural ATtention fields (NEAT), a novel …
prerequisite for autonomous driving. We present NEural ATtention fields (NEAT), a novel …
Hierarchical interpretable imitation learning for end-to-end autonomous driving
End-to-end autonomous driving provides a simple and efficient framework for autonomous
driving systems, which can directly obtain control commands from raw perception data …
driving systems, which can directly obtain control commands from raw perception data …
Attention for vision-based assistive and automated driving: A review of algorithms and datasets
I Kotseruba, JK Tsotsos - IEEE transactions on intelligent …, 2022 - ieeexplore.ieee.org
Driving safety has been a concern since the first cars appeared on the streets. Driver
inattention has been singled out as a major cause of accidents early on. This is hardly …
inattention has been singled out as a major cause of accidents early on. This is hardly …