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 …
Explainable artificial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions
Autonomous driving has achieved significant milestones in research and development over
the last decade. There is increasing interest in the field as the deployment of self-operating …
the last decade. There is increasing interest in the field as the deployment of self-operating …
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 …
Diffusion-based generation, optimization, and planning in 3d scenes
We introduce SceneDiffuser, a conditional generative model for 3D scene understanding.
SceneDiffuser provides a unified model for solving scene-conditioned generation …
SceneDiffuser provides a unified model for solving scene-conditioned generation …
Motion transformer with global intention localization and local movement refinement
Predicting multimodal future behavior of traffic participants is essential for robotic vehicles to
make safe decisions. Existing works explore to directly predict future trajectories based on …
make safe decisions. Existing works explore to directly predict future trajectories based on …
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 …
[PDF][PDF] Drive like a human: Rethinking autonomous driving with large language models
In this paper, we explore the potential of using a large language model (LLM) to understand
the driving environment in a human-like manner and analyze its ability to reason, interpret …
the driving environment in a human-like manner and analyze its ability to reason, interpret …
Vad: Vectorized scene representation for efficient autonomous driving
Autonomous driving requires a comprehensive understanding of the surrounding
environment for reliable trajectory planning. Previous works rely on dense rasterized scene …
environment for reliable trajectory planning. Previous works rely on dense rasterized scene …
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 …
Multipath++: Efficient information fusion and trajectory aggregation for behavior prediction
Predicting the future behavior of road users is one of the most challenging and important
problems in autonomous driving. Applying deep learning to this problem requires fusing …
problems in autonomous driving. Applying deep learning to this problem requires fusing …