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- Planning-oriented Autonomous Driving1,690
This work introduces Unified Autonomous Driving (UniAD), a comprehensive framework up-to-date that incorporates full-stack driving tasks in one network and is exquisitely devised to leverage advantages of each module, and provide complementary feature abstractions for agent interaction from a global perspective.
- Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks1,364
This work introduces Grounded SAM, which uses Grounding DINO as an open-set object detector to combine with the segment anything model (SAM), which enables the detection and segmentation of any regions based on arbitrary text inputs and opens a door to connecting various vision models.
- End-to-End Autonomous Driving: Challenges and Frontiers1,023
This survey provides a comprehensive analysis of more than 270 papers, covering the motivation, roadmap, methodology, challenges, and future trends in end-to-end autonomous driving, and explores several critical challenges, including multi-modality, interpretability, causal confusion, robustness, and world models.
- BEVFormer: Learning Bird’s-Eye-View Representation From LiDAR-Camera via Spatiotemporal Transformers593
This work presents a new framework termed BEVFormer, which learns unified BEV representations from multi-modality data with spatiotemporal transformers to support multiple autonomous driving perception tasks, achieving outstanding results across these tasks.
- Scene as Occupancy297
OccNet is proposed, a multi-view vision-centric pipeline with a cascade and temporal voxel decoder to reconstruct 3D occupancy embedding to represent 3D physical world and there are evident performance gain across multiple tasks, demonstrating the superiority of this method.
- HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding272
Heterogeneous Driving Graph Transformer (HDGT), a backbone modelling the driving scene as a heterogeneous graph with different types of nodes and edges, achieves state-of-the-art performance for the task of trajectory prediction, on INTERACTION Prediction Challenge and Waymo Open Motion Challenge.
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- Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving258
The above refinement module could be stacked in a cascaded fashion, which extends the capacity of the decoder with spatial-temporal prior knowledge about the conditioned future, and achieves state-of-the-art performance in closed-loop benchmarks.
- Delving Into the Devils of Bird’s-Eye-View Perception: A Review, Evaluation and Recipe252
A full suite of practical guidebook to improve the performance of BEV perception tasks, including camera, LiDAR and fusion inputs is introduced, and the future research directions in this area are pointed out.
- DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving204
This work aims to explore the possibility of directly adopting the strong teacher model to conduct planning while letting the student model focus more on the perception part, and proposes DriveAdapter, which employs adapters with the feature alignment objective function between the student (perception) and teacher (planning) modules.
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- A Survey of Reasoning with Foundation Models: Concepts, Methodologies, and Outlook142
This article introduces seminal foundation models proposed or adaptable for reasoning, highlighting the latest advancements in various reasoning tasks, methods, and benchmarks, and delves into the potential future directions behind the emergence of reasoning abilities within foundation models.
- Dual Deep Network for Visual Tracking121
The proposed dual network is updated online in a unique manner based on the observation, that the target being tracked in consecutive frames should share more similar feature representations than those in the surrounding background.
- LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving80
This work introduces LaneSegNet, the first end-to-end mapping network generating lane segments to obtain a complete representation of the road structure, and features a lane attention module to capture pivotal region details within the long-range feature space.
- Detection Transformer with Stable Matching73
This paper proposes two simple yet effective modifications by integrating positional metrics to DETR’s classification loss and matching cost, named position-supervised loss and position-modulated cost, and shows consistent improvements over baselines.
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- Centaur: Robust End-to-End Autonomous Driving with Test-Time Training49
Centaur (Cluster Entropy for Test-time trAining using Uncertainty) is proposed, which updates a planner's behavior via test-time training, without relying on hand-engineered rules or cost functions, and measures and minimize the uncertainty in the planner's decisions.
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- FastMAC: Stochastic Spectral Sampling of Correspondence Graph43
This paper builds a complete 3D registration algorithm termed as FastMAC, that reaches real-time speed while leading to little to none performance drop, and exploits the generalized degree signal on correspondence graph and pursue sampling strategies that preserve highfrequency components of this signal.
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- DGGT: Feedforward 4D Reconstruction of Dynamic Driving Scenes using Unposed Images26
This work reformulates pose as an output of the model, enabling reconstruction directly from sparse, unposed images and supporting an arbitrary number of views for long sequences, and introduces DGGT, a unified framework for pose-free dynamic scene reconstruction.
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- Action recognition by saliency-based dense sampling20
It is shown that an appropriate pruning of feature points can achieve a good balance between saliency and density of the sampled points, and the fusion of deep-learned features and hand-crafted features can even improve the recognition performance over baseline dense sampling methods.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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