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Found 10 papers, 8 papers with code
DriveLM: Driving with Graph Visual Question Answering
The experiments demonstrate that Graph VQA provides a simple, principled framework for reasoning about a driving scene, and DriveLM-Data provides a challenging benchmark for this task.
Leveraging Vision-Centric Multi-Modal Expertise for 3D Object Detection
Current research is primarily dedicated to advancing the accuracy of camera-only 3D object detectors (apprentice) through the knowledge transferred from LiDAR- or multi-modal-based counterparts (expert).
Ranked #6 on
3D Object Detection
on nuScenes Camera Only
OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping
Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute correct judgments.
Sparse Dense Fusion for 3D Object Detection
With the prevalence of multimodal learning, camera-LiDAR fusion has gained popularity in 3D object detection.
Planning-oriented Autonomous Driving
Oriented at this, we revisit the key components within perception and prediction, and prioritize the tasks such that all these tasks contribute to planning.
Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe
As sensor configurations get more complex, integrating multi-source information from different sensors and representing features in a unified view come of vital importance.
BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers
In a nutshell, BEVFormer exploits both spatial and temporal information by interacting with spatial and temporal space through predefined grid-shaped BEV queries.
PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark
Methods for 3D lane detection have been recently proposed to address the issue of inaccurate lane layouts in many autonomous driving scenarios (uphill/downhill, bump, etc.).
Ranked #5 on
3D Lane Detection
on Apollo Synthetic 3D Lane
LSH-SMILE: Locality Sensitive Hashing Accelerated Simulation and Learning
The advancement of deep neural networks over the last decade has enabled progress in scientific knowledge discovery in the form of learning Partial Differential Equations (PDEs) directly from experiment data.
