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Found 12 papers, 9 papers with code
Generalized Predictive Model for Autonomous Driving
In this paper, we introduce the first large-scale video prediction model in the autonomous driving discipline.
V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs
However, the lack of this visual search mechanism in current multimodal LLMs (MLLMs) hinders their ability to focus on important visual details, especially when handling high-resolution and visually crowded images.
Ranked #79 on
Visual Question Answering
on MM-Vet
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 motion plans, instead of concentrating on individual tasks such as detection and motion prediction.
Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving
End-to-end autonomous driving has made impressive progress in recent years.
Ranked #4 on
CARLA longest6
on CARLA
Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling
Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving.
Inharmonious Region Localization via Recurrent Self-Reasoning
Synthetic images created by image editing operations are prevalent, but the color or illumination inconsistency between the manipulated region and background may make it unrealistic.
Inharmonious Region Localization with Auxiliary Style Feature
Based on the extracted style features, we also propose a novel style voting module to guide the localization of inharmonious region.
Inharmonious Region Localization by Magnifying Domain Discrepancy
Inharmonious region localization aims to localize the region in a synthetic image which is incompatible with surrounding background.
ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning
In particular, we propose a spatial-temporal feature learning scheme towards a set of more representative features for perception, prediction and planning tasks simultaneously, which is called ST-P3.
Ranked #7 on
Bird's-Eye View Semantic Segmentation
on nuScenes
(IoU ped - 224x480 - Vis filter. - 100x100 at 0.5 metric)
Level 2 Autonomous Driving on a Single Device: Diving into the Devils of Openpilot
Equipped with a wide span of sensors, predominant autonomous driving solutions are becoming more modular-oriented for safe system design.
Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
The two branches are connected so that the control branch receives corresponding guidance from the trajectory branch at each time step.
Ranked #3 on
Autonomous Driving
on CARLA Leaderboard
HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding
Based on these observations, we propose Heterogeneous Driving Graph Transformer (HDGT), a backbone modelling the driving scene as a heterogeneous graph with different types of nodes and edges.
