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Found 11 papers, 8 papers with code
End-to-End Autonomous Driving through V2X Cooperation
Cooperatively utilizing both ego-vehicle and infrastructure sensor data via V2X communication has emerged as a promising approach for advanced autonomous driving.
RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception
The value of roadside perception, which could extend the boundaries of autonomous driving and traffic management, has gradually become more prominent and acknowledged in recent years.
RoboCodeX: Multimodal Code Generation for Robotic Behavior Synthesis
Robotic behavior synthesis, the problem of understanding multimodal inputs and generating precise physical control for robots, is an important part of Embodied AI.
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Flow-Based Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object Detection
To address these issues in vehicle-infrastructure cooperative 3D (VIC3D) object detection, we propose the Feature Flow Net (FFNet), a novel cooperative detection framework.
Learning Cooperative Trajectory Representations for Motion Forecasting
Specifically, we present V2X-Graph, the first interpretable and end-to-end learning framework for cooperative motion forecasting.
QUEST: Query Stream for Practical Cooperative Perception
In this paper, we propose the concept of query cooperation to enable interpretable instance-level flexible feature interaction.
V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting
Utilizing infrastructure and vehicle-side information to track and forecast the behaviors of surrounding traffic participants can significantly improve decision-making and safety in autonomous driving.
Vehicle-Infrastructure Cooperative 3D Object Detection via Feature Flow Prediction
Cooperatively utilizing both ego-vehicle and infrastructure sensor data can significantly enhance autonomous driving perception abilities.
DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection
Autonomous driving faces great safety challenges for a lack of global perspective and the limitation of long-range perception capabilities.
Search What You Want: Barrier Panelty NAS for Mixed Precision Quantization
Learning to find an optimal mixed precision model that can preserve accuracy and satisfy the specific constraints on model size and computation is extremely challenge due to the difficult in training a mixed precision model and the huge space of all possible bit quantizations.
GDRQ: Group-based Distribution Reshaping for Quantization
Low-bit quantization is challenging to maintain high performance with limited model capacity (e. g., 4-bit for both weights and activations).
