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Found 7 papers, 0 papers with code
GMFlow: Global Motion-Guided Recurrent Flow for 6D Object Pose Estimation
6D object pose estimation is crucial for robotic perception and precise manipulation.
SEMPose: A Single End-to-end Network for Multi-object Pose Estimation
On the LM-O and YCB-V datasets, our method outperforms other RGB-based single-model methods, achieving higher accuracy.
A Cooperation-Aware Lane Change Method for Automated Vehicles
Thereafter, we propose a motion planning algorithm based on model predictive control (MPC), which incorporates AV’s decision and surrounding vehicles’ interactive behaviors into constraints so as to avoid collisions during lane change.
HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation
Real-time 6D object pose estimation is essential for many real-world applications, such as robotic grasping and augmented reality.
Global Update Guided Federated Learning
Furthermore, considering that the update direction of a global model is informative in the early stage of training, we propose adaptive loss weights based on the update distances of local models.
A Cooperation-Aware Lane Change Method for Autonomous Vehicles
Further, an evaluation is designed to make a decision on lane change, in which safety, efficiency and comfort are taken into consideration.
Graph-Based Spatial-Temporal Convolutional Network for Vehicle Trajectory Prediction in Autonomous Driving
This paper proposes a graph-based spatial-temporal convolutional network (GSTCN) to predict future trajectory distributions of all neighbor vehicles using past trajectories.
