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Found 11 papers, 3 papers with code
Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective
End-to-end visual-based imitation learning has been widely applied in autonomous driving.
Tightly Coupled 3D Lidar Inertial Odometry and Mapping
By sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable estimations.
Gaze Training by Modulated Dropout Improves Imitation Learning
The basic idea behind behavioral cloning is to have the neural network learn from observing a human expert's behavior.
Utilizing Eye Gaze to Enhance the Generalization of Imitation Networks to Unseen Environments
Vision-based autonomous driving through imitation learning mimics the behaviors of human drivers by training on pairs of data of raw driver-view images and actions.
Neural Cognitive Diagnosis for Intelligent Education Systems
Cognitive diagnosis is a fundamental issue in intelligent education, which aims to discover the proficiency level of students on specific knowledge concepts.
Robot Navigation in Crowds by Graph Convolutional Networks with Attention Learned from Human Gaze
Previous work has shown the power of deep reinforcement learning frameworks to train efficient policies.
The Role of the Hercules Autonomous Vehicle During the COVID-19 Pandemic: An Autonomous Logistic Vehicle for Contactless Goods Transportation
Moreover, many countries have imposed tough lockdown measures to reduce the virus transmission (e. g., retail, catering) during the pandemic, which causes inconveniences for human daily life.
CoMoGCN: Coherent Motion Aware Trajectory Prediction with Graph Representation
Forecasting human trajectories is critical for tasks such as robot crowd navigation and autonomous driving.
HGCN-GJS: Hierarchical Graph Convolutional Network with Groupwise Joint Sampling for Trajectory Prediction
Fully investigating the social interactions within the crowd is crucial for accurate pedestrian trajectory prediction.
AVGCN: Trajectory Prediction using Graph Convolutional Networks Guided by Human Attention
We suggest that introducing an attention mechanism to infer the importance of different neighbors is critical for accurate trajectory prediction in scenes with varying crowd size.
Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars
To provide a solution to this problem, we propose a novel branched network G-CIL for the navigation policy learning.
