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Found 6 papers, 0 papers with code
Interpretable Long Term Waypoint-Based Trajectory Prediction Model
In this paper, we study the impact of adding a long-term goal on the performance of a trajectory prediction framework.
Hierarchical Attention and Graph Neural Networks: Toward Drift-Free Pose Estimation
The most commonly used method for addressing 3D geometric registration is the iterative closet-point algorithm, this approach is incremental and prone to drift over multiple consecutive frames.
Interpretable Goal-Based model for Vehicle Trajectory Prediction in Interactive Scenarios
The abilities to understand the social interaction behaviors between a vehicle and its surroundings while predicting its trajectory in an urban environment are critical for road safety in autonomous driving.
Trajectory Prediction for Autonomous Driving based on Multi-Head Attention with Joint Agent-Map Representation
The future trajectories of agents can be inferred using two important cues: the locations and past motion of agents, and the static scene structure.
Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation
Convolutional neural networks are designed for dense data, but vision data is often sparse (stereo depth, point clouds, pen stroke, etc.).
Ranked #9 on
Depth Completion
on KITTI Depth Completion
End-to-End Race Driving with Deep Reinforcement Learning
We present research using the latest reinforcement learning algorithm for end-to-end driving without any mediated perception (object recognition, scene understanding).
