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Found 9 papers, 3 papers with code
On the Road to Portability: Compressing End-to-End Motion Planner for Autonomous Driving
However, the oversized neural networks render them impractical for deployment on resource-constrained systems, which unavoidably requires more computational time and resources during reference. To handle this, knowledge distillation offers a promising approach that compresses models by enabling a smaller student model to learn from a larger teacher model.
GANet: Goal Area Network for Motion Forecasting
In view of this, we propose a new goal area-based framework, named Goal Area Network (GANet), for motion forecasting, which models goal areas rather than exact goal coordinates as preconditions for trajectory prediction, performing more robustly and accurately.
Ranked #15 on
Motion Forecasting
on Argoverse CVPR 2020
CORE: Consistent Representation Learning for Face Forgery Detection
Specifically, we first capture the different representations with different augmentations, then regularize the cosine distance of the representations to enhance the consistency.
Sliding Sequential CVAE with Time Variant Socially-aware Rethinking for Trajectory Prediction
First, with the continuation of time, the prediction error at each time step increases significantly, causing the final displacement error to be impossible to ignore.
An Efficient Generation Method based on Dynamic Curvature of the Reference Curve for Robust Trajectory Planning
Then, some segments on the reference curve are not the image of any part of the planned path.
Robust Trajectory Forecasting for Multiple Intelligent Agents in Dynamic Scene
Trajectory forecasting, or trajectory prediction, of multiple interacting agents in dynamic scenes, is an important problem for many applications, such as robotic systems and autonomous driving.
VisionNet: A Drivable-space-based Interactive Motion Prediction Network for Autonomous Driving
To further advance the performance, we propose an interactive loss to guide the generation of the drivable spaces.
StarNet: Pedestrian Trajectory Prediction using Deep Neural Network in Star Topology
The hub network takes observed trajectories of all pedestrians to produce a comprehensive description of the interpersonal interactions.
On Study of the Binarized Deep Neural Network for Image Classification
Unlike those trials, in this paper, we focused on the basic propagation function of the artificial neural network and proposed the binarized deep neural network.
