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Found 13 papers, 4 papers with code
It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction
In this work, we present Predicted Endpoint Conditioned Network (PECNet) for flexible human trajectory prediction.
Ranked #1 on
Multi Future Trajectory Prediction
on ETH/UCY
Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction
In addition, we introduce a new dataset designed specifically for autonomous-driving scenarios in areas with dense pedestrian populations: the Stanford-TRI Intent Prediction (STIP) dataset.
Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty
Reasoning about the future behavior of other agents is critical to safe robot navigation.
SPIGAN: Privileged Adversarial Learning from Simulation
Deep Learning for Computer Vision depends mainly on the source of supervision. Photo-realistic simulators can generate large-scale automatically labeled syntheticdata, but introduce a domain gap negatively impacting performance.
An Attention-based Recurrent Convolutional Network for Vehicle Taillight Recognition
Vehicle taillight recognition is an important application for automated driving, especially for intent prediction of ado vehicles and trajectory planning of the ego vehicle.
Disentangling Human Dynamics for Pedestrian Locomotion Forecasting with Noisy Supervision
In contrast to the previous work that aims to solve either the task of pose prediction or trajectory forecasting in isolation, we propose a framework to unify the two problems and address the practically useful task of pedestrian locomotion prediction in the wild.
Spatio-Temporal Graph for Video Captioning with Knowledge Distillation
In this paper, we propose a novel spatio-temporal graph model for video captioning that exploits object interactions in space and time.
PillarFlow: End-to-end Birds-eye-view Flow Estimation for Autonomous Driving
In autonomous driving, accurately estimating the state of surrounding obstacles is critical for safe and robust path planning.
Behaviorally Diverse Traffic Simulation via Reinforcement Learning
Traffic simulators are important tools in autonomous driving development.
Discovering Avoidable Planner Failures of Autonomous Vehicles using Counterfactual Analysis in Behaviorally Diverse Simulation
Automated Vehicles require exhaustive testing in simulation to detect as many safety-critical failures as possible before deployment on public roads.
Learning Optical Flow, Depth, and Scene Flow without Real-World Labels
However, the simultaneous self-supervised learning of depth and scene flow is ill-posed, as there are infinitely many combinations that result in the same 3D point.
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables
One key challenge for multi-task Reinforcement learning (RL) in practice is the absence of task indicators.
