Papers Multi-future Trajectory Prediction
“Multi-future Trajectory Prediction” 태그가 달린 논문 10편 · 필터 해제
Vision-based Multi-future Trajectory Prediction: A Survey
Vision-based trajectory prediction is an important task that supports safe and intelligent behaviours in autonomous systems. Many advanced approaches have been proposed over the years with improved spatial and temporal f…
Multi-future Trajectory PredictionMulti Future Trajectory PredictionPredictionSurvey+1Social-Implicit: Rethinking Trajectory Prediction Evaluation and The Effectiveness of Implicit Maximum Likelihood Estimation
Best-of-N (BoN) Average Displacement Error (ADE)/ Final Displacement Error (FDE) is the most used metric for evaluating trajectory prediction models. Yet, the BoN does not quantify the whole generated samples, resulting …
Human motion predictionmotion predictionMulti-future Trajectory PredictionPedestrian Trajectory Prediction+1Stepwise Goal-Driven Networks for Trajectory Prediction
We propose to predict the future trajectories of observed agents (e.g., pedestrians or vehicles) by estimating and using their goals at multiple time scales. We argue that the goal of a moving agent may change over time,…
DecoderMulti-future Trajectory PredictionPredictionTrajectory PredictionBiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal Estimation
Pedestrian trajectory prediction is an essential task in robotic applications such as autonomous driving and robot navigation. State-of-the-art trajectory predictors use a conditional variational autoencoder (CVAE) with …
Autonomous DrivingCollision AvoidanceDecoderMulti-future Trajectory Prediction+4DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting
Understanding human motion behaviour is a critical task for several possible applications like self-driving cars or social robots, and in general for all those settings where an autonomous agent has to navigate inside a …
Graph Neural NetworkHuman motion predictionMulti-future Trajectory PredictionNavigate+3AC-VRNN: Attentive Conditional-VRNN for Multi-Future Trajectory Prediction
Anticipating human motion in crowded scenarios is essential for developing intelligent transportation systems, social-aware robots and advanced video surveillance applications. A key component of this task is represented…
Graph AttentionMulti-future Trajectory PredictionMulti Future Trajectory PredictionTrajectory PredictionIt Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction
Human trajectory forecasting with multiple socially interacting agents is of critical importance for autonomous navigation in human environments, e.g., for self-driving cars and social robots. In this work, we present Pr…
Autonomous NavigationMulti-future Trajectory PredictionMulti Future Trajectory PredictionPrediction+3The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction
This paper studies the problem of predicting the distribution over multiple possible future paths of people as they move through various visual scenes. We make two main contributions. The first contribution is a new data…
Autonomous DrivingHuman motion predictionmotion predictionMulti-future Trajectory Prediction+4Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs
This paper proposes a novel approach for predicting the motion of pedestrians interacting with others. It uses a Generative Adversarial Network (GAN) to sample plausible predictions for any agent in the scene. As GANs ar…
Generative Adversarial NetworkHuman motion predictionMulti-future Trajectory PredictionPedestrian Trajectory Prediction+3Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments. This is challenging because human motion is i…
Collision AvoidanceMotion ForecastingMulti-future Trajectory PredictionNavigate+3