17 papers with code • 1 benchmarks • 3 datasets
Motion forecasting is the task of predicting the location of a tracked object in the future
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.
Ranked #12 on Trajectory Prediction on Stanford Drone
In our baseline experiments, we illustrate how detailed map information such as lane direction, driveable area, and ground height improves the accuracy of 3D object tracking and motion forecasting.
To facilitate the training, the network is learned with an auxiliary task of predicting future location in which the activity will happen.
Ranked #1 on Trajectory Forecasting on ActEV
We propose a motion forecasting model that exploits a novel structured map representation as well as actor-map interactions.
Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds.
Ranked #1 on Trajectory Forecasting on TrajNet++
In this paper, we present a graph-based trajectory prediction network named the Dual Scale Predictor (DSP), which encodes both the static and dynamical driving context in a hierarchical manner.