Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Plans

01/03/2020
by   Nachiket Deo, et al.
29

In this paper, we address the problem of forecasting agent trajectories in unknown environments, conditioned on their past motion and scene structure. Trajectory forecasting is a challenging problem due to the large variation in scene structure, and the multi-modal nature of the distribution of future trajectories. Unlike prior approaches that directly learn one-to-many mappings from observed context, to multiple future trajectories, we propose to condition trajectory forecasts on plans sampled from a grid based policy learned using maximum entropy inverse reinforcement learning policy (MaxEnt IRL). We reformulate MaxEnt IRL to allow the policy to jointly infer plausible agent goals and paths to those goals on a coarse 2-D grid defined over an unknown scene. We propose an attention based trajectory generator that generates continuous valued future trajectories conditioned on state sequences sampled from the MaxEnt policy. Quantitative and qualitative evaluation on the publicly available Stanford drone dataset (SDD) shows that our model generates trajectories that are (1) diverse, representing the multi-modal predictive distribution, and (2) precise, conforming to the underlying scene structure over long prediction horizons, achieving state of the art results on the TrajNet benchmark split of SDD.

READ FULL TEXT

page 2

page 6

page 8

page 10

page 11

research
05/23/2019

Scene Induced Multi-Modal Trajectory Forecasting via Planning

We address multi-modal trajectory forecasting of agents in unknown scene...
research
03/31/2022

End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps

In this paper, we aim to forecast a future trajectory distribution of a ...
research
09/16/2019

Scene Compliant Trajectory Forecast with Agent-Centric Spatio-Temporal Grids

Forecasting long-term human motion is a challenging task due to the non-...
research
12/13/2019

The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction

This paper studies the problem of predicting the distribution over multi...
research
03/05/2021

Multi-modal anticipation of stochastic trajectories in a dynamic environment with Conditional Variational Autoencoders

Forecasting short-term motion of nearby vehicles presents an inherently ...
research
10/02/2020

Goal-GAN: Multimodal Trajectory Prediction Based on Goal Position Estimation

In this paper, we present Goal-GAN, an interpretable and end-to-end trai...
research
12/22/2016

First-Person Activity Forecasting with Online Inverse Reinforcement Learning

We address the problem of incrementally modeling and forecasting long-te...

Please sign up or login with your details

Forgot password? Click here to reset