Diverse Trajectory Forecasting with Determinantal Point Processes

07/11/2019
by   Ye Yuan, et al.
0

The ability to forecast a set of likely yet diverse possible future behaviors of an agent (e.g., future trajectories of a pedestrian) is essential for safety-critical perception systems (e.g., autonomous vehicles). In particular, a set of possible future behaviors generated by the system must be diverse to account for all possible outcomes in order to take necessary safety precautions. It is not sufficient to maintain a set of the most likely future outcomes because the set may only contain perturbations of a single outcome. While generative models such as variational autoencoders (VAEs) have been shown to be a powerful tool for learning a distribution over future trajectories, randomly drawn samples from the learned implicit likelihood model may not be diverse -- the likelihood model is derived from the training data distribution and the samples will concentrate around the major mode that has most data. In this work, we propose to learn a diversity sampling function (DSF) that generates a diverse and likely set of future trajectories. The DSF maps forecasting context features to a set of latent codes which can be decoded by a generative model (e.g., VAE) into a set of diverse trajectory samples. Concretely, the process of identifying the diverse set of samples is posed as a parameter estimation of the DSF. To learn the parameters of the DSF, the diversity of the trajectory samples is evaluated by a diversity loss based on a determinantal point process (DPP). Gradient descent is performed over the DSF parameters, which in turn move the latent codes of the sample set to find an optimal diverse and likely set of trajectories. Our method is a novel application of DPPs to optimize a set of items (trajectories) in continuous space. We demonstrate the diversity of the trajectories produced by our approach on both low-dimensional 2D trajectory data and high-dimensional human motion data.

READ FULL TEXT

page 2

page 3

page 4

page 5

page 6

page 7

page 10

page 12

research
11/30/2020

Diverse Sampling for Normalizing Flow Based Trajectory Forecasting

For autonomous cars to drive safely and effectively, they must anticipat...
research
02/07/2023

Diverse Probabilistic Trajectory Forecasting with Admissibility Constraints

Predicting multiple trajectories for road users is important for automat...
research
04/12/2019

Generative Hybrid Representations for Activity Forecasting with No-Regret Learning

Automatically reasoning about future human behaviors is a difficult prob...
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
03/19/2020

DLow: Diversifying Latent Flows for Diverse Human Motion Prediction

Deep generative models are often used for human motion prediction as the...
research
03/19/2020

Joint 3D Tracking and Forecasting with Graph Neural Network and Diversity Sampling

3D multi-object tracking (MOT) and trajectory forecasting are two critic...
research
07/15/2022

Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary Space

Diverse human motion prediction aims at predicting multiple possible fut...

Please sign up or login with your details

Forgot password? Click here to reset