Stepwise Goal-Driven Networks for Trajectory Prediction

25 Mar 2021  ·  Chuhua Wang, Yuchen Wang, Mingze Xu, David J. Crandall ·

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, and modeling goals continuously provides more accurate and detailed information for future trajectory estimation... In this paper, we present a novel recurrent network for trajectory prediction, called Stepwise Goal-Driven Network (SGNet). Unlike prior work that models only a single, long-term goal, SGNet estimates and uses goals at multiple temporal scales. In particular, the framework incorporates an encoder module that captures historical information, a stepwise goal estimator that predicts successive goals into the future, and a decoder module that predicts future trajectory. We evaluate our model on three first-person traffic datasets (HEV-I, JAAD, and PIE) as well as on two bird's eye view datasets (ETH and UCY), and show that our model outperforms the state-of-the-art methods in terms of both average and final displacement errors on all datasets. Code has been made available at: https://github.com/ChuhuaW/SGNet.pytorch. read more

PDF Abstract

Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Trajectory Prediction ETH/UCY SGNet ADE-8/12 0.18 # 1
FDE-8/12 0.35 # 2
Trajectory Prediction HEV-I SGNet ADE(0.5) 6.28 # 1
ADE(1.0) 11.35 # 1
ADE(1.5) 18.27 # 1
FDE(1.5) 39.86 # 1
FIOU(1.5) 0.63 # 1
Multi-future Trajectory Prediction JAAD SGNet MSE(0.5) 37 # 1
MSE(1.0) 86 # 1
MSE(1.5) 197 # 1
C_MSE(1.5) 146 # 1
CF_MSE(1.5) 443 # 1
Trajectory Prediction JAAD SGNet MSE(0.5) 82 # 1
MSE(1.0) 328 # 1
MSE(1.5) 1049 # 1
C_MSE(1.5) 996 # 1
CF_MSE(1.5) 4076 # 1
Trajectory Prediction PIE SGNet MSE(0.5) 34 # 1
MSE(1.0) 133 # 1
MSE(1.5) 442 # 1
C_MSE(1.5) 413 # 1
CF_MSE(1.5) 1761 # 1
Multi-future Trajectory Prediction PIE SGNet MSE(0.5) 16 # 1
MSE(1.0) 39 # 1
MSE(1.5) 88 # 1
C_MSE(1.5) 66 # 1
CF_MSE(1.5) 206 # 1

Methods


No methods listed for this paper. Add relevant methods here