CVAE-H: Conditionalizing Variational Autoencoders via Hypernetworks and Trajectory Forecasting for Autonomous Driving

2022/01/24 by Oh, Geunseob, Peng, Huei · 2 citations
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Robotics (cs.RO)

paper · doi:10.48550/arxiv.2201.09874

Abstract

The task of predicting stochastic behaviors of road agents in diverse environments is a challenging problem for autonomous driving. To best understand scene contexts and produce diverse possible future states of the road agents adaptively in different environments, a prediction model should be probabilistic, multi-modal, context-driven, and general. We present Conditionalizing Variational AutoEncoders via Hypernetworks (CVAE-H); a conditional VAE that extensively leverages hypernetwork and performs generative tasks for high-dimensional problems like the prediction task. We first evaluate CVAE-H on simple generative experiments to show that CVAE-H is probabilistic, multi-modal, context-driven, and general. Then, we demonstrate that the proposed model effectively solves a self-driving prediction problem by producing accurate predictions of road agents in various environments.

Cited by

• IrisNet: Infrared Image Status Awareness Meta Decoder for Infrared Small Targets Detection
• GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomes

Related

• Multi-modal anticipation of stochastic trajectories in a dynamic environment with Conditional Variational Autoencoders
• Conditional Prediction by Simulation for Automated Driving
• Exploring Dynamic Context for Multi-path Trajectory Prediction
• GenAD: Generative End-to-End Autonomous Driving
• Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Prediction