DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling

2019/11/28 by Xin Huang, Stephen G. McGill, Huang, Xin +11 · 1 citation
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Machine Learning (cs.LG) #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.1911.12736

openalex publication_date 2019/11/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Vehicle trajectory prediction is crucial for autonomous driving and advanced driver assistant systems. While existing approaches may sample from a predicted distribution of vehicle trajectories, they lack the ability to explore it -- a key ability for evaluating safety from a planning and verification perspective. In this work, we devise a novel approach for generating realistic and diverse vehicle trajectories. We extend the generative adversarial network (GAN) framework with a low-dimensional approximate semantic space, and shape that space to capture semantics such as merging and turning. We sample from this space in a way that mimics the predicted distribution, but allows us to control coverage of semantically distinct outcomes. We validate our approach on a publicly available dataset and show results that achieve state-of-the-art prediction performance, while providing improved coverage of the space of predicted trajectory semantics.

Citations

• Better Mixing via Deep Representations
• Clustering to minimize the maximum intercluster distance
• Representation Learning: A Review and New Perspectives
• Probabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Network
• Learning Self-Imitating Diverse Policies
• Interaction-aware Multi-agent Tracking and Probabilistic Behavior Prediction via Adversarial Learning
• Conditional Generative Neural System for Probabilistic Trajectory Prediction
• Diverse Trajectory Forecasting with Determinantal Point Processes
• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
• Interpreting the Latent Space of GANs for Semantic Face Editing
• Argoverse: 3D Tracking and Forecasting with Rich Maps

Cited by

• Controllable Generative Trajectory Prediction via Weak Preference Alignment

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