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

arXiv: https://arxiv.org/abs/1911.12736 · HTML: https://mlsanity.com/paper/1911.12736/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2019.

## Built on by (most cited first, 30 of 40)

- [LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving](https://mlsanity.com/paper/2101.06547/) (2021)
- [M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction](https://mlsanity.com/paper/2202.11884/) (2022)
- [LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agents](https://mlsanity.com/paper/2104.00249/) (2021)
- [ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning](https://mlsanity.com/paper/2206.13387/) (2022)
- [StretchBEV: Stretching Future Instance Prediction Spatially and Temporally](https://mlsanity.com/paper/2203.13641/) (2022)
- [Social-Implicit: Rethinking Trajectory Prediction Evaluation and The Effectiveness of Implicit Maximum Likelihood Estimation](https://mlsanity.com/paper/2203.03057/) (2022)
- [Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction](https://mlsanity.com/paper/2104.08277/) (2021)
- [End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps](https://mlsanity.com/paper/2203.16910/) (2022)
- [Trajectory Prediction with Linguistic Representations](https://mlsanity.com/paper/2110.09741/) (2021)
- [Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting](https://mlsanity.com/paper/2207.05195/) (2022)
- [HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling](https://mlsanity.com/paper/2110.02344/) (2021)
- [Diverse Multiple Trajectory Prediction Using a Two-stage Prediction Network Trained with Lane Loss](https://mlsanity.com/paper/2206.08641/) (2022)
- [RAP: Risk-Aware Prediction for Robust Planning](https://mlsanity.com/paper/2210.01368/) (2022)
- [Context-Aware Timewise VAEs for Real-Time Vehicle Trajectory Prediction](https://mlsanity.com/paper/2302.10873/) (2023)
- [DROID: Driver-centric Risk Object Identification](https://mlsanity.com/paper/2106.13201/) (2021)
- [Self-Supervised Action-Space Prediction for Automated Driving](https://mlsanity.com/paper/2109.10024/) (2021)
- [Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction](https://mlsanity.com/paper/2203.04421/) (2022)
- [Imitation Learning of Hierarchical Driving Model: from Continuous Intention to Continuous Trajectory](https://mlsanity.com/paper/2010.10393/) (2020)
- [TIP: Task-Informed Motion Prediction for Intelligent Vehicles](https://mlsanity.com/paper/2110.08750/) (2021)
- [SUPR-GAN: SUrgical PRediction GAN for Event Anticipation in Laparoscopic and Robotic Surgery](https://mlsanity.com/paper/2105.04642/) (2021)
- [Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions](https://mlsanity.com/paper/2409.13144/) (2024)
- [Blending Data-Driven Priors in Dynamic Games](https://mlsanity.com/paper/2402.14174/) (2024)
- [Hypergraph-Transformer (HGT) for Interactive Event Prediction in Laparoscopic and Robotic Surgery](https://mlsanity.com/paper/2402.01974/) (2024)
- [Stochasticity in Motion: An Information-Theoretic Approach to Trajectory Prediction](https://mlsanity.com/paper/2410.01628/) (2024)
- [Motion Forecasting via Model-Based Risk Minimization](https://mlsanity.com/paper/2409.10585/) (2024)
- [Trajectory Forecasting on Temporal Graphs](https://mlsanity.com/paper/2207.00255/) (2022)
- [A Fast and Map-Free Model for Trajectory Prediction in Traffics](https://mlsanity.com/paper/2307.09831/) (2023)
- [TAFormer: A Unified Target-Aware Transformer for Video and Motion Joint Prediction in Aerial Scenes](https://mlsanity.com/paper/2403.18238/) (2024)
- [INTENT: Trajectory Prediction Framework with Intention-Guided Contrastive Clustering](https://mlsanity.com/paper/2503.04952/) (2025)
- [Fast nonlinear risk assessment for autonomous vehicles using learned conditional probabilistic models of agent futures](https://mlsanity.com/paper/2109.09975/) (2021)

## Abstract (arXiv)

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.