# RESET: Revisiting Trajectory Sets for Conditional Behavior Prediction

arXiv: https://arxiv.org/abs/2304.05856 · HTML: https://mlsanity.com/paper/2304.05856/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 28 references that are in the map.
- First posted on arXiv 12 Apr 2023.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [Learning Lane Graph Representations for Motion Forecasting](https://mlsanity.com/paper/2007.13732/) (2020)
- [DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents](https://mlsanity.com/paper/1704.04394/) (2017)
- [DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets](https://mlsanity.com/paper/2108.09640/) (2021)
- [CoverNet: Multimodal Behavior Prediction using Trajectory Sets](https://mlsanity.com/paper/1911.10298/) (2019)
- [PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings](https://mlsanity.com/paper/1905.01296/) (2019)
- [Multiple Futures Prediction](https://mlsanity.com/paper/1911.00997/) (2019)
- [HOME: Heatmap Output for future Motion Estimation](https://mlsanity.com/paper/2105.10968/) (2021)
- [Implicit Latent Variable Model for Scene-Consistent Motion Forecasting](https://mlsanity.com/paper/2007.12036/) (2020)
- [LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting](https://mlsanity.com/paper/2101.06653/) (2021)
- [What-If Motion Prediction for Autonomous Driving](https://mlsanity.com/paper/2008.10587/) (2020)
- [Learning to Predict Vehicle Trajectories with Model-based Planning](https://mlsanity.com/paper/2103.04027/) (2021)
- [Identifying Driver Interactions via Conditional Behavior Prediction](https://mlsanity.com/paper/2104.09959/) (2021)
- [Conditional Predictive Behavior Planning with Inverse Reinforcement Learning for Human-like Autonomous Driving](https://mlsanity.com/paper/2212.08787/) (2022)
- [CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-Attention](https://mlsanity.com/paper/2202.04488/) (2022)
- [SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2301.03512/) (2023)
- [BANet: Motion Forecasting with Boundary Aware Network](https://mlsanity.com/paper/2206.07934/) (2022)

## Built on by (most cited first, 4 of 4)

- [Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network](https://mlsanity.com/paper/2407.18551/) (2024)
- [KI-PMF: Knowledge Integrated Plausible Motion Forecasting](https://mlsanity.com/paper/2310.12007/) (2023)
- [Motion Forecasting via Model-Based Risk Minimization](https://mlsanity.com/paper/2409.10585/) (2024)
- [Efficient Data Representation for Motion Forecasting: A Scene-Specific Trajectory Set Approach](https://mlsanity.com/paper/2407.20732/) (2024)

## Abstract (arXiv)

It is desirable to predict the behavior of traffic participants conditioned on different planned trajectories of the autonomous vehicle. This allows the downstream planner to estimate the impact of its decisions. Recent approaches for conditional behavior prediction rely on a regression decoder, meaning that coordinates or polynomial coefficients are regressed. In this work we revisit set-based trajectory prediction, where the probability of each trajectory in a predefined trajectory set is determined by a classification model, and first-time employ it to the task of conditional behavior prediction. We propose RESET, which combines a new metric-driven algorithm for trajectory set generation with a graph-based encoder. For unconditional prediction, RESET achieves comparable performance to a regression-based approach. Due to the nature of set-based approaches, it has the advantageous property of being able to predict a flexible number of trajectories without influencing runtime or complexity. For conditional prediction, RESET achieves reasonable results with late fusion of the planned trajectory, which was not observed for regression-based approaches before. This means that RESET is computationally lightweight to combine with a planner that proposes multiple future plans of the autonomous vehicle, as large parts of the forward pass can be reused.