# TAE: A Semi-supervised Controllable Behavior-aware Trajectory Generator and Predictor

arXiv: https://arxiv.org/abs/2203.01261 · HTML: https://mlsanity.com/paper/2203.01261/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 39 references that are in the map.
- First posted on arXiv 2 Mar 2022.

## Builds on (most cited first, 21 of 21)

- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [Learning Lane Graph Representations for Motion Forecasting](https://mlsanity.com/paper/2007.13732/) (2020)
- [Adversarial Autoencoders](https://mlsanity.com/paper/1511.05644/) (2015)
- [TNT: Target-driveN Trajectory Prediction](https://mlsanity.com/paper/2008.08294/) (2020)
- [AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting](https://mlsanity.com/paper/2103.14023/) (2021)
- [CoverNet: Multimodal Behavior Prediction using Trajectory Sets](https://mlsanity.com/paper/1911.10298/) (2019)
- [Multimodal Motion Prediction with Stacked Transformers](https://mlsanity.com/paper/2103.11624/) (2021)
- [DLow: Diversifying Latent Flows for Diverse Human Motion Prediction](https://mlsanity.com/paper/2003.08386/) (2020)
- [GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation](https://mlsanity.com/paper/2109.01827/) (2021)
- [TPCN: Temporal Point Cloud Networks for Motion Forecasting](https://mlsanity.com/paper/2103.03067/) (2021)
- [Multi-Head Attention for Multi-Modal Joint Vehicle Motion Forecasting](https://mlsanity.com/paper/1910.03650/) (2019)
- [What-If Motion Prediction for Autonomous Driving](https://mlsanity.com/paper/2008.10587/) (2020)
- [Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation](https://mlsanity.com/paper/2009.08311/) (2020)
- [Probabilistic Prediction of Vehicle Semantic Intention and Motion](https://mlsanity.com/paper/1804.03629/) (2018)
- [CMTS: Conditional Multiple Trajectory Synthesizer for Generating Safety-critical Driving Scenarios](https://mlsanity.com/paper/1910.00099/) (2019)
- [Diverse Critical Interaction Generation for Planning and Planner Evaluation](https://mlsanity.com/paper/2103.00906/) (2021)
- [Physics-Aware Safety-Assured Design of Hierarchical Neural Network based Planner](https://mlsanity.com/paper/2201.09140/) (2022)
- [Online Vehicle Trajectory Prediction using Policy Anticipation Network and Optimization-based Context Reasoning](https://mlsanity.com/paper/1903.00847/) (2019)
- [End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering](https://mlsanity.com/paper/2103.00345/) (2021)
- [Trajectory Planning for Connected and Automated Vehicles: Cruising, Lane Changing, and Platooning](https://mlsanity.com/paper/2001.08620/) (2020)

## Built on by (most cited first, 10 of 10)

- [Empowering Autonomous Driving with Large Language Models: A Safety Perspective](https://mlsanity.com/paper/2312.00812/) (2023)
- [Data-driven Traffic Simulation: A Comprehensive Review](https://mlsanity.com/paper/2310.15975/) (2023)
- [Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-based Decision-Making Systems](https://mlsanity.com/paper/2405.20774/) (2024)
- [SceneDM: Scene-level Multi-agent Trajectory Generation with Consistent Diffusion Models](https://mlsanity.com/paper/2311.15736/) (2023)
- [Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction](https://mlsanity.com/paper/2205.14230/) (2022)
- [Learning Representation for Anomaly Detection of Vehicle Trajectories](https://mlsanity.com/paper/2303.05000/) (2023)
- [Kinematics-aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling](https://mlsanity.com/paper/2309.09317/) (2023)
- [Adversarial and Reactive Traffic Entities for Behavior-Realistic Driving Simulation: A Review](https://mlsanity.com/paper/2409.14196/) (2024)
- [Risk-Informed Diffusion Transformer for Long-Tail Trajectory Prediction in the Crash Scenario](https://mlsanity.com/paper/2501.16349/) (2025)
- [Multi-Agent Scenario Generation in Roundabouts with a Transformer-enhanced Conditional Variational Autoencoder](https://mlsanity.com/paper/2510.24671/) (2025)

## Abstract (arXiv)

Trajectory generation and prediction are two interwoven tasks that play important roles in planner evaluation and decision making for intelligent vehicles. Most existing methods focus on one of the two and are optimized to directly output the final generated/predicted trajectories, which only contain limited information for critical scenario augmentation and safe planning. In this work, we propose a novel behavior-aware Trajectory Autoencoder (TAE) that explicitly models drivers' behavior such as aggressiveness and intention in the latent space, using semi-supervised adversarial autoencoder and domain knowledge in transportation. Our model addresses trajectory generation and prediction in a unified architecture and benefits both tasks: the model can generate diverse, controllable and realistic trajectories to enhance planner optimization in safety-critical and long-tailed scenarios, and it can provide prediction of critical behavior in addition to the final trajectories for decision making. Experimental results demonstrate that our method achieves promising performance on both trajectory generation and prediction.