# Transferable and Adaptable Driving Behavior Prediction

arXiv: https://arxiv.org/abs/2202.05140 · HTML: https://mlsanity.com/paper/2202.05140/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 80 references that are in the map.
- First posted on arXiv 10 Feb 2022.

## Builds on (most cited first, 30 of 37)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [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)
- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [TNT: Target-driveN Trajectory Prediction](https://mlsanity.com/paper/2008.08294/) (2020)
- [Graph networks as learnable physics engines for inference and control](https://mlsanity.com/paper/1806.01242/) (2018)
- [Human Motion Trajectory Prediction: A Survey](https://mlsanity.com/paper/1905.06113/) (2019)
- [INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps](https://mlsanity.com/paper/1910.03088/) (2019)
- [Learning to Navigate in Complex Environments](https://mlsanity.com/paper/1611.03673/) (2016)
- [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)
- [On a Formal Model of Safe and Scalable Self-driving Cars](https://mlsanity.com/paper/1708.06374/) (2017)
- [TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents](https://mlsanity.com/paper/1811.02146/) (2018)
- [Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture](https://mlsanity.com/paper/1802.06338/) (2018)
- [How would surround vehicles move? A Unified Framework for Maneuver Classification and Motion Prediction](https://mlsanity.com/paper/1801.06523/) (2018)
- [Probabilistic Prediction of Vehicle Semantic Intention and Motion](https://mlsanity.com/paper/1804.03629/) (2018)
- [Naturalistic Driver Intention and Path Prediction using Recurrent Neural Networks](https://mlsanity.com/paper/1807.09995/) (2018)
- [Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learning](https://mlsanity.com/paper/1809.02926/) (2018)
- [Neural Machine Translation and Sequence-to-sequence Models: A Tutorial](https://mlsanity.com/paper/1703.01619/) (2017)
- [Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data](https://mlsanity.com/paper/2010.14712/) (2020)
- [Scenario-Transferable Semantic Graph Reasoning for Interaction-Aware Probabilistic Prediction](https://mlsanity.com/paper/2004.03053/) (2020)
- [Predicting Vehicle Behaviors Over An Extended Horizon Using Behavior Interaction Network](https://mlsanity.com/paper/1903.00848/) (2019)
- [Courteous Autonomous Cars](https://mlsanity.com/paper/1808.02633/) (2018)
- [Multimodal Interaction-aware Motion Prediction for Autonomous Street Crossing](https://mlsanity.com/paper/1808.06887/) (2018)

## Built on by (most cited first, 20 of 20)

- [Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer](https://mlsanity.com/paper/2207.14024/) (2022)
- [ReasonNet: End-to-End Driving with Temporal and Global Reasoning](https://mlsanity.com/paper/2305.10507/) (2023)
- [Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priors](https://mlsanity.com/paper/2305.04412/) (2023)
- [SmartRefine: A Scenario-Adaptive Refinement Framework for Efficient Motion Prediction](https://mlsanity.com/paper/2403.11492/) (2024)
- [Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning](https://mlsanity.com/paper/2209.11820/) (2022)
- [T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memory](https://mlsanity.com/paper/2403.10052/) (2024)
- [Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills](https://mlsanity.com/paper/2309.13614/) (2023)
- [Accelerating Reinforcement Learning for Autonomous Driving using Task-Agnostic and Ego-Centric Motion Skills](https://mlsanity.com/paper/2209.12072/) (2022)
- [Motion Style Transfer: Modular Low-Rank Adaptation for Deep Motion Forecasting](https://mlsanity.com/paper/2211.03165/) (2022)
- [Improving Transferability for Cross-domain Trajectory Prediction via Neural Stochastic Differential Equation](https://mlsanity.com/paper/2312.15906/) (2023)
- [DriveAgent-R1: Advancing VLM-based Autonomous Driving with Active Perception and Hybrid Thinking](https://mlsanity.com/paper/2507.20879/) (2025)
- [SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction](https://mlsanity.com/paper/2410.08669/) (2024)
- [SHAIL: Safety-Aware Hierarchical Adversarial Imitation Learning for Autonomous Driving in Urban Environments](https://mlsanity.com/paper/2204.01922/) (2022)
- [Adaptive Human Trajectory Prediction via Latent Corridors](https://mlsanity.com/paper/2312.06653/) (2023)
- [Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach](https://mlsanity.com/paper/2403.06086/) (2024)
- [Trends in Motion Prediction Toward Deployable and Generalizable Autonomy: A Revisit and Perspectives](https://mlsanity.com/paper/2505.09074/) (2025)
- [ForeSight: Multi-View Streaming Joint Object Detection and Trajectory Forecasting](https://mlsanity.com/paper/2508.07089/) (2025)
- [MetaTra: Meta-Learning for Generalized Trajectory Prediction in Unseen Domain](https://mlsanity.com/paper/2402.08221/) (2024)
- [Generalizability Analysis of Graph-based Trajectory Predictor with Vectorized Representation](https://mlsanity.com/paper/2208.03578/) (2022)
- [AdaSports-Traj: Role- and Domain-Aware Adaptation for Multi-Agent Trajectory Modeling in Sports](https://mlsanity.com/paper/2509.16095/) (2025)

## Abstract (arXiv)

While autonomous vehicles still struggle to solve challenging situations during on-road driving, humans have long mastered the essence of driving with efficient, transferable, and adaptable driving capability. By mimicking humans' cognition model and semantic understanding during driving, we propose HATN, a hierarchical framework to generate high-quality, transferable, and adaptable predictions for driving behaviors in multi-agent dense-traffic environments. Our hierarchical method consists of a high-level intention identification policy and a low-level trajectory generation policy. We introduce a novel semantic sub-task definition and generic state representation for each sub-task. With these techniques, the hierarchical framework is transferable across different driving scenarios. Besides, our model is able to capture variations of driving behaviors among individuals and scenarios by an online adaptation module. We demonstrate our algorithms in the task of trajectory prediction for real traffic data at intersections and roundabouts from the INTERACTION dataset. Through extensive numerical studies, it is evident that our method significantly outperformed other methods in terms of prediction accuracy, transferability, and adaptability. Pushing the state-of-the-art performance by a considerable margin, we also provide a cognitive view of understanding the driving behavior behind such improvement. We highlight that in the future, more research attention and effort are deserved for transferability and adaptability. It is not only due to the promising performance elevation of prediction and planning algorithms, but more fundamentally, they are crucial for the scalable and general deployment of autonomous vehicles.