# Learning Interaction-aware Motion Prediction Model for Decision-making in Autonomous Driving

arXiv: https://arxiv.org/abs/2302.03939 · HTML: https://mlsanity.com/paper/2302.03939/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving.
- Builds on 28 of its 42 references that are in the map.
- First posted on arXiv 8 Feb 2023.

## Placed in

- End-to-end autonomous driving / [Imitation-learning end-to-end planners](https://mlsanity.com/driving/imitation-learning-end-to-end-planners/)

## Builds on (most cited first, 28 of 28)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Model-Based Reinforcement Learning for Atari](https://mlsanity.com/paper/1903.00374/) (2019)
- [ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst](https://mlsanity.com/paper/1812.03079/) (2018)
- [MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction](https://mlsanity.com/paper/2111.14973/) (2021)
- [Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees](https://mlsanity.com/paper/1807.03858/) (2018)
- [SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving](https://mlsanity.com/paper/2010.09776/) (2020)
- [PiP: Planning-informed Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2003.11476/) (2020)
- [Multi-modal Motion Prediction with Transformer-based Neural Network for Autonomous Driving](https://mlsanity.com/paper/2109.06446/) (2021)
- [Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Driving](https://mlsanity.com/paper/2207.10422/) (2022)
- [A Survey on Model-based Reinforcement Learning](https://mlsanity.com/paper/2206.09328/) (2022)
- [On the role of planning in model-based deep reinforcement learning](https://mlsanity.com/paper/2011.04021/) (2020)
- [Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic](https://mlsanity.com/paper/1901.02705/) (2019)
- [Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety](https://mlsanity.com/paper/1903.00640/) (2019)
- [Identifying Driver Interactions via Conditional Behavior Prediction](https://mlsanity.com/paper/2104.09959/) (2021)
- [Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Driving](https://mlsanity.com/paper/2103.10690/) (2021)
- [Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learning](https://mlsanity.com/paper/2010.03118/) (2020)
- [Multi-modal Sensor Fusion-Based Deep Neural Network for End-to-end Autonomous Driving with Scene Understanding](https://mlsanity.com/paper/2005.09202/) (2020)
- [Conditional Predictive Behavior Planning with Inverse Reinforcement Learning for Human-like Autonomous Driving](https://mlsanity.com/paper/2212.08787/) (2022)
- [Improved Deep Reinforcement Learning with Expert Demonstrations for Urban Autonomous Driving](https://mlsanity.com/paper/2102.09243/) (2021)
- [Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Models](https://mlsanity.com/paper/2204.02392/) (2022)
- [Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous Driving](https://mlsanity.com/paper/2208.12263/) (2022)
- [Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous Driving](https://mlsanity.com/paper/2109.12516/) (2021)
- [ReCoAt: A Deep Learning-based Framework for Multi-Modal Motion Prediction in Autonomous Driving Application](https://mlsanity.com/paper/2207.00726/) (2022)
- [Interventional Behavior Prediction: Avoiding Overly Confident Anticipation in Interactive Prediction](https://mlsanity.com/paper/2204.08665/) (2022)
- [Separating the World and Ego Models for Self-Driving](https://mlsanity.com/paper/2204.07184/) (2022)

## Built on by (most cited first, 6 of 6)

- [DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Driving](https://mlsanity.com/paper/2310.05885/) (2023)
- [Hybrid-Prediction Integrated Planning for Autonomous Driving](https://mlsanity.com/paper/2402.02426/) (2024)
- [InteRACT: Transformer Models for Human Intent Prediction Conditioned on Robot Actions](https://mlsanity.com/paper/2311.12943/) (2023)
- [A Review of Learning-Based Motion Planning: Toward a Data-Driven Optimal Control Approach](https://mlsanity.com/paper/2512.11944/) (2025)
- [S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles](https://mlsanity.com/paper/2404.11946/) (2024)
- [Goal-conditioned Hierarchical Reinforcement Learning for Sample-efficient and Safe Autonomous Driving at Intersections](https://mlsanity.com/paper/2506.16336/) (2025)

## Abstract (arXiv)

Predicting the behaviors of other road users is crucial to safe and intelligent decision-making for autonomous vehicles (AVs). However, most motion prediction models ignore the influence of the AV's actions and the planning module has to treat other agents as unalterable moving obstacles. To address this problem, this paper proposes an interaction-aware motion prediction model that is able to predict other agents' future trajectories according to the ego agent's future plan, i.e., their reactions to the ego's actions. Specifically, we employ Transformers to effectively encode the driving scene and incorporate the AV's plan in decoding the predicted trajectories. To train the model to accurately predict the reactions of other agents, we develop an online learning framework, where the ego agent explores the environment and collects other agents' reactions to itself. We validate the decision-making and learning framework in three highly interactive simulated driving scenarios. The results reveal that our decision-making method significantly outperforms the reinforcement learning methods in terms of data efficiency and performance. We also find that using the interaction-aware model can bring better performance than the non-interaction-aware model and the exploration process helps improve the success rate in testing.