# Integrated Intention Prediction and Decision-Making with Spectrum Attention Net and Proximal Policy Optimization

arXiv: https://arxiv.org/abs/2408.03191 · HTML: https://mlsanity.com/paper/2408.03191/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 6 Aug 2024.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 13 of 13)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Driving](https://mlsanity.com/paper/2207.10422/) (2022)
- [ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planning](https://mlsanity.com/paper/2206.13387/) (2022)
- [View Vertically: A Hierarchical Network for Trajectory Prediction via Fourier Spectrums](https://mlsanity.com/paper/2110.07288/) (2021)
- [Alternating Direction Method of Multipliers for Constrained Iterative LQR in Autonomous Driving](https://mlsanity.com/paper/2011.00462/) (2020)
- [Reward-Driven Automated Curriculum Learning for Interaction-Aware Self-Driving at Unsignalized Intersections](https://mlsanity.com/paper/2403.13674/) (2024)
- [State Sequences Prediction via Fourier Transform for Representation Learning](https://mlsanity.com/paper/2310.15888/) (2023)
- [Curriculum Proximal Policy Optimization with Stage-Decaying Clipping for Self-Driving at Unsignalized Intersections](https://mlsanity.com/paper/2308.16445/) (2023)
- [Autonomous Vehicle Parking in Dynamic Environments: An Integrated System with Prediction and Motion Planning](https://mlsanity.com/paper/2204.10383/) (2022)
- [A Hierarchical Hybrid Learning Framework for Multi-agent Trajectory Prediction](https://mlsanity.com/paper/2303.12274/) (2023)
- [End-To-End Training and Testing Gamification Framework to Learn Human Highway Driving](https://mlsanity.com/paper/2404.10849/) (2024)

## Built on by (most cited first, 1 of 1)

- [SocialTraj: Two-Stage Socially-Aware Trajectory Prediction for Autonomous Driving via Conditional Diffusion Model](https://mlsanity.com/paper/2509.17850/) (2025)

## Abstract (arXiv)

For autonomous driving in highly dynamic environments, it is anticipated to predict the future behaviors of surrounding vehicles (SVs) and make safe and effective decisions. However, modeling the inherent coupling effect between the prediction and decision-making modules has been a long-standing challenge, especially when there is a need to maintain appropriate computational efficiency. To tackle these problems, we propose a novel integrated intention prediction and decision-making approach, which explicitly models the coupling relationship and achieves efficient computation. Specifically, a spectrum attention net is designed to predict the intentions of SVs by capturing the trends of each frequency component over time and their interrelations. Fast computation of the intention prediction module is attained as the predicted intentions are not decoded to trajectories in the executing process. Furthermore, the proximal policy optimization (PPO) algorithm is employed to address the non-stationary problem in the framework through a modest policy update enabled by a clipping mechanism within its objective function. On the basis of these developments, the intention prediction and decision-making modules are integrated through joint learning. Experiments are conducted in representative traffic scenarios, and the results reveal that the proposed integrated framework demonstrates superior performance over several deep reinforcement learning (DRL) baselines in terms of success rate, efficiency, and safety in driving tasks.