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

TL;DR

This paper introduces an integrated framework combining intention prediction and decision-making for autonomous driving, utilizing spectrum attention nets and PPO to improve safety, efficiency, and success rates in dynamic traffic scenarios.

Contribution

The paper presents a novel integrated approach with spectrum attention nets and PPO, explicitly modeling the coupling between prediction and decision-making for autonomous vehicles.

Findings

Outperforms several DRL baselines in success rate, efficiency, and safety.

Achieves fast intention prediction without trajectory decoding.

Demonstrates effectiveness in complex traffic scenarios.

Abstract

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.