Interpretable Decision-Making for Autonomous Vehicles at Highway On-Ramps With Latent Space Reinforcement Learning

This paper presents a latent space reinforcement learning method for interpretable decision-making of autonomous vehicles at highway on-ramps. This method is based on the latent model and the combination model of the hidden Markov model and Gaussian mixture regression (HMM-GMR). It is difficult for the traditional decision-making method to understand the environment because its input is high-dimensional and lacks an understanding of the task. By utilizing the HMM-GMR model, we can obtain the interpretable state providing semantic information and environment understanding. A framework is proposed to unify representation learning with the deep reinforcement learning (DRL) approach, in which the latent model is used to reduce the dimension of interpretable state by extracting underlying task-relevant information. Experimental results are presented and the results show the right balance between driving safety and efficiency in the challenging scenarios of highway on-ramps merging.

Automated summary

This paper introduces a latent space reinforcement learning method for interpretable decision-making of autonomous vehicles at highway on-ramps. By utilizing the latent model and HMM-GMR, the method provides an interpretable state that offers semantic information and environment understanding. Experimental results demonstrate a successful balance between driving safety and efficiency in challenging highway on-ramps merging scenarios.