An online evolving framework for advancing reinforcement-learning based automated vehicle control

TL;DR

This paper introduces an online evolving framework that enhances reinforcement learning-based vehicle control by detecting and revising suboptimal decisions in real-time, improving safety and reliability in autonomous driving scenarios.

Contribution

The paper presents a novel online evolving framework with modules for dynamic state modeling and action validation, advancing reinforcement learning control methods for autonomous vehicles.

Findings

Inappropriate actions are effectively detected and revised.

The framework reduces control failures in vehicle following scenarios.

Enhanced safety and decision accuracy in autonomous driving.

Abstract

In this paper, an online evolving framework is proposed to detect and revise a controller's imperfect decision-making in advance. The framework consists of three modules: the evolving Finite State Machine (e-FSM), action-reviser, and controller modules. The e-FSM module evolves a stochastic model (e.g., Discrete-Time Markov Chain) from scratch by determining new states and identifying transition probabilities repeatedly. With the latest stochastic model and given criteria, the action-reviser module checks validity of the controller's chosen action by predicting future states. Then, if the chosen action is not appropriate, another action is inspected and selected. In order to show the advantage of the proposed framework, the Deep Deterministic Policy Gradient (DDPG) w/ and w/o the online evolving framework are applied to control an ego-vehicle in the car-following scenario where control criteria are set by speed and safety. Experimental results show that inappropriate actions chosen by the DDPG controller are detected and revised appropriately through our proposed framework, resulting in no control failures after a few iterations.