# Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning

## TL;DR

This paper examines the limitations of using simple kinematic models in deep reinforcement learning for autonomous vehicle control and proposes a redesigned framework that accounts for vehicle dynamics, significantly improving control performance.

## Contribution

It introduces a modified DRL framework that incorporates vehicle dynamics, including acceleration delay and command dynamics, for more realistic car-following control.

## Key findings

- Redesigned DRL controller achieves near-optimal performance with vehicle dynamics.
- Inclusion of vehicle dynamics in training improves control accuracy.
- Traditional kinematic models lead to degraded performance when applied to real vehicle dynamics.

## Abstract

The majority of current studies on autonomous vehicle control via deep reinforcement learning (DRL) utilize point-mass kinematic models, neglecting vehicle dynamics which includes acceleration delay and acceleration command dynamics. The acceleration delay, which results from sensing and actuation delays, results in delayed execution of the control inputs. The acceleration command dynamics dictates that the actual vehicle acceleration does not rise up to the desired command acceleration instantaneously due to dynamics. In this work, we investigate the feasibility of applying DRL controllers trained using vehicle kinematic models to more realistic driving control with vehicle dynamics. We consider a particular longitudinal car-following control, i.e., Adaptive Cruise Control (ACC), problem solved via DRL using a point-mass kinematic model. When such a controller is applied to car following with vehicle dynamics, we observe significantly degraded car-following performance. Therefore, we redesign the DRL framework to accommodate the acceleration delay and acceleration command dynamics by adding the delayed control inputs and the actual vehicle acceleration to the reinforcement learning environment state, respectively. The training results show that the redesigned DRL controller results in near-optimal control performance of car following with vehicle dynamics considered when compared with dynamic programming solutions.