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

arXiv: https://arxiv.org/abs/1905.08314 · HTML: https://mlsanity.com/paper/1905.08314/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2019.

## Built on by (most cited first, 10 of 10)

- [Separated Proportional-Integral Lagrangian for Chance Constrained Reinforcement Learning](https://mlsanity.com/paper/2102.08539/) (2021)
- [A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles](https://mlsanity.com/paper/2105.14218/) (2021)
- [Hybrid Car-Following Strategy based on Deep Deterministic Policy Gradient and Cooperative Adaptive Cruise Control](https://mlsanity.com/paper/2103.03796/) (2021)
- [EnsembleFollower: A Hybrid Car-Following Framework Based On Reinforcement Learning and Hierarchical Planning](https://mlsanity.com/paper/2308.16008/) (2023)
- [Deep Reinforcement Learning Aided Platoon Control Relying on V2X Information](https://mlsanity.com/paper/2203.15781/) (2022)
- [Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programming](https://mlsanity.com/paper/2206.07536/) (2022)
- [Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning](https://mlsanity.com/paper/2212.07594/) (2022)
- [Vessel-following model for inland waterways based on deep reinforcement learning](https://mlsanity.com/paper/2207.03257/) (2022)
- [Vision-based DRL Autonomous Driving Agent with Sim2Real Transfer](https://mlsanity.com/paper/2305.11589/) (2023)
- [AVDDPG: Federated reinforcement learning applied to autonomous platoon control](https://mlsanity.com/paper/2207.03484/) (2022)

## Abstract (arXiv)

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.