# Enhancing Vehicle Aerodynamics with Deep Reinforcement Learning in Voxelised Models

arXiv: https://arxiv.org/abs/2405.11492 · HTML: https://mlsanity.com/paper/2405.11492/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 19 May 2024.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 6 of 6)

- [Vox-Fusion: Dense Tracking and Mapping with Voxel-based Neural Implicit Representation](https://mlsanity.com/paper/2210.15858/) (2022)
- [Deep Reinforcement Learning for Multi-objective Optimization](https://mlsanity.com/paper/1906.02386/) (2019)
- [A review on Deep Reinforcement Learning for Fluid Mechanics](https://mlsanity.com/paper/1908.04127/) (2019)
- [Machine Learning in Aerodynamic Shape Optimization](https://mlsanity.com/paper/2202.07141/) (2022)
- [Direct shape optimization through deep reinforcement learning](https://mlsanity.com/paper/1908.09885/) (2019)
- [Learning the aerodynamic design of supercritical airfoils through deep reinforcement learning](https://mlsanity.com/paper/2010.03651/) (2020)

## Built on by (most cited first, 3 of 3)

- [Towards a Reward-Free Reinforcement Learning Framework for Vehicle Control](https://mlsanity.com/paper/2502.15262/) (2025)
- [Airfoil optimization using Design-by-Morphing with minimized design-space dimensionality](https://mlsanity.com/paper/2510.16020/) (2025)
- [Offline Reinforcement Learning for Fluid Controls: Data-based Multi-observational Policy Extraction](https://mlsanity.com/paper/2606.31025/) (2026)

## Abstract (arXiv)

Aerodynamic design optimisation plays a crucial role in improving the performance and efficiency of automotive vehicles. This paper presents a novel approach for aerodynamic optimisation in car design using deep reinforcement learning (DRL). Traditional optimisation methods often face challenges in handling the complexity of the design space and capturing non-linear relationships between design parameters and aerodynamic performance metrics. This study addresses these challenges by employing DRL to learn optimal aerodynamic design strategies in a voxelised model representation. The proposed approach utilises voxelised models to discretise the vehicle geometry into a grid of voxels, allowing for a detailed representation of the aerodynamic flow field. The Proximal Policy Optimisation (PPO) algorithm is then employed to train a DRL agent to optimise the design parameters of the vehicle with respect to drag force, kinetic energy, and voxel collision count. Experimental results demonstrate the effectiveness and efficiency of the proposed approach in achieving significant results in aerodynamic performance. The findings highlight the potential of DRL techniques for addressing complex aerodynamic design optimisation problems in automotive engineering, with implications for improving vehicle performance, fuel efficiency, and environmental sustainability.