Enhancing Vehicle Aerodynamics with Deep Reinforcement Learning in Voxelised Models
On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic
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.
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.
Builds on
6 of 32 references.
References · 6
- 2022Vox-Fusion: Dense Tracking and Mapping with Voxel-based Neural Implicit Representationcited by 149
- 2019Deep Reinforcement Learning for Multi-objective Optimizationcited by 90
- 2019A review on Deep Reinforcement Learning for Fluid Mechanicscited by 66
- 2022Machine Learning in Aerodynamic Shape Optimizationcited by 39
- 2019Direct shape optimization through deep reinforcement learningcited by 28
- 2020Learning the aerodynamic design of supercritical airfoils through deep reinforcement learningcited by 10
Built on by
Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025Towards a Reward-Free Reinforcement Learning Framework for Vehicle Controlcited by 0
- 2025Airfoil optimization using Design-by-Morphing with minimized design-space dimensionalitycited by 0
- 2026Offline Reinforcement Learning for Fluid Controls: Data-based Multi-observational Policy Extractioncited by 0
- 2026Offline Reinforcement Learning for Fluid Controls: Data-based Multi-observational Policy Extraction30 Jun 2026
- 2025Airfoil optimization using Design-by-Morphing with minimized design-space dimensionality15 Oct 2025
- 2025Towards a Reward-Free Reinforcement Learning Framework for Vehicle Control21 Feb 2025
Similar, not cited
Close in wording, no citation either way
- 2025Transfer learning-enhanced deep reinforcement learning for aerodynamic airfoil optimisation subject to structural constraints
- 2024Demystifying the Physics of Deep Reinforcement Learning-Based Autonomous Vehicle Decision-Making
- 2024Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey
- 2025Digital Twin-based Control Co-Design of Full Vehicle Active Suspensions via Deep Reinforcement Learning
- 2024Towards Active Flow Control Strategies Through Deep Reinforcement Learning
- 2025CuRLA: Curriculum Learning Based Deep Reinforcement Learning for Autonomous Driving
- 2025Vision based driving agent for race car simulation environments
- 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles