By Stephen Beech
Artificial intelligence is making driverless vehicles more efficient so they can travel longer distances, according to new research.
State-of-the-art AI technology is reducing aerodynamic drag caused by externally mounted sensors, say scientists.
Autonomous vehicles (AVs) are already being used for logistics delivery and low-speed public transportation.
While most research has focused on control algorithms to heighten safety, less attention has been directed at improving aerodynamic performance, which is essential for lowering energy consumption and extending driving range.
As a result, aerodynamic drag issues have been preventing self-driving vehicles from keeping pace with regular vehicle acceleration.
Researchers at the Wuhan University of Technology in China have been focusing on enhancing the aerodynamic performance of AVs by reducing drag caused by externally mounted sensors such as cameras and light detection and ranging (LiDAR) instruments, which are necessary for AVs to work.
Study author Professor Yiping Wang said: “Externally mounted sensors significantly increase aerodynamic drag, particularly by increasing the proportion of interference drag within the total aerodynamic drag.
“Considering these factors – the interactions among sensors and the impact of geometric dimensions on interference drag – it is essential to perform a comprehensive optimization of the sensors during the design phase.”
The Chinese research team used a combination of computational and experimental methods.
After establishing an automated computational platform, they combined the experimental design with a substitute model and an optimization algorithm to improve the structural shapes of AV sensors.
The team performed simulations of both the original and optimised models, analysing the effects of drag reduction and examining the improvements in the aerodynamic performance of the optimised model.
They used a wind tunnel experiment to validate the reliability of their findings, published in the journal Physics of Fluids.
After optimising the design, the researchers found a 3.44% decrease in the total aerodynamic drag of an AV.
Compared with the original model, the optimized model reduced the aerodynamic drag coefficient by 5.99% in simulations and “significantly” improved aerodynamic performance in unsteady simulations.
The Chinese team also observed improvements in airflow, with less turbulence around the sensors and better pressure distribution at the back of the vehicle.
Wang added: “Looking ahead, our findings could inform the design of more aerodynamically efficient autonomous vehicles, enabling them to travel longer distances.
“This is especially important as the adoption of autonomous vehicles increases, not only in passenger transport but also in delivery and logistics applications.”
