Autonomous vehicle sensors don’t have to be a drag, find researchers

Deformation control volumes are set for the front sensor, front-side sensor, roof sensor, and rear-side sensor, which affect the aerodynamic drag coefficient. The sensor shapes can be modified by adjusting the control points. Credit: Yiping Wang

Thanks to the rapid progress of information technology and artificial intelligence, autonomous vehicle technology has been taking off. In fact, AVs are now advanced enough that they are being used for logistics delivery and low-speed public transportation.

While most research has focused on control algorithms to heighten autonomous vehicle 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.

In Physics of Fluids, from AIP Publishing, researchers from Wuhan University of Technology in Wuhan, China, focused on enhancing the aerodynamic performance of AVs. Their goal was to reduce drag from externally mounted sensors such as cameras and lidar instruments, which are necessary for AV functionality.

“Externally mounted sensors significantly increase aerodynamic drag, particularly by increasing the proportion of interference drag within the total aerodynamic drag,” said author Yiping Wang. “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.”

Scientists calculate shapes for drag reduction 

The researchers 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 autonomous vehicle sensors.

Finally, they performed simulations of both the baseline and optimized models, analyzing the effects of drag reduction and examining the improvements in the aerodynamic performance of the optimized model. They used a wind tunnel to validate the reliability of their findings.

Autonomous vehicle design can be optimized

After optimizing the design, researchers found a 3.44% decrease in the total aerodynamic drag of an autonomous vehicle. Compared with the baseline model, the optimized model reduced the aerodynamic drag coefficient by 5.99% in simulations and significantly improved aerodynamic performance in unsteady simulations.

The team also observed improvements in airflow, with less turbulence around the sensors and better pressure distribution at the back of the vehicle.

“Looking ahead, our findings could inform the design of more aerodynamically efficient autonomous vehicles, enabling them to travel longer distances,” said Wang. “This is especially important as the adoption of autonomous vehicles increases, not only in passenger transport but also in delivery and logistics applications.”

The article, “Numerical and experimental investigations of the aerodynamic drag characteristics and reduction of an autonomous vehicle,” was authored by Jian Zhao, Chuqi Su, Xun Liu, Junyan Wang, Dongxu Tang, and Yiping Wang.

Editor’s note: Companies testing AVs in China include AutoX, Baidu, Haomo.AI, Inceptio, IVECO, Plus, Momenta, Pony.ai, Uisee, Waymo, and WeRide. Beijing’s government last week passed rules to allow road trials for autonomous buses and robotaxis.

Register today to save 40% on conference passes!

Keep track of your essentials with the Apple AirTag 4 Pack, the ultimate tracking solution for your belongings. With over 5,972 ratings and a stellar 4.7-star average, this product has quickly become a customer favorite. Over 10,000 units were purchased in the past month, solidifying its status as a highly rated Amazon Choice product.

For just $79.98, you can enjoy peace of mind knowing your items are always within reach. Order now for only $79.98 at Amazon!