However, these sensors are often large and bulky, hindering the car from effectively navigating the surrounding air. This obstruction forces the car to consume more energy to accelerate, ultimately affecting its overall driving range. However, given the current state of autonomous vehicle development, researchers are more focused on the functional implementation of the vehicle's sensors than on aerodynamic considerations.
▍Air resistance: the enemy of speed
For half a century, automakers have devoted significant effort to refining vehicle body designs to combat air resistance—the reaction forces a vehicle must overcome as it moves forward. Over time, car curves have become smoother, and new features such as pop-up headlights, rear spoilers, and active grilles have been added to improve the efficiency of airflow around the vehicle. Automotive engineers even determine a car's aerodynamic performance by testing it in controlled wind tunnels. Cars with lower "drag coefficients" are considered to have better aerodynamics.
However, the addition of numerous sensors to autonomous vehicles makes combating air resistance much more complex. Waymo, the US-based driverless taxi company, states that each of its driverless taxis is equipped with 29 cameras arranged around the vehicle. LiDAR sensors are larger and more square, emitting millions of laser pulses in all directions around the vehicle to create 3D maps. Research has found that the protruding roof-mounted LiDAR sensors significantly "delay airflow separation," while multiple sensors mounted at the rear of the vehicle and on both sides of the bumper create a pair of air vortices, further contributing to airflow separation. In other words, all the sensors essentially work together to impede airflow, ultimately reducing the vehicle's aerodynamic performance.
▍Latest Solution
A recent study published in the journal *Physics of Fluids* by Wuhan University of Technology suggests that an optimized artificial intelligence algorithm can improve the overall aerodynamic performance of autonomous vehicles by altering the structural shape of their sensors. The researchers chose to lower the height of the front sensors to reduce the positive pressure zone and drag, while simultaneously lowering the leading edge of the roof sensors to create a "decompression effect," mitigating the direct impact of frontal airflow and allowing airflow to reach the roof. This resulted in similar drag coefficients between the optimized and basic groups. The researchers stated that compared to the standard configuration of an autonomous vehicle, the optimized version reduced total air resistance by 3.44%. This small difference accumulates over time during long-distance driving, producing a significant effect. This research demonstrates that fine-tuning the shape of roof sensors can reduce air resistance for autonomous vehicles.
Professor Wang Yiping, one of the study's authors, stated that external sensors significantly increase aerodynamic drag, particularly by increasing the proportion of interference drag in the total aerodynamic drag. Considering these factors—the interaction between sensors and the impact of geometry on interference drag—comprehensive sensor optimization during the design phase is crucial.
▍Aerodynamic sensors help reduce the energy consumption of autonomous vehicles
Self-driving car companies are now aware of the aerodynamic challenges posed by sensors. Waymo states that it strategically places sensors around vehicles to maximize their field of view (FOV). Prioritizing FOV is crucial for safety, but this can conflict with overall vehicle performance and speed. Self-driving car manufacturers have attempted to address this issue by fine-tuning sensor installations; one company says it has redesigned the beam sensors mounted on top of a semi-truck's windshield to reduce drag.
"While this may seem like a minor adjustment, it can lead to significant improvements in fuel efficiency over the lifespan of a vehicle," Waymo wrote in a blog post.
Currently, most autonomous taxis from companies like Waymo, Zoox, and domestic companies like Luobo Express and Pony.ai are limited to slower, non-highway areas, where the effectiveness of aerodynamic sensors is relatively limited. However, they are more effective in long-haul autonomous trucking. In long-haul trucking, even a small reduction in drag can lead to faster delivery times and lower overall energy consumption, which in turn reduces costs for autonomous vehicle companies and their end-user customers. Over time, this energy reduction will also help further improve the utilization rate of resource-intensive electric vehicle batteries.
Professor Wang Yiping believes that, looking to the future, the research findings of this project can provide a reference for designing more aerodynamically efficient autonomous vehicles and improving their driving range. With the increasing prevalence of autonomous vehicles, this is important not only for passenger transport but also for delivery and logistics applications.
