The ultimate goal for vehicle aerodynamicists is to develop vehicles that perform well on the road under the real-world conditions. One of the most important metric to evaluate the car performance is the drag coefficient. However, vehicle development today is performed mostly under controlled settings using wind tunnels and computational fluid dynamics (CFD) with artificially uniform freestream conditions, neglecting real-world effects due to road turbulence from wind and other vehicles. As a result, the drag coefficients computed with these methods might be not representative of the real performance of the car on the road and lead to an imprecise estimation of the fuel efficiency for internal combustion cars and range of electric car vehicles. The discrepancies between the reported values of fuel efficiency and/or range and those experienced by drivers might eventually erode the confidence of consumers in a car’s manufacturer. For this reason, it is important to estimate the vehicle’s drag as seen in real-world conditions. Effort in this direction is not new and different “wind averaged drag” have been proposed, especially in the truck industry, in the effort to most accurately reflects the performance of the vehicle in real world conditions. These wind averaged coefficients are obtained by weighting the vehicle’s drag under different yaw conditions. However, these wind averaged coefficients, being computed in idealized environments with low level of turbulence (both wind tunnels or CFD simulations) neglect the natural variability of the wind experienced on the road. High yaw angles are only possible in strong crosswind and are therefore more sensitive to the natural wind variability. This observation call into question the validity of wind averaged drag, which relies on unphysical test conditions with high yaw angle and low level of turbulence, to assess the vehicle’s performance. In this paper, we use transient aerodynamics simulations based on the Lattice Boltzmann Method (LBM) to perform simulations on a detailed SUV automobile model. Using a surface response method, the aerodynamic performance of the vehicle is fully and efficiently characterized for different yaw angles, turbulence intensities and turbulent length scales. Two different driving cycles are considered which are representative of city and highway driving conditions in a typical windy day. Unlike previous wind averaged drag calculations we include the effect of the natural wind variability and show that it significantly changes the aerodynamic performance of the vehicle and its fuel efficiency.