By exploiting aerodynamic interactions among road vehicles, driving-automation technologies have the potential to reduce energy use and emissions. Traditional platooning concepts consider multiple vehicles travelling in close longitudinal proximity within the same road lane, but recent research suggests that lateral offsetting and adjacent-lane positioning can provide additional benefits in the complex traffic-and-wind climate experienced in everyday driving. On-road aerodynamic-state estimation will be a critical feedback metric for such autonomous-vehicle systems. A small sample of multi-vehicle wind-tunnel results is used to introduce a concept for using surface-pressure differences to infer aerodynamic states, and measurements from on-road in-traffic tests highlight ways to differentiate between wake effects from leading vehicles and close-proximity pressure-field influences from adjacent-lane vehicles. Complex conditions and varied positions of other vehicles in traffic highlight the need to track temporal changes in states, and to use multiple metrics to predict aerodynamic states.