This work gives an outlook on the potential of automated driving functions (ADFs) to reduce real-world CO
2 and pollutant emissions for heavy-duty powertrains. Up to now, ADF research mainly focuses on increased traffic safety, driver comfort, and road capacity. Studies on emissions are lacking. By taking the driver out-of-the-loop, cycle-to-cycle variability is removed and energy losses and large accelerations can be significantly reduced. This enhances emission performance robustness, which will allow for more fuel-efficient engine settings. A general, optimal control framework is introduced, which integrates ADF with energy and emission management. Based on predictions of the vehicle power demand and emissions, a desired vehicle velocity profile, which minimizes the overall vehicle energy consumption, is determined. In this approach, real-world tailpipe emissions are explicitly taken into account. This opens the route to emission trading on vehicle or even, on platoon, fleet, and traffic level. For the combined ADF and powertrain development, testing, and certification, various opportunities are presented to fully exploit the synergy between these systems and to reduce development time and costs. By equipping vehicles with an emission monitoring system, real-world data of the ADF emission reduction potential becomes available. As validated traffic and component aging models are lacking, this data is also valuable for realistic scenario development and uncertainty modeling in virtual or mixed testing. This will lead to improved robustness evaluation and performance.