We introduce activity-based navigation, which uses human activities derived from sensor data to help people navigate, in particular to retrace a ―trail‖ previously taken by that person or another person. Such trails may include step counts, walking up/down stairs or taking elevators, compass directions, and photos taken along a user‘s path, in addition to absolute positioning (GPS and maps) when available. To explore the user experience of activity-based navigation, we built Greenfield, a mobile device interface for finding a car. We conducted a ten participant user study comparing users‘ ability to find cars across three different presentations of activity-based information as well as verbal instructions. Our results show that activity-based navigation can be used for car finding and suggest its promise more generally for supporting navigation tasks. We present lessons for future activity-based navigation interfaces, and motivate further work in this space, particularly in the area of robust activity inference.