Vehicle re-identification (Re-ID) is fundamentally challenging due to the difficulties in data labeling, visual domain mismatch between datasets and diverse appearance of the same vehicle. We propose the adaptive feature learning technique based on the space-time prior to address these issues. The idea is demonstrated effectively in both the human Re-ID and the vehicle Re-ID tasks. We train a vehicle feature extractor in a multi-task learning manner on three existing vehicle datasets and fine-tune the feature extractor with the adaptive feature learning technique on the target domain. We then develop a vehicle Re-ID system based on the learned vehicle feature extractor. Finally, our meticulous system design leads to the second place in the 2018 NVIDIA AI City Challenge Track 3.