The high similarities of different real-world vehicles and great diversities of the acquisition views pose grand challenges to vehicle re-identification (ReID), which traditionally maps the vehicle images into a high-dimensional embedding space for distance optimization, vehicle discrimination, and identification. To improve the discriminative capability and robustness of the ReID algorithm, we propose a novel end-to-end embedding adversarial learning network (EALN) that is capable of generating samples localized in the embedding space. Instead of selecting abundant hard negatives from the training set, which is extremely difficult if not impossible, with our embedding adversarial learning scheme, the automatically generated hard negative samples in the specified embedding space can greatly improve the capability of the network for discriminating similar vehicles. Moreover, the more challenging cross-view vehicle ReID problem, which requires the ReID algorithm to be robust with different query views, can also benefit from such a scheme based on the artificially generated cross-view samples. We demonstrate the promise of EALN through extensive experiments and show the effectiveness of hard negative and cross-view generation in facilitating vehicle ReID based on the comparisons with the state-of-the-art schemes.