  Trajectory generation and prediction are two interwoven tasks that play
important roles in planner evaluation and decision making for intelligent
vehicles. Most existing methods focus on one of the two and are optimized to
directly output the final generated/predicted trajectories, which only contain
limited information for critical scenario augmentation and safe planning. In
this work, we propose a novel behavior-aware Trajectory Autoencoder (TAE) that
explicitly models drivers' behavior such as aggressiveness and intention in the
latent space, using semi-supervised adversarial autoencoder and domain
knowledge in transportation. Our model addresses trajectory generation and
prediction in a unified architecture and benefits both tasks: the model can
generate diverse, controllable and realistic trajectories to enhance planner
optimization in safety-critical and long-tailed scenarios, and it can provide
prediction of critical behavior in addition to the final trajectories for
decision making. Experimental results demonstrate that our method achieves
promising performance on both trajectory generation and prediction.
