  Scene information plays a crucial role in trajectory forecasting systems for
autonomous driving by providing semantic clues and constraints on potential
future paths of traffic agents. Prevalent trajectory prediction techniques
often take high-definition maps (HD maps) as part of the inputs to provide
scene knowledge. Although HD maps offer accurate road information, they may
suffer from the high cost of annotation or restrictions of law that limits
their widespread use. Therefore, those methods are still expected to generate
reliable prediction results in mapless scenarios. In this paper, we tackle the
problem of improving the consistency of multi-modal prediction trajectories and
the real road topology when map information is unavailable during the test
phase. Specifically, we achieve this by training a map-based prediction teacher
network on the annotated samples and transferring the knowledge to a student
mapless prediction network using a two-fold knowledge distillation framework.
Our solution is generalizable for common trajectory prediction networks and
does not bring extra computation burden. Experimental results show that our
method stably improves prediction performance in mapless mode on many widely
used state-of-the-art trajectory prediction baselines, compensating for the
gaps caused by the absence of HD maps. Qualitative visualization results
demonstrate that our approach helps infer unseen map information.
