  In the simulation-based testing and evaluation of autonomous vehicles (AVs),
how background vehicles (BVs) drive directly influences the AV's driving
behavior and further impacts the testing result. Existing simulation platforms
use either pre-determined trajectories or deterministic driving models to model
the BVs' behaviors. However, pre-determined BV trajectories can not react to
the AV's maneuvers, and deterministic models are different from real human
drivers due to the lack of stochastic components and errors. Both methods lead
to unrealistic traffic scenarios. This paper presents a learning-based
stochastic driving model that meets the unique needs of AV testing, i.e.
interactive and human-like. The model is built based on the
long-short-term-memory (LSTM) architecture. By incorporating the concept of
quantile-regression to the loss function of the model, the stochastic behaviors
are reproduced without any prior assumption of human drivers. The model is
trained with the large-scale naturalistic driving data (NDD) from the Safety
Pilot Model Deployment(SPMD) project and then compared with a stochastic
intelligent driving model (IDM). Analysis of individual trajectories shows that
the proposed model can reproduce more similar trajectories to human drivers
than IDM. To validate the ability of the proposed model in generating a
naturalistic driving environment, traffic simulation experiments are
implemented. The results show that the traffic flow parameters such as speed,
range, and headway distribution match closely with the NDD, which is of
significant importance for AV testing and evaluation.
