  Understanding the probabilistic traffic environment is a vital challenge for
the motion planning of autonomous vehicles. To make feasible control decisions,
forecasting future trajectories of adjacent cars is essential for intelligent
vehicles to assess potential conflicts and react to reduce the risk. This paper
first introduces a Bayesian Long Short-term Memory (BLSTM) model to learn human
drivers' behaviors and habits from their historical trajectory data. The model
predicts the probability distribution of surrounding vehicles' positions, which
are used to estimate dynamic conflict risks. Next, a hybrid automaton is built
to model the basic motions of a car, and the conflict risks are assessed for
real-time state-space transitions based on environmental information. Finally,
a BLSTM-based Model Predictive Control (MPC) is built to navigate vehicles
through safe paths with the least predicted conflict risk. By merging BLSTM
with MPC, the designed neural-based MPC overcomes the defect that traditional
MPC is hard to model uncertain conflict risks. The simulation results show that
our proposed BLSTM-based MPC performs better than human drivers because it can
foresee potential conflicts and take action to avoid them.

Robotics;Systems and Control;Systems and Control