  Accurate and robust tracking of surrounding road participants plays an
important role in autonomous driving. However, there is usually no prior
knowledge of the number of tracking targets due to object emergence, object
disappearance and false alarms. To overcome this challenge, we propose a
generic vehicle tracking framework based on modified mixture particle filter,
which can make the number of tracking targets adaptive to real-time
observations and track all the vehicles within sensor range simultaneously in a
uniform architecture without explicit data association. Each object corresponds
to a mixture component whose distribution is non-parametric and approximated by
particle hypotheses. Most tracking approaches employ vehicle kinematic models
as the prediction model. However, it is hard for these models to make proper
predictions when sensor measurements are lost or become low quality due to
partial or complete occlusions. Moreover, these models are incapable of
forecasting sudden maneuvers. To address these problems, we propose to
incorporate learning-based behavioral models instead of pure vehicle kinematic
models to realize prediction in the prior update of recursive Bayesian state
estimation. Two typical driving scenarios including lane keeping and lane
change are demonstrated to verify the effectiveness and accuracy of the
proposed framework as well as the advantages of employing learning-based
models.
