  Target detection and tracking provides crucial information for motion
planning and decision making in autonomous driving. This paper proposes an
online multi-object tracking (MOT) framework with tracking-by-detection for
maneuvering vehicles under motion uncertainty in dynamic road context. We
employ a point cloud based vehicle detector to provide real-time 3D bounding
boxes of detected vehicles and conduct the online bipartite optimization of the
maneuver-orientated data association between the detections and the targets.
Kalman Filter (KF) is adopted as the backbone for multi-object tracking. In
order to entertain the maneuvering uncertainty, we leverage the interacting
multiple model (IMM) approach to obtain the \textit{a-posterior} residual as
the cost for each association hypothesis, which is calculated with the hybrid
model posterior (after mode-switch). Road context is integrated to conduct
adjustments of the time varying transition probability matrix (TPM) of the IMM
to regulate the maneuvers according to road segments and traffic sign/signals,
with which the data association is performed in a unified spatial-temporal
fashion. Experiments show our framework is able to effectively track multiple
vehicles with maneuvers subject to dynamic road context and localization drift.
