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Accurate and robust tracking of surrounding road participants plays animportant role in autonomous driving. However, there is usually no priorknowledge of the number of tracking targets due to object emergence, objectdisappearance and false alarms. To overcome this challenge, we propose ageneric vehicle tracking framework based on modified mixture particle filter,which can make the number of tracking targets adaptive to real-timeobservations and track all the vehicles within sensor range simultaneously in auniform architecture without explicit data association. Each object correspondsto a mixture component whose distribution is non-parametric and approximated byparticle hypotheses. Most tracking approaches employ vehicle kinematic modelsas the prediction model. However, it is hard for these models to make properpredictions when sensor measurements are lost or become low quality due topartial or complete occlusions. Moreover, these models are incapable offorecasting sudden maneuvers. To address these problems, we propose toincorporate learning-based behavioral models instead of pure vehicle kinematicmodels to realize prediction in the prior update of recursive Bayesian stateestimation. Two typical driving scenarios including lane keeping and lanechange are demonstrated to verify the effectiveness and accuracy of theproposed framework as well as the advantages of employing learning-basedmodels.

Generic Vehicle Tracking Framework Capable of Handling Occlusions Based on Modified Mixture Particle Filter