Automotive perception involves understanding the external driving environment and the
internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving …

Environment perception, one of the most fundamental and challenging problems of
autonomous vehicles (AVs), has been widely studied in recent decades. Due to inferior fault …

Multiobject tracking provides situational awareness that enables new applications for
modern convenience, public safety, and homeland security. This paper presents a factor …

Grid map** is a well-established approach for environment perception in robotic and
automotive applications. Early work suggests estimating the occupancy state of each grid …

This paper presents two trajectory Poisson multi-Bernoulli (TPMB) filters for multi-target
tracking: one to estimate the set of alive trajectories at each time step and another to …

This tutorial introduces state-of-the-art methods for tracking multiple spatially extended
objects based on unlabeled noisy point clouds, eg, from radar or lidar sensors. In the first …

This paper proposes a new adaptive framework for tracking multiple objects in the presence
of data association uncertainty and heavy clutter, either with or without knowledge of the …

We propose a computationally efficient, the first so far, multi-sensor extension of the Poisson
multi-Bernoulli mixture (PMBM) filter that accommodates both centralized and distributed …

Online 3D multi-object tracking (MOT) has recently received significant research interests
due to the expanding demand of 3D perception in advanced driver assistance systems …

Multiple hypothesis tracking addresses difficult multiple target tracking problems by making
association decisions using multiple scans or frames of data. This paper reviews forty years …