Abstract
Environmental perception plays an essential role in autonomous driving tasks and demands robustness in cluttered dynamic environments such as complex urban scenarios. In this paper, a robust Multiple Object Detection and Tracking (MODT) algorithm for a non-stationary base is presented, using multiple 3D LiDARs for perception. The merged LiDAR data is treated with an efficient MODT framework, considering the limitations of the vehicle-embedded computing environment. The ground classification is obtained through a grid-based method while considering a non-planar ground. Furthermore, unlike prior works, 3D grid-based clustering technique is developed to detect objects under elevated structures. The centroid measurements obtained from the object detection are tracked using Interactive Multiple Model-Unscented Kalman Filter-Joint Probabilistic Data Association Filter (IMM-UKF-JPDAF). IMM captures different motion patterns, UKF handles the nonlinearities of motion models, and JPDAF associates the measurements in the presence of clutter. The proposed algorithm is implemented on two slightly dissimilar platforms, giving real-time performance on embedded computers. The performance evaluation metrics by MOT16 and ground truths provided by KITTI Datasets are used for evaluations and comparison with the state-of-the-art. The experimentation on platforms and comparisons with state-of-the-art techniques suggest that the proposed framework is a feasible solution for MODT tasks.
Highlights
Automated driving systems capable of performing all driving tasks under all roadway and environmental conditions manageable by a human counterpart, is classified as the highest level of automation by the Society of Automotive Engineers (SAE) International [1]
MATLAB wrapper is offered by KITTI Dataset to extract tracking information from the XML files to perform the metrics evaluations
Light Detector and Ranging (LiDAR)-based Multiple Object Detection and Tracking (MODT) approaches are relatively scarce in literature compared to camera- based ones
Summary
Automated driving systems capable of performing all driving tasks under all roadway and environmental conditions manageable by a human counterpart, is classified as the highest level of automation by the Society of Automotive Engineers (SAE) International [1]. Driving Assistants (ADAs) are commercially available, they either require human intervention or operate only under specific environmental conditions. The realization of the said autonomy has put forth huge requirements on the associated research domains, including Multiple Object Detection and Tracking (MODT). Over the past decade numerous MODT approaches have been studied, traditionally using cameras for perception. Objects are detected in the camera reference frame either in a 2D coordinate system, or in a 3D coordinate system under a stereo setup, producing 2D or 3D trajectories, respectively. Panoramic camera-based tracking is yet to be investigated
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