Multipleobject Tracking based on Monocular Camera and 3D Lidar Fusion for Autonomous Vehicles This article describes a multiobject tracking method through sensor fusion with a monocular camera and a 3D Lidar for autonomous vehicles. Specifically, several pairwise costs from information, such as locations, movements, and poses of 3D cues, are designed for tracking. These costs can complement each other to reduce matching errors during the tracking process. Moreover, they are efficient to be online computed with embedded equipment. We feed the pairwise costs to the dataassociation framework, which is based on the Hungarian algorithm, and then do the backend fusion for the tracking results. The experimental results on our autonomous sightseeing car demonstrate that our tracking method could achieve accurate and robust results in realworld traffic scenarios.

MultiObject Tracking of 3D Cuboids Using Aggregated Features the unknown correspondences of measurements and targets, referred to as data association, is one of the main challenges of multitarget tracking. Each new measurement received could be the continuation of some previously detected target, the first detection of a new target or a false alarm. Tracking 3D cuboids, is particularly difficult due to the high amount of data, which can include erroneous or noisy information coming from sensors, that can lead to false measurements, detections from an unknown number of objects which may not be consistent over frames or varying object properties like dimension and orientation. In the selfdriving car context, the target tracking module holds an important role due to the fact that the ego vehicle has to make predictions regarding the position and velocity of the surrounding objects in the next time epoch, plan for actions and make the correct decisions. To tackle the above mentioned problems and other issues coming from the selfdriving car processing pipeline we propose three original contributions: 1) designing a novel affinity measurement function to associate measurements and targets using multiple types of features coming from LIDAR and camera, 2) a context aware descriptor for 3D objects that improves the data association process, 3) a framework that includes a module for tracking dimensions and orientation of objects. The implemented solution runs in real time and experiments that were performed on real world urban scenarios prove that the presented method is effective and robust even in a highly dynamic environment.