Pedestrian Flow Estimation Using Sparse Observation for Autonomous Vehicles One of the major challenges that autonomous cars are facing today is the unpredictability of pedestrian movement in urban environments. Since pedestrian data acquired by vehicles are sparse observed a pedestrian flow directed graph is proposed to understand pedestrian behavior. In this work, an autonomous electric vehicle is employed to gather LiDAR and camera data. Pedestrian tracking information and semantic information from the environment are used with a probabilistic approach to create the graph. In order to refine the graph a set of outlier removal techniques are described. The graphbased pedestrian flow shows an increase of 61.29 % of coverage zone, and the outlier removal approach successfully removed 81 % of the edges.

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.