<div><p>Analyzing behaviors of objects on the road is a complex task that requires data from various sensors and their fusion to recreate movement of objects with a high degree of accuracy. A data collection and processing system are thus needed to track the objects accurately in order to make an accurate and clear map of the trajectories of objects relative to various coordinate frame(s) of interest in the map. Detection and tracking moving objects (DATMO) and Simultaneous localization and mapping (SLAM) are the tasks that needs to be achieved in conjunction to create a clear map of the road comprising of the moving and static objects.</p> <p> These computational problems are commonly solved and used to aid scenario reconstruction for the objects of interest. The tracking of objects can be done in various ways, utilizing sensors such as monocular or stereo cameras, Light Detection and Ranging (LIDAR) sensors as well as Inertial Navigation systems (INS) systems. One relatively common method for solving DATMO and SLAM involves utilizing a 3D LIDAR with multiple monocular cameras in conjunction with an inertial measurement unit (IMU) allows for redundancies to maintain object classification and tracking with the help of sensor fusion in cases when sensor specific traditional algorithms prove to be ineffectual when either sensor falls short due to their limitations. The usage of the IMU and sensor fusion methods relatively eliminates the need for having an expensive INS rig. Fusion of these sensors allows for more effectual tracking to utilize the maximum potential of each sensor while allowing for methods to increase perceptional accuracy. </p> <p>The focus of this thesis will be the dock-less e-scooter and the primary goal will be to track its movements effectively and accurately with respect to cars on the road and the world. Since it is relatively more common to observe a car on the road than e-scooters, we propose a data collection system that can be built on top of an e-scooter and an offline processing pipeline that can be used to collect data in order to understand the behaviors of the e-scooters themselves. In this thesis, we plan to explore a data collection system involving a 3D LIDAR sensor and multiple monocular cameras and an IMU on an e-scooter as well as an offline method for processing the data to generate data to aid scenario reconstruction. </p><br></div>

lt has been shown that engagement in secondary tasks may contribute to cyclists crash risk [1 ], meditated by cycling errors or risky behaviors. For influences on secondary task: engagement, it is generally found that phone use is negatively correlated with age. In most studies, males are more found engaged in phone tasks than females. lt was also found that users of a bicycle-sharing program more often to wear headphones and engage in more unsafe behavior. The use of safety gear (e.g., wearing a helmet, using reflectors) is often negatively correlated with distracted cycling. Also, cyclists engaged in a secondary task exhibit other risky behaviors more often [2]. The present study's first aim was to get (an updated) estimate of the observable frequency of different secondary tasks, use of additional safety equipment, and rule violations while riding bicycles and e-scooters in Germany. The second aim was to examine possible differences in secondary task: engagement, use of additional safety equipment, and rule violations between different types of users of the cycling infrastructure, i.e., riders of conventional bikes, e-bikes, scooters, and e-scooters. A third aim was to explore whether riders' secondary task engagement is related to active safety precautions (e.g., wearing a helmet), traffic rule violations, and at-fault conflicts and if there are rider profiles regarding safety-related behaviors. As the study is explorative, no hypotheses were formulated. [From: Introduction]