Study on Feasible Planner for Automated Driving Personal Vehicle

芝浦工業大学2021年

Data Processing for Perception of Autonomous Vehicles in Urban Traffic Environments

The perception system of AVs, consisting of various onboard sensors including cameras and LiDAR scanners, is crucial to perceiving the environment, localizing the vehicle, and recognizing the semantics of traffic scenes. Despite recent advances in computer vision, the perception of AVs has remained challenging, especially in urban environments. One of the reasons is that multiple types of dynamic agents usually coexist in an urban area. The interactions between agents and surroundings are complicated to explicitly model, and the agents' unpredictable behaviors also increase the problem complexity. Moreover, ground truth data with a rich set of labels is not sufficient to cover diverse scenarios, and it is challenging to get data from real scenes. This dissertation presents research contributions to overcome the challenges of AV perception in urban traffic environments, from data collection and labeling to 3D reconstruction and analysis of intrinsic properties. Mainly focusing on unsignalized urban intersections, we discuss (1) how to obtain valuable data from real urban traffic scenes, (2) how to efficiently process the raw data to produce meaningful labels for the dynamic road agents, (3) how to augment the data with semantic labels to the scenes, and (4) what factors make the reconstruction more realistic. We first built a data capture system with a multi-modal sensor suite to simulate actual AV perception. We then introduced a 3D model-fitting algorithm to fit parametrized human mesh models to the pedestrians in a scene. The generated 3D models provide free labels, such as human pose and trajectories, with no cost of manual labeling. We proposed performing the entire scene modeling through densely reconstructing the scene and expanding the scope of automatic labeling to scene elements. These include dynamic vehicles and static components, such as roads, buildings, and traffic signs. To do this, we built a simulator that can generate a rich set of labels using virtual sensors. Finally, we tackled the problem of estimating intrinsic properties and discuss ways to achieve realistic 3D reconstruction. This dissertation understands the AV perception pipeline, explores data preparations at urban traffic scenes, and discusses relevant experiments and applications critical for tackling other problems. We conclude the dissertation with future research directions for further augmenting the data and improving the realism of the reconstructed scene models.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169648/1/wonhui_1.pd

Robotics 2010

Without a doubt, robotics has made an incredible progress over the last decades. The vision of developing, designing and creating technical systems that help humans to achieve hard and complex tasks, has intelligently led to an incredible variety of solutions. There are barely technical fields that could exhibit more interdisciplinary interconnections like robotics. This fact is generated by highly complex challenges imposed by robotic systems, especially the requirement on intelligent and autonomous operation. This book tries to give an insight into the evolutionary process that takes place in robotics. It provides articles covering a wide range of this exciting area. The progress of technical challenges and concepts may illuminate the relationship between developments that seem to be completely different at first sight. The robotics remains an exciting scientific and engineering field. The community looks optimistically ahead and also looks forward for the future challenges and new development