Multi-Sensor Data Fusion for Robust Environment Reconstruction in Autonomous Vehicle Applications

In autonomous vehicle systems, understanding the surrounding environment is mandatory for an intelligent vehicle to make every decision of movement on the road. Knowledge about the neighboring environment enables the vehicle to detect moving objects, especially irregular events such as jaywalking, sudden lane change of the vehicle etc. to avoid collision. This local situation awareness mostly depends on the advanced sensors (e.g. camera, LIDAR, RADAR) added to the vehicle. The main focus of this work is to formulate a problem of reconstructing the vehicle environment using point cloud data from the LIDAR and RGB color images from the camera. Based on a widely used point cloud registration tool such as iterated closest point (ICP), an expectation-maximization (EM)-ICP technique has been proposed to automatically mosaic multiple point cloud sets into a larger one. Motion trajectories of the moving objects are analyzed to address the issue of irregularity detection. Another contribution of this work is the utilization of fusion of color information (from RGB color images captured by the camera) with the three-dimensional point cloud data for better representation of the environment. For better understanding of the surrounding environment, histogram of oriented gradient (HOG) based techniques are exploited to detect pedestrians and vehicles.;Using both camera and LIDAR, an autonomous vehicle can gather information and reconstruct the map of the surrounding environment up to a certain distance. Capability of communicating and cooperating among vehicles can improve the automated driving decisions by providing extended and more precise view of the surroundings. In this work, a transmission power control algorithm is studied along with the adaptive content control algorithm to achieve a more accurate map of the vehicle environment. To exchange the local sensor data among the vehicles, an adaptive communication scheme is proposed that controls the lengths and the contents of the messages depending on the load of the communication channel. The exchange of this information can extend the tracking region of a vehicle beyond the area sensed by its own sensors. In this experiment, a combined effect of power control, and message length and content control algorithm is exploited to improve the map\u27s accuracy of the surroundings in a cooperative automated vehicle system

The discrete dynamics of small-scale spatial events: agent-based models of mobility in carnivals and street parades

Small-scale spatial events are situations in which elements or objects vary in such away that temporal dynamics is intrinsic to their representation and explanation. Someof the clearest examples involve local movement from conventional traffic modelingto disaster evacuation where congestion, crowding, panic, and related safety issue arekey features of such events. We propose that such events can be simulated using newvariants of pedestrian model, which embody ideas about how behavior emerges fromthe accumulated interactions between small-scale objects. We present a model inwhich the event space is first explored by agents using ?swarm intelligence?. Armedwith information about the space, agents then move in an unobstructed fashion to theevent. Congestion and problems over safety are then resolved through introducingcontrols in an iterative fashion and rerunning the model until a ?safe solution? isreached. The model has been developed to simulate the effect of changing the route ofthe Notting Hill Carnival, an annual event held in west central London over 2 days inAugust each year. One of the key issues in using such simulation is how the processof modeling interacts with those who manage and control the event. As such, thischanges the nature of the modeling problem from one where control and optimizationis external to the model to one where this is intrinsic to the simulation

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

Time-evolving measures and macroscopic modeling of pedestrian flow

This paper deals with the early results of a new model of pedestrian flow, conceived within a measure-theoretical framework. The modeling approach consists in a discrete-time Eulerian macroscopic representation of the system via a family of measures which, pushed forward by some motion mappings, provide an estimate of the space occupancy by pedestrians at successive time steps. From the modeling point of view, this setting is particularly suitable to treat nonlocal interactions among pedestrians, obstacles, and wall boundary conditions. In addition, analysis and numerical approximation of the resulting mathematical structures, which is the main target of this work, follow more easily and straightforwardly than in case of standard hyperbolic conservation laws, also used in the specialized literature by some Authors to address analogous problems.Comment: 27 pages, 6 figures -- Accepted for publication in Arch. Ration. Mech. Anal., 201