Browsing by Subject "LiDAR"

Now showing 1 - 6 of 6
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    Determination of Sensors Characteristics of Curb and Development of Surrogate Curb for the Evaluation of Vehicle Active Safety Systems
    (2020-05) Pandey, Seeta Ram; Chien, Yung-Ping Chien; Chen, Yaobin; King, Brian
    Over the years, car driving experience has evolved drastically. Many new and useful technologies have emerged, which have enhanced safety and reliability measures. The Automotive world is now trying to build capabilities for driverless or vehicle assisted driving. Building capabilities for driverless cars practically means first developing training methods, then training the machine, evaluating the test results, and then based on testing results; develop a confidence interval for trusting the machine. One of the critical models is the model adopting the Road Departure Assisting Techniques (RDAT). These techniques are primarily the standards for alleviating the risk of roadside fatalities. The different models developed or proposed for RDAT falls under “The Road Departure Mitigation System” (RDMS). But, almost every RDMS to date has over-reliance on the presence and the quality of the lane markings. In the absence of lane markings or of proper lane markings, these RDMS are unreliable. Therefore, RDMS requires new references such as roadside objects and road edges for detecting road departures. This new system should propose and establish a standard for RDMS testing with roadside objects. As the foremost task, this new system requires the creation of a testing environment consisting of soft, robust, and reusable surrogates. Critically, these surrogates must have comparable sensors characteristics to those of real roadside objects from various commonly used object detection sensors on the vehicles such as camera, radar, and LIDAR. One of such everyday roadside objects is the curbs. For developing a surrogate for the curb, the first step is to recognize what the roadside objects should look like concerning different sensors, and the next step is to design and develop a surrogate curb that successfully follows the properties of the real roadside objects. This thesis first demonstrates and proposes the methods for extracting the color, Radar reflectivity, and the LiDAR reflectance properties of real roadside curbs. That is, the study deals with what all color combinations and patterns represent the US roadside curbs, what should be the range of Radar reflectivity values, and LiDAR reflectance bounds that a surrogate curb should satisfy. The later part of the thesis illustrates methods and steps on how to mimic the extracted properties, design a surrogate curb as per federal standards, and then develop a surrogate curb. Finally, the surrogate curbs were subjected to crash tests for testing their robustness.
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    Leveraging the NEON Airborne Observation Platform for socio-environmental systems research
    (Wiley, 2021) Ordway, Elsa M.; Elmore, Andrew J.; Kolstoe, Sonja; Quinn, John E.; Swanwick, Rachel; Cattau, Megan; Taillie, Dylan; Guinn, Steven M.; Chadwick, K. Dana; Atkins, Jeff W.; Blake, Rachael E.; Chapman, Melissa; Cobourn, Kelly; Goulden, Tristan; Helmus, Matthew R.; Hondula, Kelly; Hritz, Carrie; Jensen, Jennifer; Julian, Jason P.; Kuwayama, Yusuke; Lulla, Vijay; O’Leary, Donal; Nelson, Donald R.; Ocón, Jonathan P.; Pau, Stephanie; Ponce-Campos, Guillermo E.; Portillo-Quintero, Carlos; Pricope, Narcisa G.; Rivero, Rosanna G.; Schneider, Laura; Steele, Meredith; Tulbure, Mirela G.; Williamson, Matthew A.; Wilson, Cyril; Geography, School of Liberal Arts
    During the 21st century, human–environment interactions will increasingly expose both systems to risks, but also yield opportunities for improvement as we gain insight into these complex, coupled systems. Human–environment interactions operate over multiple spatial and temporal scales, requiring large data volumes of multi-resolution information for analysis. Climate change, land-use change, urbanization, and wildfires, for example, can affect regions differently depending on ecological and socioeconomic structures. The relative scarcity of data on both humans and natural systems at the relevant extent can be prohibitive when pursuing inquiries into these complex relationships. We explore the value of multitemporal, high-density, and high-resolution LiDAR, imaging spectroscopy, and digital camera data from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) for Socio-Environmental Systems (SES) research. In addition to providing an overview of NEON AOP datasets and outlining specific applications for addressing SES questions, we highlight current challenges and provide recommendations for the SES research community to improve and expand its use of this platform for SES research. The coordinated, nationwide AOP remote sensing data, collected annually over the next 30 yr, offer exciting opportunities for cross-site analyses and comparison, upscaling metrics derived from LiDAR and hyperspectral datasets across larger spatial extents, and addressing questions across diverse scales. Integrating AOP data with other SES datasets will allow researchers to investigate complex systems and provide urgently needed policy recommendations for socio-environmental challenges. We urge the SES research community to further explore questions and theories in social and economic disciplines that might leverage NEON AOP data.
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    A Multi Sensor Real-time Tracking with LiDAR and Camera
    (IEEE, 2020-01) Kollazhi Manghat, Surya; El-Sharkawy, Mohamed; Electrical and Computer Engineering, School of Engineering and Technology
    Self driving cars are equipped with various driver-assistive technologies (ADAS) like Forward Collision Warning system (FCW), Adaptive Cruise Control and Collision Mitigation by Breaking (CMbB) to ensure safety. Tracking plays an important role in ADAS systems for understanding dynamic environment. This paper proposes 3D multi-target tracking method by following a lean way of implementation using object detection with aim of real time. Object Tracking is an integral part of environment sensing, which enables the vehicle to estimate the surrounding object's trajectories to accomplish motion planning. The advancement in the object detection methodologies benefits greatly when following the tracking by detection approach. The proposed method implemented 2D tracking on camera data and 3D tracking on LiDAR point cloud data. The estimated state from each sensors are fused together to come with a more optimal state of objects present in the surrounding. The multi object tracking performance has evaluated on publicly available KITTI dataset.
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    Predicting locations for urban tree planting
    (2014) King, Steven M.; Johnson, Daniel P. (Daniel Patrick), 1971-; Bein, Frederick L. (Frederick Louis), 1943-; Lulla, Vijay O.
    The purpose of this study was to locate the most suitable blocks to plant trees within Indianapolis, Indiana’s Near Eastside Community (NESCO). LiDAR data were utilized, with 1.0 meter average post spacing, captured by the Indiana Statewide Imagery and LiDAR Program from March 13, 2011 to April 30, 2012, to conduct a covertype classification and identify blocks that have low canopies, high impervious surfaces and high surface temperatures. Tree plantings in these blocks can help mitigate the effects of the urban heat island effect. Using 2010 U.S. Census demographic data and the principal component analysis, block groups with high social vulnerability were determined, and tree plantings in these locations could help reduce mortality from extreme heat events. This study also determined high and low priority plantable space in order to emphasize plantable spaces with the potential to shade buildings; this can reduce cooling costs and the urban heat island, and it can maximize the potential of each planted tree.
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    RADAR Modeling For Autonomous Vehicle Simulation Environment using Open Source
    (2022-05) Kesury, Tayabali Akhtar; Anwar, Sohel; Tovar, Andres; Li, Lingxi
    Advancement in modern technology has brought with it an advent of increased interest in self-driving. The rapid growth in interest has caused a surge in the development of autonomous vehicles which in turn brought with itself a few challenges. To overcome these new challenges, automotive companies are forced to invest heavily in the research and development of autonomous vehicles. To overcome this challenge, simulations are a great tool in any arsenal that’s inclined towards making progress towards a self-driving autonomous future. There is a massive growth in the amount of computing power in today’s world and with the help of the same computing power, simulations will help test and simulate scenarios to have real time results. However, the challenge does not end here, there is a much bigger hurdle caused by the growing complexities of modelling a complete simulation environment. This thesis focuses on providing a solution for modelling a RADAR sensor for a simulation environment. This research presents a RADAR modeling technique suitable for autonomous vehicle simulation environment using open-source utilities. This study proposes to customize an onboard LiDAR model to the specification of a desired RADAR field of view, resolution, and range and then utilizes a density-based clustering algorithm to generate the RADAR output on an open-source graphical engine such as Unreal Engine (UE). High fidelity RADAR models have recently been developed for proprietary simulation platforms such as MATLAB under its automated driving toolbox. However, open-source RADAR models for open-source simulation platform such as UE are not available. This research focuses on developing a RADAR model on UE using blueprint visual scripting for off-road vehicles. The model discussed in the thesis uses 3D pointcloud data generated from the simulation environment and then clipping the data according to the FOV of the RADAR specification, it clusters the points generated from an object using DBSCAN. The model gives the distance and azimuth to the object from the RADAR sensor in 2D. This model offers the developers a base to build upon and help them develop and test autonomous control algorithms requiring RADAR sensor data. Preliminary simulation results show promise for the proposed RADAR model.
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    The Spatial Relationship Between Septic System Failure and Environmental Factors in Washington Township, Marion County, Indiana
    (2019-04) Hanson, Brian L.; Johnson, Daniel P.; Lulla, Vijay; Bein, Frederick L.
    Underground septic systems thrive or fail based on the relationship with their local environment. This paper explores ways environmental variables such as soil type, tree roots, degree of slope, and impervious surfaces affect on-site wastewater treatment systems. It also discusses the effects each of these variables may have on a septic system, and the resulting impact a compromised system may have on the surrounding environment. This research focuses on an approximately 20 square mile area of central Washington Township in Marion County, Indiana. This area of central Indiana contains a large septic system owning population in a sampling of different environments such as wooded areas, hilly areas, and a variety of different soil types.