Determination of Sensors Characteristics of Curb and Development of Surrogate Curb for the Evaluation of Vehicle Active Safety Systems
type: CreativeWork
description: 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 re…
abstract: 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 re…
author: Pandey, Seeta Ram
language: en_US
keywords: RDMS, LKA, CAMERA, COLOR, RADAR, REFLECTIVITY, LiDAR, REFLECTANCE, SURROGATE CURBS
date published: 2020-05
date modified: 2026-07-24T02:40:46Z
provider: IUPUI
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: IUPUI

PropertyValue
property ID: Handle
value: https://hdl.handle.net/1805/22278

PropertyValue
property ID: DOI
value: 10.7912/C2/2569