Investigation and Implementation of Available Software and Algorithms for Autonomous Vehicle Development

The purpose of this project is to integrate available algorithms for automated driving from open source software into a simulation to demonstrate how the software can be used to quickly prototype control systems for automated vehicles. Autoware is the primary software program that will be tested which processes incoming sensing data for automated driving functions resulting in control inputs for the vehicle such as speed and steering control to respond to the various driving situations. Most of these programs for automated driving research are built based on Robot Operating System (ROS) which is an open source meta-operating system specifically designed to be used for robot software development. Testing of Autoware was completed with ROS visualization simulation tools in addition to a software program called LGSVL Simulator based on the Unity game engine which was used to develop a variety of testing scenarios. Once the software integration was completed, performance of the different algorithms was evaluated using LGSVL simulator and scenarios built in Unity. The primary goal of the project was to uncover the functionality of open source autonomous vehicle software platforms and develop realistic simulation scenarios for the testing of the algorithms.No embargoAcademic Major: Mechanical Engineerin

리모트 랩: 원격 자율 주행 애플리케이션을 위한 테스트 플랫폼

학위논문(석사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2021.8. 이창건.실제 자율주행차 연구는 가장 단순한 자율주행차 플랫폼조차도 높은 기 준제조단가로 인해 자동차 산업 대기업(예: 현대, 테슬라)이나 자동차 하 드웨어 제조업체(예: NVIDIA, Bosch)와 관련 연구자들이 아니고선 종종 접근할 수 없습니다. 원격랩의 주요 기능은 자율주행 분야에서 초급 또는 중간 수준의 지식만 가질 수 있는 사용자에게 초점을 맞춰 원격 실험 플랫폼에 일반 대중이 접근할 수 있도록 하는 것입니다. 본 연구에서는 Remote Lab의 아키텍 처, 다양한 기능 및 Remote Lab 개발과 관련된 문제에 대한 개요를 제 공합니다. 추가로 플랫폼에 활용되는 기본 로컬라이제이션 알고리듬인 NDT (Normal Distributions Transform) 매칭에 대한 심층 분석은 일관되고 강력한 로컬라이제이션과 관련된 알고리듬의 약점을 논의합니다.Real-world autonomous vehicle research is often inaccessible to researchers outside of those related to automotive industry giants (i.e. Hyundai, Tesla) or automotive hardware manufacturers (i.e. NVIDIA, Bosch) due to the high baseline costs of creating even the simplest autonomous vehicle platform. Remote Lab’s primary function is to provide access to a remote experimentation platform to the general public, with a focus on users who may only have a beginner or intermediate level of knowledge in the field of autonomous driving. This work presents Remote Lab’s architecture, its various features, and an overview of issues relating to the development of Remote Lab. Additionally, an in-depth analysis of the primary localization algorithm utilized on the platform, Normal Distributions Transform (NDT) matching, discusses the algorithm’s weaknesses with respect to consistent and robust localization.Chapter 1. Introduction 1 1.1. OSCAR Platform and Remote Lab 1 1.2. Remote Lab's Motivation & Goals 2 1.3. Organization 3 Chapter 2. Remote Lab Features 4 2.1. Architecture Overview 4 2.2. Remote Lab User Flow 5 2.3. Reservation Request 7 2.4. Code Editor 9 2.5. Monitoring Interface & System 12 2.6. Default Docker Image 12 2.7. OSCAR Scaled Car 16 Chapter 3. Remote Lab Development Challenges 18 3.1. NDT Matching Localization Inconsistency 18 3.1.1 Normal Distributions Transform (NDT) 18 3.1.2. NDT Matching Localization Experiments 19 3.1.3. Experiment Conclusion 24 Chapter 4. Conclusion and Future Work 26 4.1. Conclusion 26 4.2. Future Work 26 Bibliography 27 Abstract 28석

Autonomous navigation for mobility scooters: a complete framework based on open-source software

In recent years, there has been a growing demand for small vehicles targeted at users with mobility restrictions and designed to operate on pedestrian areas. The users of these vehicles are generally required to be in control for the entire duration of their journey, but a lot more people could benefit from them if some of the driving tasks could be automated. In this scenario, we set out to develop an autonomous mobility scooter, with the aim to understand the commercial feasibility of a similar product. This paper reports on the progress of this project, proposing a framework for autonomous navigation on pedestrian areas, and focusing in particular on the construction of suitable costmaps. The proposed framework is based on open-source software, including a library created by the authors for the generation of costmaps

ADBench: benchmarking autonomous driving systems

Driven by the improvements in a variety of domains, autonomous driving is becoming a reality and today, industry aims at moving toward fully autonomous vehicles. High-tech chip manufacturers are designing high-performance and energy-efficient platforms in accordance with safety standard requirements. However, the software used to implement advanced functionalities in autonomous vehicles challenges real-time constraints on those platforms. Hence, there is a clear need for industry-level autonomous driving benchmarks to evaluate platforms and systems. In this paper, we propose ADBench, a benchmarking approach and benchmark suite for state-of-the-art autonomous driving platforms, in accordance with the key modules, structural design and functions of AD systems, building on several industry-level autonomous driving systems. The use of standard benchmarks facilitates the design, verification and validation process of autonomous systems.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under Grant TIN2015-65316-P, the SuPerCom European Research Council (ERC) project under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 772773), and the HiPEAC Network of Excellence.Peer ReviewedPostprint (author's final draft

Pre-Deployment Testing of Low Speed, Urban Road Autonomous Driving in a Simulated Environment

Low speed autonomous shuttles emulating SAE Level L4 automated driving using human driver assisted autonomy have been operating in geo-fenced areas in several cities in the US and the rest of the world. These autonomous vehicles (AV) are operated by small to mid-sized technology companies that do not have the resources of automotive OEMs for carrying out exhaustive, comprehensive testing of their AV technology solutions before public road deployment. Due to the low speed of operation and hence not operating on roads containing highways, the base vehicles of these AV shuttles are not required to go through rigorous certification tests. The way the driver assisted AV technology is tested and allowed for public road deployment is continuously evolving but is not standardized and shows differences between the different states where these vehicles operate. Currently, AVs and AV shuttles deployed on public roads are using these deployments for testing and improving their technology. However, this is not the right approach. Safe and extensive testing in a lab and controlled test environment including Model-in-the-Loop (MiL), Hardware-in-the-Loop (HiL) and Autonomous-Vehicle-in-the-Loop (AViL) testing should be the prerequisite to such public road deployments. This paper presents three dimensional virtual modeling of an AV shuttle deployment site and simulation testing in this virtual environment. We have two deployment sites in Columbus of these AV shuttles through the Department of Transportation funded Smart City Challenge project named Smart Columbus. The Linden residential area AV shuttle deployment site of Smart Columbus is used as the specific example for illustrating the AV testing method proposed in this paper

AVstack: An Open-Source, Reconfigurable Platform for Autonomous Vehicle Development

Pioneers of autonomous vehicles (AVs) promised to revolutionize the driving experience and driving safety. However, milestones in AVs have materialized slower than forecast. Two culprits are (1) the lack of verifiability of proposed state-of-the-art AV components, and (2) stagnation of pursuing next-level evaluations, e.g., vehicle-to-infrastructure (V2I) and multi-agent collaboration. In part, progress has been hampered by: the large volume of software in AVs, the multiple disparate conventions, the difficulty of testing across datasets and simulators, and the inflexibility of state-of-the-art AV components. To address these challenges, we present AVstack, an open-source, reconfigurable software platform for AV design, implementation, test, and analysis. AVstack solves the validation problem by enabling first-of-a-kind trade studies on datasets and physics-based simulators. AVstack solves the stagnation problem as a reconfigurable AV platform built on dozens of open-source AV components in a high-level programming language. We demonstrate the power of AVstack through longitudinal testing across multiple benchmark datasets and V2I-collaboration case studies that explore trade-offs of designing multi-sensor, multi-agent algorithms

Simulation environment for testing of planning algorithms

Tato diplomová práce si klade za cíl vytvořit simulační prostředí pro testování plánovacích algoritmů v systému ROS, které umožňuje výzkumníkům ověřit své plánovací algoritmy pro autonomní vozidla. Práce se věnuje rozboru různých zdrojů dat a hodnotí, zda jsou data vhodná pro testování algoritmů. Součástí práce je sada scénářů obsahujících anotace silnic, trajektorie vozidel, a cílů pro autonomní vozidlo. Tyto scénáře jsou vytvořeny na základě záznamů skutečné dopravy. Součástí práce je rovněž simulátor, který je schopen spouštět, hodnotit a vykreslit chování plánovacích algoritmů v různých dopravních situacích. K jeho ověření byl implementován vlastní plánovací algoritmus, s jehož pomocí lze doložit nejen funkčnost simulačního programu, ale i přiblížit čtenáři práci s testovací sadou dat a příslušnými knihovnami.This diploma thesis seeks to create an environment for benchmarking of path planning algorithms for autonomous driving in ROS. The aim is to give researchers the means to validate the performance of their algorithms. We analyze different sources of data to determine, whether they are suitable for the benchmarking. Using this knowledge, we create a set of benchmarks containing vehicle trajectories, road descriptions, and goals for the planning algorithm, all extracted from real traffic. We also provide a simulator able to run multiple planning algorithms, evaluate them and visualize their performance. Furthermore, we provide a planning algorithm to demonstrate the benchmarking process and its outputs, and to provide insight on how to work with the benchmark data set

Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World

We present a new approach to automated scenario-based testing of the safety of autonomous vehicles, especially those using advanced artificial intelligence-based components, spanning both simulation-based evaluation as well as testing in the real world. Our approach is based on formal methods, combining formal specification of scenarios and safety properties, algorithmic test case generation using formal simulation, test case selection for track testing, executing test cases on the track, and analyzing the resulting data. Experiments with a real autonomous vehicle at an industrial testing facility support our hypotheses that (i) formal simulation can be effective at identifying test cases to run on the track, and (ii) the gap between simulated and real worlds can be systematically evaluated and bridged.Comment: 9 pages, 6 figures. Full version of an ITSC 2020 pape

OpenPodcar: An Open Source Vehicle for Self-Driving Car Research

OpenPodcar is a low-cost, open source hardware and software, autonomous vehicle research platform based on an off-the-shelf, hard-canopy, mobility scooter donor vehicle. Hardware and software build instructions are provided to convert the donor vehicle into a low-cost and fully autonomous platform. The open platform consists of (a) hardware components: CAD designs, bill of materials, and build instructions; (b) Arduino, ROS and Gazebo control and simulation software files which provide standard ROS interfaces and simulation of the vehicle; and (c) higher-level ROS software implementations and configurations of standard robot autonomous planning and control, including the move\_base interface with Timed-Elastic-Band planner which enacts commands to drive the vehicle from a current to a desired pose around obstacles. The vehicle is large enough to transport a human passenger or similar load at speeds up to 15km/h, for example for use as a last-mile autonomous taxi service or to transport delivery containers similarly around a city center. It is small and safe enough to be parked in a standard research lab and be used for realistic human-vehicle interaction studies. System build cost from new components is around USD7,000 in total in 2022. OpenPodcar thus provides a good balance between real world utility, safety, cost and research convenience