Download or read book Development of a Simulation based Platform for Autonomous Vehicle Algorithm Validation written by Rohan Bandopadhay Banerjee and published by . This book was released on 2019 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt: Developing robust algorithms for autonomous driving typically requires extensive validation and testing with physical hardware platforms and increasingly requires large amounts of diverse training data. The physical cost of these hardware platforms makes eld testing prohibitive, and the cost of collecting training data limits the size and diversity of this data. Autonomous driving simulation is a promising solution to address both of these challenges because it eliminates the need for a physical testing environment and because it oers environments that are congurable and diverse. However, most autonomous driving simulators are not fully useful for algorithm validation because they lack full integration with fundamental autonomous driving capabilities and because their sensor data is limited in functionality. In this work, we develop and present a simulation-based platform for testing and validation of autonomous driving algorithms that combines an open-source autonomous driving simulator (CARLA) with our existing autonomous driving codebase. Specically, we describe our software contributions to this platform, including simulated proprioceptive sensors and ground-truth LIDAR road information, and we demonstrate how we used the platform to validate both fundamental autonomous driving capabilities and a point-to-point navigation algorithm in simulation. We also describe how our platform was used to both develop and validate an approach to dynamic obstacle avoidance, a new capability in our codebase. Our platform is a capable tool for both validation and development of autonomous driving algorithms, although open directions remain in the areas of simulator sensor realism and runtime efficiency.

Download or read book Distributed Moving Base Driving Simulators written by Anders Andersson and published by Linköping University Electronic Press. This book was released on 2019-04-30 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: Development of new functionality and smart systems for different types of vehicles is accelerating with the advent of new emerging technologies such as connected and autonomous vehicles. To ensure that these new systems and functions work as intended, flexible and credible evaluation tools are necessary. One example of this type of tool is a driving simulator, which can be used for testing new and existing vehicle concepts and driver support systems. When a driver in a driving simulator operates it in the same way as they would in actual traffic, you get a realistic evaluation of what you want to investigate. Two advantages of a driving simulator are (1.) that you can repeat the same situation several times over a short period of time, and (2.) you can study driver reactions during dangerous situations that could result in serious injuries if they occurred in the real world. An important component of a driving simulator is the vehicle model, i.e., the model that describes how the vehicle reacts to its surroundings and driver inputs. To increase the simulator realism or the computational performance, it is possible to divide the vehicle model into subsystems that run on different computers that are connected in a network. A subsystem can also be replaced with hardware using so-called hardware-in-the-loop simulation, and can then be connected to the rest of the vehicle model using a specified interface. The technique of dividing a model into smaller subsystems running on separate nodes that communicate through a network is called distributed simulation. This thesis investigates if and how a distributed simulator design might facilitate the maintenance and new development required for a driving simulator to be able to keep up with the increasing pace of vehicle development. For this purpose, three different distributed simulator solutions have been designed, built, and analyzed with the aim of constructing distributed simulators, including external hardware, where the simulation achieves the same degree of realism as with a traditional driving simulator. One of these simulator solutions has been used to create a parameterized powertrain model that can be configured to represent any of a number of different vehicles. Furthermore, the driver's driving task is combined with the powertrain model to monitor deviations. After the powertrain model was created, subsystems from a simulator solution and the powertrain model have been transferred to a Modelica environment. The goal is to create a framework for requirement testing that guarantees sufficient realism, also for a distributed driving simulation. The results show that the distributed simulators we have developed work well overall with satisfactory performance. It is important to manage the vehicle model and how it is connected to a distributed system. In the distributed driveline simulator setup, the network delays were so small that they could be ignored, i.e., they did not affect the driving experience. However, if one gradually increases the delays, a driver in the distributed simulator will change his/her behavior. The impact of communication latency on a distributed simulator also depends on the simulator application, where different usages of the simulator, i.e., different simulator studies, will have different demands. We believe that many simulator studies could be performed using a distributed setup. One issue is how modifications to the system affect the vehicle model and the desired behavior. This leads to the need for methodology for managing model requirements. In order to detect model deviations in the simulator environment, a monitoring aid has been implemented to help notify test managers when a model behaves strangely or is driven outside of its validated region. Since the availability of distributed laboratory equipment can be limited, the possibility of using Modelica (which is an equation-based and object-oriented programming language) for simulating subsystems is also examined. Implementation of the model in Modelica has also been extended with requirements management, and in this work a framework is proposed for automatically evaluating the model in a tool.

Download or read book ADAS and Automated Driving written by Plato Pathrose and published by SAE International. This book was released on 2022-06-09 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: The day will soon come when you will be able to verbally communicate with a vehicle and instruct it to drive to a location. The car will navigate through street traffic and take you to your destination without additional instruction or effort on your part. Today, this scenario is still in the future, but the automotive industry is racing to toward the finish line to have automated driving vehicles deployed on our roads. ADAS and Automated Driving: A Practical Approach to Verification and Validation focuses on how automated driving systems (ADS) can be developed from concept to a product on the market for widescale public use. It covers practically viable approaches, methods, and techniques with examples from multiple production programs across different organizations. The author provides an overview of the various Advanced Driver Assistance Systems (ADAS) and ADS currently being developed and installed in vehicles. The technology needed for large-scale production and public use of fully autonomous vehicles is still under development, and the creation of such technology is a highly innovative area of the automotive industry. This text is a comprehensive reference for anyone interested in a career focused on the verification and validation of ADAS and ADS. The examples included in the volume provide the reader foundational knowledge and follow best and proven practices from the industry. Using the information in ADAS and Automated Driving, you can kick start your career in the field of ADAS and ADS.

Download or read book Development of an Autonomous Vehicle Platform written by Hamid Tahir and published by . This book was released on 2019 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous vehicles and their related development are gaining a lot of traction as a promising up and coming technology. The Mechatronics Vehicle Systems lab at the University of Waterloo is well pioneered in the automotive industry and seeks to apply their knowledge and skills to autonomous vehicles. Having an autonomous vehicle development platform at the University allows for development and testing of state of the art algorithms that can potentially benefit the entire automotive industry. An autonomous driving platform based on a Chevrolet Equinox is proposed in this thesis. Various types of sensors are installed on the vehicle and interfaced, allowing for full coverage of the surrounding environment. A software platform is developed which uses ROS and Matlab simultaneously, benefiting from the libraries, tools, and resources that come with both. The hardware platform is designed with simplicity and functionality in mind. Moreover, a simulation platform is used for testing various algorithms before real world implementation. Various types of sensor calibrations are necessary to fully synchronize all the sensors on the platform spatially. A joint calibration method that allows for the simultaneous calibration of all 3D sensors sharing a common field of view is implemented. Specialized hand-eye calibration methods to calibrate the GPS navigation system to the LIDAR and camera sensors are explored. Furthermore, vehicle to everything interfacing is kept in mind and a calibration technique is presented in order to localize infrastructure mounted sensors to a GPS navigation system. The calibration techniques are tested and areas of improvement are revealed. The developed platform is tested with the task of autonomous lane keeping. The steering wheel angle of the vehicle is controlled by the developed algorithm utilizing the camera and GPS navigation solution. The algorithm is tested in simulation with good results. Before real world testing, time synchronization between various devices on the platform, as well as testing of the actuators' controllers is performed. Finally, the lane keeping algorithm is tested on the developed platform on the University of Waterloo Ring Road. The system is able to autonomously steer around the majority of the road which is approximately a 2.5 km distance.

Download or read book Creating Autonomous Vehicle Systems written by Shaoshan Liu and published by Morgan & Claypool Publishers. This book was released on 2017-10-25 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first technical overview of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences of creating autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions about its actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, we are able to test new algorithms and update the HD map—plus, train better recognition, tracking, and decision models. This book consists of nine chapters. Chapter 1 provides an overview of autonomous vehicle systems; Chapter 2 focuses on localization technologies; Chapter 3 discusses traditional techniques used for perception; Chapter 4 discusses deep learning based techniques for perception; Chapter 5 introduces the planning and control sub-system, especially prediction and routing technologies; Chapter 6 focuses on motion planning and feedback control of the planning and control subsystem; Chapter 7 introduces reinforcement learning-based planning and control; Chapter 8 delves into the details of client systems design; and Chapter 9 provides the details of cloud platforms for autonomous driving. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find plenty of references for an effective, deeper exploration of the various technologies.

Download or read book Creating Autonomous Vehicle Systems Second Edition written by Liu Shaoshan and published by Springer Nature. This book was released on 2022-05-31 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is one of the first technical overviews of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences designing autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions as to its future actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, new algorithms can be tested so as to update the HD map—in addition to training better recognition, tracking, and decision models. Since the first edition of this book was released, many universities have adopted it in their autonomous driving classes, and the authors received many helpful comments and feedback from readers. Based on this, the second edition was improved by extending and rewriting multiple chapters and adding two commercial test case studies. In addition, a new section entitled “Teaching and Learning from this Book” was added to help instructors better utilize this book in their classes. The second edition captures the latest advances in autonomous driving and that it also presents usable real-world case studies to help readers better understand how to utilize their lessons in commercial autonomous driving projects. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find extensive references for an effective, deeper exploration of the various technologies.

Download or read book Creating Autonomous Vehicle Systems written by Liu Shaoshan and published by Springer Nature. This book was released on 2017-10-25 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first technical overview of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences of creating autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions about its actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, we are able to test new algorithms and update the HD map—plus, train better recognition, tracking, and decision models. This book consists of nine chapters. Chapter 1 provides an overview of autonomous vehicle systems; Chapter 2 focuses on localization technologies; Chapter 3 discusses traditional techniques used for perception; Chapter 4 discusses deep learning based techniques for perception; Chapter 5 introduces the planning and control sub-system, especially prediction and routing technologies; Chapter 6 focuses on motion planning and feedback control of the planning and control subsystem; Chapter 7 introduces reinforcement learning-based planning and control; Chapter 8 delves into the details of client systems design; and Chapter 9 provides the details of cloud platforms for autonomous driving. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find plenty of references for an effective, deeper exploration of the various technologies.

Download or read book Data Science written by Zhiwen Yu and published by Springer Nature. This book was released on 2023-09-14 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set (CCIS 1879 and 1880) constitutes the refereed proceedings of the 9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023 held in Harbin, China, during September 22–24, 2023. The 52 full papers and 14 short papers presented in these two volumes were carefully reviewed and selected from 244 submissions. The papers are organized in the following topical sections: Part I: Applications of Data Science, Big Data Management and Applications, Big Data Mining and Knowledge Management, Data Visualization, Data-driven Security, Infrastructure for Data Science, Machine Learning for Data Science and Multimedia Data Management and Analysis. Part II: Data-driven Healthcare, Data-driven Smart City/Planet, Social Media and Recommendation Systems and Education using big data, intelligent computing or data mining, etc.

Download or read book Trajectory Planning of an Autonomous Vehicle in Multi Vehicle Traffic Scenarios written by Mahdi Morsali and published by Linköping University Electronic Press. This book was released on 2021-03-25 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tremendous industrial and academic progress and investments have been made in au-tonomous driving, but still many aspects are unknown and require further investigation,development and testing. A key part of an autonomous driving system is an efficient plan-ning algorithm with potential to reduce accidents, or even unpleasant and stressful drivingexperience. A higher degree of automated planning also makes it possible to have a betterenergy management strategy with improved performance through analysis of surroundingenvironment of autonomous vehicles and taking action in a timely manner. This thesis deals with planning of autonomous vehicles in different urban scenarios, road,and vehicle conditions. The main concerns in designing the planning algorithms, are realtime capability, safety and comfort. The planning algorithms developed in this thesis aretested in simulation traffic situations with multiple moving vehicles as obstacles. The re-search conducted in this thesis falls mainly into two parts, the first part investigates decou-pled trajectory planning algorithms with a focus on speed planning, and the second sectionexplores different coupled planning algorithms in spatiotemporal environments where pathand speed are calculated simultaneously. Additionally, a behavioral analysis is carried outto evaluate different tactical maneuvers the autonomous vehicle can have considering theinitial states of the ego and surrounding vehicles. Particularly relevant for heavy duty vehicles, the issues addressed in designing a safe speedplanner in the first part are road conditions such as banking, friction, road curvature andvehicle characteristics. The vehicle constraints on acceleration, jerk, steering, steer ratelimitations and other safety limitations such as rollover are further considerations in speedplanning algorithms. For real time purposes, a minimum working roll model is identified us-ing roll angle and lateral acceleration data collected in a heavy duty truck. In the decoupledplanners, collision avoiding is treated using a search and optimization based planner. In an autonomous vehicle, the structure of the road network is known to the vehicle throughmapping applications. Therefore, this key property can be used in planning algorithms toincrease efficiency. The second part of the thesis, is focused on handling moving obstaclesin a spatiotemporal environment and collision-free planning in complex urban structures.Spatiotemporal planning holds the benefits of exhaustive search and has advantages com-pared to decoupled planning, but the search space in spatiotemporal planning is complex.Support vector machine is used to simplify the search problem to make it more efficient.A SVM classifies the surrounding obstacles into two categories and efficiently calculate anobstacle free region for the ego vehicle. The formulation achieved by solving SVM, con-tains information about the initial point, destination, stationary and moving obstacles.These features, combined with smoothness property of the Gaussian kernel used in SVMformulation is proven to be able to solve complex planning missions in a safe way. Here, three algorithms are developed by taking advantages of SVM formulation, a greedysearch algorithm, an A* lattice based planner and a geometrical based planner. One general property used in all three algorithms is reduced search space through using SVM. In A*lattice based planner, significant improvement in calculation time, is achieved by using theinformation from SVM formulation to calculate a heuristic for planning. Using this heuristic,the planning algorithm treats a simple driving scenario and a complex urban structureequal, as the structure of the road network is included in SVM solution. Inspired byobserving significant improvements in calculation time using SVM heuristic and combiningthe collision information from SVM surfaces and smoothness property, a geometrical planneris proposed that leads to further improvements in calculation time. Realistic driving scenarios such as roundabouts, intersections and takeover maneuvers areused, to test the performance of the proposed algorithms in simulation. Different roadconditions with large banking, low friction and high curvature, and vehicles prone to safetyissues, specially rollover, are evaluated to calculate the speed profile limits. The trajectoriesachieved by the proposed algorithms are compared to profiles calculated by optimal controlsolutions.

Download or read book Driving in Virtual Reality written by Björn Blissing and published by Linköping University Electronic Press. This book was released on 2020-09-02 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last decades, there has been a substantial increase in the development of complex active safety systems for automotive vehicles. These systems need to be tested for verification and validation to ensure that the system intervenes in the correct situations using the correct measures. There are multiple methods available to perform such testing. Software-in-the-loop and hardware-in-the-loop testing offer effective driverless testing. Other methods increase the fidelity by including human drivers, such as driving simulators and experiments performed at test tracks. This thesis examines vehicle-in-the-loop testing, an innovative method where the driver of a real vehicle wears a head-mounted display that displays virtual targets. This method combines the benefits of driving simulators with the benefits of using a real vehicle on a test track. Driving simulators offer repeatability, safety, and the possibility of complex interactions between actors. In contrast, the real vehicle provides the correct vehicle dynamics and motion feedback. There is a need to know how the technology behind the method might influence the results from vehicle-in-the-loop testing. Two techniques for vehicle-in-the-loop systems are studied. The first involves video-see through head-mounted displays, where the focus of the research is on the effects of visual latency on driving behavior. The results show that lateral driving behavior changes with added latency, but longitudinal behavior appears unaffected. The second system uses an opaque head-mounted display in an entirely virtual world. The research shows that this solution changes speed perception and results in a significant degradation in performance of tasks dependent on visual acuity. This research presents results that are relevant to consider when developing vehicle-in-the-loop platforms. The results are also applicable when choosing scenarios for this test method. Dagens fordon innehåller fler och fler säkerhetssystem. Vissa av dessa system ger varningar i potentiellt kritiska trafiksituationer. Det finns också mer komplexa system som tillfälligt kan ta kontroll över fordonet för att förhindra en olycka eller åtminstone mildra effekterna. Komplexiteten hos dessa system innebär att man måste genomföra omfattande tester. Både för att se att systemen reagerar vid rätt tidpunkt, men också för att se att valet av åtgärd är korrekt. Det finns många olika sätt att testa dessa system. Man börjar vanligtvis med simuleringar av programvara och hårdvara. Därefter kan systemet introduceras i ett fordon för att se vilka effekter systemet har när det interagerar med en riktig förare. Att utföra tester med förare ställer dock höga säkerhetskrav, och det är ofta svårt att samordna komplexa trafiksituationer på en testbana. Traditionellt har körsimulatorer varit ett naturligt alternativ eftersom de kan utföra komplexa scenarier i en säker miljö. Denna avhandling undersöker en testmetod där man utrustar föraren med en virtual reality-display. Genom att presentera omvärlden med hjälp av virtual reality, så kan man genomföra scenarion som tidigare varit omöjliga på en testbana. Det kan dock finnas inbyggda begränsningar i virtual reality tekniken som kan påverka körbeteendet. Det är därför viktigt att hitta och kvantifiera dessa effekter för att kunna lita på resultaten från testmetoden. Att känna till dessa effekter på körbeteendet dessutom kan hjälpa till att avgöra vilka typer av scenarier som är lämpade för denna testmetod. Det är också viktig information för att avgöra var man bör fokusera den tekniska utvecklingen av testutrustningen.

Download or read book Simulation Based Virtual Testing for Perceived Safety and Comfort of Advanced Driver Assistance Systems and Automated Driving Systems written by Harnarayan Singh and published by . This book was released on 2020 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) are ushering in a new era of transportation innovation and safety by incorporating technologies aimed at making the driving experience safer, more efficient, and comfortable. They assist in performing complex maneuvers, preempt potential risky situations, and take over the driver’s tasks in critical situations. Innovation acceptance research for ADAS show that the increasing demand for safety and comfort are the two key prime movers of ADAS market. Hence, there is a need to comprehensively test for both during the process of product verification and validation. Due to complexity of the system, cost of testing and safety of the test engineers, a significant part of ADAS/ADS algorithms validation needs to be done virtually. Although simulation-based validation and verification (V&V) is not new, the requirements of test descriptions and software tools are not yet well understood. This project builds around the process of simulation for testing by exposing ADAS/ADS software to pre-defined scenarios. Different scenarios are built in a series of virtual simulators which have unique features, methods and assumptions that must be well-understood for the results to be proven valid. These essential features of the simulators are documented to understand the effect of simulator specific scenario parameters on simulation results. For the perceived safety and comfort aspect of ADAS, objective assessment of the Lane Keep Assist feature is performed which involves a MATLAB®-based tool for giving a scalar rating to the performance of the Lane Keep Assist system. For a series of simulations, the essential drive quality parameters and the corresponding “goodness score” ratings of ADAS based on suitable metrics are used to train and develop a Machine learning algorithm that gives a quality assessment of the Lane Keep Assist system. Finally, a methodology is proposed that can be used to perform the same assessment experimentally, expanding the scope of the project. In general, the thesis is a guideline to developing simulation-based V&V tools for ADAS.

Download or read book Autonomous driving algorithms and Its IC Design written by Jianfeng Ren and published by Springer Nature. This book was released on 2023-08-09 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the rapid development of artificial intelligence and the emergence of various new sensors, autonomous driving has grown in popularity in recent years. The implementation of autonomous driving requires new sources of sensory data, such as cameras, radars, and lidars, and the algorithm processing requires a high degree of parallel computing. In this regard, traditional CPUs have insufficient computing power, while DSPs are good at image processing but lack sufficient performance for deep learning. Although GPUs are good at training, they are too “power-hungry,” which can affect vehicle performance. Therefore, this book looks to the future, arguing that custom ASICs are bound to become mainstream. With the goal of ICs design for autonomous driving, this book discusses the theory and engineering practice of designing future-oriented autonomous driving SoC chips. The content is divided into thirteen chapters, the first chapter mainly introduces readers to the current challenges and research directions in autonomous driving. Chapters 2–6 focus on algorithm design for perception and planning control. Chapters 7–10 address the optimization of deep learning models and the design of deep learning chips, while Chapters 11-12 cover automatic driving software architecture design. Chapter 13 discusses the 5G application on autonomous drving. This book is suitable for all undergraduates, graduate students, and engineering technicians who are interested in autonomous driving.

Download or read book Planning and Simulation for Autonomous Vehicles in Urban Traffic Scenarios written by Xinchen Li (Ph. D. in electrical engineering) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traffic accidents result in a high number of fatalities each year. This brings up the importance of developing Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS), due to their potential of increasing traffic safety by reducing vehicle crashes caused by driver errors. It could also be helpful to deploy the intelligent transportation systems (ITS) in different traffic scenarios to increase the efficiency of traffic flow and enlarge the traffic capacity. Planning and control of the autonomous vehicles, the two essential modules in autonomous driving, are still facing severe challenges in adapting to various traffic scenarios and complex environments. The planning and decision making of vehicles in urban traffic environment are still a big challenge for autonomous vehicles due to its complexity and uncertainties. Hence it is necessary to develop decision making and planning algorithms for vehicles in urban traffic, especially in intersections. Also, velocity profile planning for autonomous vehicles is also required based on various requirements according to the environment. Additionally, a convenient method for testing and validating the developed algorithms is also required. Hence a good simulation environment is important in the field of autonomous vehicles. This dissertation contributes to planning and decision making of autonomous vehicles in urban traffic scenarios as well as developing a way of generating realistic simulation environments as test beds to validate developed autonomous driving algorithms. Decision making methods and planning methods for autonomous shuttles and autonomous vehicles in urban traffic are proposed. A rule based decision maker working for last mile problem is introduced for an autonomous shuttle so that the autonomous shuttle can deal with typical traffic on designated routes. Then to deal with complex and uncertain urban traffic scenarios when the ego autonomous vehicles doesn’t have full observability over other vehicles’ states, a Partially Observable Markov Decision Making Process (POMDP) based decision making algorithm is proposed for solving the roundabout intersection planning problem with multiple vehicles involved. Moreover, a velocity planning method for autonomous shuttle in geo-fenced area is developed, such that passengers in the autonomous shuttle are safe and comfortable. In order to improve the performance of decision making algorithms, vehicle behavior and trajectory prediction methods are also studied. Sensor perception is an important part of the autonomous driving as the ego autonomous vehicle is detecting the environment and surrounding vehicles all the time. Noise is inevitable during the perception and some internal states of other vehicles are not detected. Hence, a Kalman filter based vehicle trajectory tracking is introduced to take care the measurement noise in the perception as well as to estimate the vehicle internal states. A change point detection based policy prediction method is also introduced for determining the most likely vehicle behavior given a series of observation data along the vehicle trajectory. Combining both methods, a vehicle trajectory prediction over a future period of time is also proposed. In addition, a method for developing simulation environment using real map data and 3D rendering based on a game engine is presented as a powerful tool for developing simulations for intelligent transportation systems. All the proposed methods are provided with simulation and test results to demonstrate the efficiency.

Download or read book Deep Learning for Autonomous Vehicle Control written by Sampo Kuutti and published by Springer Nature. This book was released on 2022-06-01 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: The next generation of autonomous vehicles will provide major improvements in traffic flow, fuel efficiency, and vehicle safety. Several challenges currently prevent the deployment of autonomous vehicles, one aspect of which is robust and adaptable vehicle control. Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalizing previously learned rules to new scenarios. For these reasons, the use of deep neural networks for vehicle control has gained significant interest. In this book, we introduce relevant deep learning techniques, discuss recent algorithms applied to autonomous vehicle control, identify strengths and limitations of available methods, discuss research challenges in the field, and provide insights into the future trends in this rapidly evolving field.

Download or read book Autonomous Vehicles for Public Transportation written by Călin Iclodean and published by Springer Nature. This book was released on 2022-11-26 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an interdisciplinary approach to autonomous driving technology design and development. It discusses a methodology of simulation that allows specialists to evaluate autonomous vehicle sensors functionality and integration, energy flow, efficiency, range, and service under public transport. The design, calibration, and physical model behind each autonomous vehicle sensor and component is explained. For each specific vehicle, the powertrain is analyzed, and output results are presented through the use of specific automotive industrial software (IPG CarMaker). The book gives the reader a clear perspective of the key factors influencing the global functionality of autonomous shuttle buses with respect to both their inner components the variable exterior factors and an exhaustive legal perspective in relation of their presence on public roads.

Download or read book Evaluation of Automated Driving in a Virtual Environment written by Griffin J. Leisenring and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automated vehicle testing in real-world driving scenarios is required for system development but may not always be able to be performed due to safety or other concerns. Testing scenarios can be replicated at a closed test track as an alternative to the real-world driving scenarios. While safer, the maneuvers performed may not be able to completely match those of the actual road. A simulated testing environment can be created to perform testing on a complete digital replica of the real-world roads. Scenes can be created to accurately match the real-world roads and surroundings. Simulated testing can be the first step in validating the automated driving functionality performance and safety for eventual implementation on a physical vehicle. The Ohio State University team in the Advanced Research Projects Agency - Energy (ARPA-E) Next-Generation Energy Technologies for Connected and Automated On-Road Vehicles (NEXTCAR) program is working on utilizing Level 4 automated driving technology to improve upon their energy saving and mobility benefits of a Plugin Hybrid Electric Vehicle (PHEV) platform. A test route representative of real-world driving scenarios is selected to measure the increased energy efficiency of the system. The automated driving platform has additional limitations that must be taken into consideration when selecting a route for testing. To reach the point at which a simulation can occur, the autonomous driving platform being used must be configured properly and a model scene must be created for simulated maneuvers to be performed. This thesis covers the initialization steps for the automated driving platform, the creation of a digital replica for a selected test route, and the validation of automated driving features in the simulated environment. Multiple open-source autonomous vehicle platforms exist for interested teams to work with the systems that enable autonomous driving. The Robot Operating System (ROS) is utilized by Autoware to allow for easier communication between the different automated driving sensors and modules. Open-source software provides clear information about the structure of the system and how modifications can be made to different modules to produce desired energy saving results. Once the automated driving platform is properly set up, simulations are run. ROSBag playback consists of replaying the Light Detection and Ranging (LiDAR) point scan values for a given time window and testing accuracy of point cloud maps through localization. The SVL Simulator is used to test automated driving functionality using a virtual vehicle in a custom-made virtual environment. Corresponding point cloud and high-definition maps were created to provide necessary road and positioning information to Autoware. Various tools including OpenStreetMap, MathWorks RoadRunner, and the Unity game engine ease the process of creating a virtual replica of the real-world test route. With localization possible, additional automated driving functions were performed using Autoware's mission and motion planning modules.

Download or read book Criticality Assessment of Simulation Based AV ADAS Test Scenarios written by Bo Shian Chen and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With a fast-paced growth of Automated Driving Systems (ADV) and Advanced Driver Assistance Systems (ADAS), simulation-based validation and verification (V&V) has become an essential way to validate the reliability of the safety algorithms and components before performing the field tests. Virtual driving scenarios typically consist of trajectories of surrounding agents, road geometry, environmental effects, lighting conditions, etc. It is necessary to identify the specific region in the scenario parameter space, that makes the scenario 'critical' such that the ADS features could play an essential role to help the driver avoid accidents. The criticality of the scenario could depend on multiple parameters, such as ego vehicle speed, vehicle dynamics, and other actor’s trajectories. However, the definition of criticality should be independent of the ADS controller or driver models. In this thesis, we propose a novel approach to compute criticality using concepts from optimal control which does not require driver models or any specific controller. The key concept is that the value function obtained from the optimal control solution is an indicator of relative ease in the maneuver and the probability of a safe result. The uniqueness of this concept is that the value function is an outcome of optimal ADS control, and it incorporates crash probability and difficulty of maneuver. Moreover, this approach incorporates modeling uncertainty and stochasticity in perception and localization. In this thesis we demonstrate the approach using three optimal control algorithms namely, dynamic programming (DP), Markov Decision Process iii (MDP), and Reinforcement Learning (RL). This approach has three key phases- 1) develop logical scenarios under several highway situations based on the real crash data, 2) develop an optimal control-based strategy to generate safety-critical simulation scenarios for autonomous vehicle obstacle avoidance maneuvers, and 3) extend the approach further to incorporate modeling uncertainties and calculate the crash probability or the value function. To better demonstrate the proposed approach, an obstacle avoidance driving scenario has been used as an example in this thesis.