Download or read book Efficient Decentralized Collaborative Perception for Autonomous Vehicles written by Maxime Chaveroche and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently, we have been witnesses of accidents involving autonomous vehicles and their lack of sufficient information at the right time. One way to tackle this issue is to benefit from the perception of different view points, namely collaborative perception. We propose here a decentralized collaboration, i.e. peer-to-peer, in which the agents are active in their quest for full perception by asking for specific areas in their surroundings on which they would like to know more. Ultimately, we want to optimize a trade-off between the maximization of knowledge about moving objects and the minimization of the total information received from others, to limit communication costs and message processing time. To this end, we chose to use Dempster-Shafer Theory (DST) in order to identify different types of uncertainties. In particular, DST allows us to distinguish what has never been perceived (out of range or occluded area) -- which is mainly what collaborative perception tries to reduce -- from what is debated among different sources (conflict arising from fusion of sensors or other vehicles perceptions). More generally, DST takes into account the specificity of evidence, meaning that it provides information about the reliability of an agent's belief, which is crucial for safety. DST also features the advantage of easily dealing with data incest with its Cautious fusion rule, which is a problem inherent to the decentralized approach. However, DST comes with high spatial and computational complexities, especially for dealing with data incest in fusion, which limits its usage to random experiments with few possible outcomes. Thus, we first proposed an efficient exact method to compute the decompositions needed for this Cautious fusion, exploiting what we called focal points. Then, we generalized this method to any Möbius transform in any partially ordered set (including all transformations in DST), we found ways to efficiently compute these focal points and we proposed a generalization of the decomposition required by the Cautious fusion. This generalized decomposition allows one to use this Cautious fusion in more cases, in particular cases where an agent has gathered very specific evidence. This enhances both accuracy and computational stability in consecutive fusions. However, algorithms naively based on our formulas would have a higher worst-case complexity than the complexity of the optimal general algorithms commonly employed in DST -- which is already more than exponential. Therefore, we later proposed algorithms with complexities always better than the state of the art, and more general, leveraging properties of distributive lattices. After this work on the fusion process itself, we tackled the issue of redundancy and irrelevance in decentralized collaborative perception. For this, we proposed a way to learn a communication policy that reverses the usual communication paradigm by only requesting from other vehicles what is unknown to the ego-vehicle, instead of filtering on the sender side. We tested three different models to be taken as base for a Deep Reinforcement Learning (DRL) algorithm and compared them to a broadcasting policy and a random policy. More precisely, we slightly modified a state-of-the-art generative model named Temporal Difference VAE (TD-VAE) to make it sequential. We named this variant Sequential TD-VAE (STD-VAE). We also proposed Locally Predictable VAE (LP-VAE), inspired by STD-VAE, designed to enhance its prediction capabilities. We showed that LP-VAE produced better belief states for prediction than STDVAE, both as a standalone model and in the context of DRL. The last model we tested was a simple state-less model (Convolutional VAE). Experiments were conducted in the driving simulator CARLA, with vehicles exchanging parts of semantic grid maps. Policies learned based on LP-VAE featured the best trade-off, as long as future rewards were taken into account.

Download or read book Learning to Drive written by David Michael Stavens and published by Stanford University. This book was released on 2011 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Every year, 1.2 million people die in automobile accidents and up to 50 million are injured. Many of these deaths are due to driver error and other preventable causes. Autonomous or highly aware cars have the potential to positively impact tens of millions of people. Building an autonomous car is not easy. Although the absolute number of traffic fatalities is tragically large, the failure rate of human driving is actually very small. A human driver makes a fatal mistake once in about 88 million miles. As a co-founding member of the Stanford Racing Team, we have built several relevant prototypes of autonomous cars. These include Stanley, the winner of the 2005 DARPA Grand Challenge and Junior, the car that took second place in the 2007 Urban Challenge. These prototypes demonstrate that autonomous vehicles can be successful in challenging environments. Nevertheless, reliable, cost-effective perception under uncertainty is a major challenge to the deployment of robotic cars in practice. This dissertation presents selected perception technologies for autonomous driving in the context of Stanford's autonomous cars. We consider speed selection in response to terrain conditions, smooth road finding, improved visual feature optimization, and cost effective car detection. Our work does not rely on manual engineering or even supervised machine learning. Rather, the car learns on its own, training itself without human teaching or labeling. We show this "self-supervised" learning often meets or exceeds traditional methods. Furthermore, we feel self-supervised learning is the only approach with the potential to provide the very low failure rates necessary to improve on human driving performance.

Download or read book Autonomous Driving Perception written by Rui Fan and published by Springer Nature. This book was released on 2023-10-06 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the captivating world of computer vision and deep learning for autonomous driving with our comprehensive and in-depth guide. Immerse yourself in an in-depth exploration of cutting-edge topics, carefully crafted to engage tertiary students and ignite the curiosity of researchers and professionals in the field. From fundamental principles to practical applications, this comprehensive guide offers a gentle introduction, expert evaluations of state-of-the-art methods, and inspiring research directions. With a broad range of topics covered, it is also an invaluable resource for university programs offering computer vision and deep learning courses. This book provides clear and simplified algorithm descriptions, making it easy for beginners to understand the complex concepts. We also include carefully selected problems and examples to help reinforce your learning. Don't miss out on this essential guide to computer vision and deep learning for autonomous driving.

Download or read book Human Like Decision Making and Control for Autonomous Driving written by Peng Hang and published by CRC Press. This book was released on 2022-07-25 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book details cutting-edge research into human-like driving technology, utilising game theory to better suit a human and machine hybrid driving environment. Covering feature identification and modelling of human driving behaviours, the book explains how to design an algorithm for decision making and control of autonomous vehicles in complex scenarios. Beginning with a review of current research in the field, the book uses this as a springboard from which to present a new theory of human-like driving framework for autonomous vehicles. Chapters cover system models of decision making and control, driving safety, riding comfort and travel efficiency. Throughout the book, game theory is applied to human-like decision making, enabling the autonomous vehicle and the human driver interaction to be modelled using noncooperative game theory approach. It also uses game theory to model collaborative decision making between connected autonomous vehicles. This framework enables human-like decision making and control of autonomous vehicles, which leads to safer and more efficient driving in complicated traffic scenarios. The book will be of interest to students and professionals alike, in the field of automotive engineering, computer engineering and control engineering.

Download or read book Multi sensor Fusion for Autonomous Driving written by Xinyu Zhang and published by Springer Nature. This book was released on with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Uncertainty aware Spatiotemporal Perception for Autonomous Vehicles written by Mikhal Itkina and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous vehicles are set to revolutionize transportation in terms of safety and efficiency. However, autonomous systems still have challenges operating in complex human environments, such as an autonomous vehicle in a cluttered, dynamic urban setting. A key obstacle to deploying autonomous systems on the road is understanding, anticipating, and making inferences about human behaviors. Autonomous perception builds a general understanding of the environment for a robot. This includes making inferences about human behaviors in both space and time. Humans are difficult to model due to their vastly diverse behaviors and rapidly evolving objectives. Moreover, in cluttered settings, there are computational and visibility limitations. However, humans also possess desirable capabilities, such as their ability to generalize beyond their observed environment. Although learning-based systems have had success in recent years in modeling and imitating human behavior, efficiently capturing the data and model uncertainty for these systems remains an open problem. This thesis proposes algorithmic advances to uncertainty-aware autonomous perception systems in human environments. We make system-level contributions to spatiotemporal robot perception that reasons about human behavior, and foundational advancements in uncertainty-aware machine learning models for trajectory prediction. These contributions enable robotic systems to make uncertainty- and socially-aware spatiotemporal inferences about human behavior. Traditional robot perception is object-centric and modular, consisting of object detection, tracking, and trajectory prediction stages. These systems can fail prior to the prediction stage due to partial occlusions in the environment. We thus propose an alternative end-to-end paradigm for spatiotemporal environment prediction from a map-centric occupancy grid representation. Occupancy grids are robust to partial occlusions, can handle an arbitrary number of human agents in the scene, and do not require a priori information regarding the environment. We investigate the performance of computer vision techniques in this context and develop new mechanisms tailored to the task of spatiotemporal environment prediction. Spatially, robots also need to reason about fully occluded agents in their environment, which may occur due to sensor limitations or other agents on the road obstructing the field of view. Humans excel at extrapolating from their experiences by making inferences from observed social behaviors. We draw inspiration from human intuition to fill in portions of the robot's map that are not observable by traditional sensors. We infer occupancy in these occluded regions by learning a multimodal mapping from observed human driver behaviors to the environment ahead of them, thus treating people as sensors. Our system handles multiple observed agents to maximally inform the occupancy map around the robot. In order to safely integrate human behavior modeling into the robot autonomy stack, the perception system must efficiently account for uncertainty. Human behavior is often modeled using discrete latent spaces in learning-based models to capture the multimodality in the distribution. For example, in a trajectory prediction task, there may be multiple valid future predictions given a past trajectory. To accurately model this latent distribution, the latent space needs to be sufficiently large, leading to tractability concerns for downstream tasks, such as path planning. We address this issue by proposing a sparsification algorithm for discrete latent sample spaces that can be applied post hoc without sacrificing model performance. Our approach successfully balances multimodality and sparsity to achieve efficient data uncertainty estimation. Aside from modeling data uncertainty, learning-based autonomous systems must be aware of their model uncertainty or what they do not know. Flagging out-of-distribution or unknown scenarios encountered in the real world could be helpful to downstream autonomy stack components and to engineers for further system development. Although the machine learning community has been prolific in model uncertainty estimation for small benchmark problems, relatively little work has been done on estimating this uncertainty in complex, learning-based robotic systems. We propose efficiently learning the model uncertainty over an interpretable, low-dimensional latent space in the context of a trajectory prediction task. The algorithms presented in this thesis were validated on real-world autonomous driving data and baselined against state-of-the-art techniques. We show that drawing inspiration from human-level reasoning while modeling the associated uncertainty can inform environment understanding for autonomous perception systems. The contributions made in this thesis are a step towards uncertainty- and socially-aware autonomous systems that can function seamlessly in human environments.

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 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 Collaborative Perception Localization and Mapping for Autonomous Systems written by Yufeng Yue and published by Springer Nature. This book was released on 2020-11-13 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the breakthrough and cutting-edge progress for collaborative perception and mapping by proposing a novel framework of multimodal perception-relative localization–collaborative mapping for collaborative robot systems. The organization of the book allows the readers to analyze, model and design collaborative perception technology for autonomous robots. It presents the basic foundation in the field of collaborative robot systems and the fundamental theory and technical guidelines for collaborative perception and mapping. The book significantly promotes the development of autonomous systems from individual intelligence to collaborative intelligence by providing extensive simulations and real experiments results in the different chapters. This book caters to engineers, graduate students and researchers in the fields of autonomous systems, robotics, computer vision and collaborative perception.

Download or read book Learning to Drive written by David Michael Stavens and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Every year, 1.2 million people die in automobile accidents and up to 50 million are injured. Many of these deaths are due to driver error and other preventable causes. Autonomous or highly aware cars have the potential to positively impact tens of millions of people. Building an autonomous car is not easy. Although the absolute number of traffic fatalities is tragically large, the failure rate of human driving is actually very small. A human driver makes a fatal mistake once in about 88 million miles. As a co-founding member of the Stanford Racing Team, we have built several relevant prototypes of autonomous cars. These include Stanley, the winner of the 2005 DARPA Grand Challenge and Junior, the car that took second place in the 2007 Urban Challenge. These prototypes demonstrate that autonomous vehicles can be successful in challenging environments. Nevertheless, reliable, cost-effective perception under uncertainty is a major challenge to the deployment of robotic cars in practice. This dissertation presents selected perception technologies for autonomous driving in the context of Stanford's autonomous cars. We consider speed selection in response to terrain conditions, smooth road finding, improved visual feature optimization, and cost effective car detection. Our work does not rely on manual engineering or even supervised machine learning. Rather, the car learns on its own, training itself without human teaching or labeling. We show this "self-supervised" learning often meets or exceeds traditional methods. Furthermore, we feel self-supervised learning is the only approach with the potential to provide the very low failure rates necessary to improve on human driving performance.

Download or read book Cooperative Perception and Use of Connectivity in Automated Driving written by Mustafa Ridvan Cantas and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent developments in connected and autonomous vehicles (CAV) improve traffic safety and fuel efficiency and take away the driving burden partially or completely from the driver. CAVs are improving the traffic safety using their on-board sensors such as camera, lidar, radar and ultrasonic sensors. While these sensors are effective in sensing the objects in their field of view, CAVs can also sense other road users by utilizing communication modems, and learn more about the traffic patterns such as the signal phase and timing (SPaT) information of a traffic light at an intersection. One recent approach to boost capabilities of CAVs is the sharing of perceived target detections with other road users. This practice significantly increases the situational awareness of connected road users. Since the cooperative perception concept is still in early stages, the development of use case scenarios and capabilities of this concept are still active research areas. Therefore, a Cooperative Perception (CP) architecture and CAV functionalities are developed in this research to improve the traffic safety, fuel economy, and ride comfort. Their effectiveness is demonstrated with use case scenarios. The developed CP architecture relies on Joint Probability Data Association (JPDA) multi object tracking algorithm to track detected objects and create CP messages. Then, with the simulations, it is shown that situational awareness of the road users increased significantly, thereby improving their traffic safety. Later, another use case scenario for CP is developed to improve Green Light Optimized Speed Advisory (GLOSA). In this use case, the vehicle not only relies on SPaT and MAP messages but also relies on the shared CP messages by a smart intersection. As a result, two different algorithms are developed to utilize infrastructure CP messages. While the first approach to generate speed advisory was to create a rule-based solution, the second approach utilizes a Deep Deterministic Policy Gradient (DDPG) reinforcement learning agent to control the vehicle. The developed approaches showed promising fuel efficiency and ride comfort advantages. Finally, as part of the CAV functionalities, lateral and longitudinal controllers are designed to aid the driver whenever needed. While the designed lateral controller has lane centering and path following functionalities, the designed cooperative adaptive cruise control reduces time gap between the ego vehicle and the target vehicle to being followed utilize roads more efficiently and improve fuel economy for platoons. The designed CAV subsystems can be used as standalone functionalities to improve safety, efficiency and comfort of the passengers or they can be used as an enabling part of a highly automated vehicles.

Download or read book Multi agent Collaborative Perception for Autonomous Driving Unsettled Aspects written by Guang Chen and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: SAE EDGE Research Reports provide state-of-the-art and state-of-industry examinations of the most significant topics in mobility engineering. SAE EDGE contributors are experts from research, academia, and industry who have come together to explore and define the most critical advancements, challenges, and future direction in areas such as vehicle automation, unmanned aircraft, IoT and connectivity, cybersecurity, advanced propulsion, and advanced manufacturing.

Download or read book Proceedings of 3rd 2023 International Conference on Autonomous Unmanned Systems 3rd ICAUS 2023 written by Yi Qu and published by Springer Nature. This book was released on with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Autonomous Vehicle Groups in Urban Traffic written by Jana Görmer-Redding and published by Cuvillier Verlag. This book was released on 2018-08-08 with total page 468 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is likely that autonomous vehicles will be the future of mobility. To handle the increase in autonomy, traffic coordination methods will become indispensable. Based on this, an investigation into the performance of Autonomous Vehicle Group Formation (AVGF) based on a decentralized model and a simulative evaluation in urban environments is needed. An Autonomous Vehicle Group (AVG) is a set of vehicles used for transporting people or goods, such as a car, truck, or bus, that are located, gathered, or classed together and are characterized by constant change or progress within the traffic system. The focus is on decentralized autonomous vehicle grouping, which allows the flexibility of single vehicles to be retained while also enabling the use of group coordination to achieve higher throughput in urban networks, as already witnessed in highway vehicular platoons. A known and practiced concept for urban traffic control at traffic signals is to bundle vehicles passively according to green signal phases; the novelty being active coordination of the vehicles in decentralized groups of interests. Likewise, AVGs make coordinated decisions with and without communication depending on the similarities of their vehicle properties and destinations. AVGs coordinate the motion of traffic, making strategic (i.e., group destination) and tactical (i.e., speed and gaps) group decisions in a street network.

Download or read book Decentralized Control for Formation of Autonomous Vehicles written by Viswanath Musti and published by . This book was released on 2010 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Autonomous Vehicles Applications and Perspectives written by Petar Piljek and published by BoD – Books on Demand. This book was released on 2023-09-27 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent times, remarkable progress has taken place in the field of autonomous vehicles, reshaping industries such as logistics, transportation, defense, and more. The quest for achieving fully autonomous systems has been a thrilling yet demanding journey, as researchers and engineers continually push the limits of technological ingenuity. Autonomous Vehicles - Applications and Perspectives delves into the field of autonomous vehicles across eight chapters that cover various facets of this domain. The book is organized into four sections: "Introduction", "Autonomous Vehicles Enabling Technologies", "Autonomous Vehicles Applications and Potentials", and "Challenges and Perspectives". Its main goal is to provide an informative resource for those interested in autonomous vehicles, inspiring progress and discussions for researchers, students, and professionals alike.