Download or read book Environmental Perception for Automated Vehicles written by Jae Bum Choi and published by . This book was released on 2016-05-25 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust Environmental Perception and Reliability Control for Intelligent Vehicles written by Huihui Pan and published by Springer Nature. This book was released on 2023-11-25 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the most recent state-of-the-art algorithms on robust environmental perception and reliability control for intelligent vehicle systems. By integrating object detection, semantic segmentation, trajectory prediction, multi-object tracking, multi-sensor fusion, and reliability control in a systematic way, this book is aimed at guaranteeing that intelligent vehicles can run safely in complex road traffic scenes. Adopts the multi-sensor data fusion-based neural networks to environmental perception fault tolerance algorithms, solving the problem of perception reliability when some sensors fail by using data redundancy. Presents the camera-based monocular approach to implement the robust perception tasks, which introduces sequential feature association and depth hint augmentation, and introduces seven adaptive methods. Proposes efficient and robust semantic segmentation of traffic scenes through real-time deep dual-resolution networks and representation separation of vision transformers. Focuses on trajectory prediction and proposes phased and progressive trajectory prediction methods that is more consistent with human psychological characteristics, which is able to take both social interactions and personal intentions into account. Puts forward methods based on conditional random field and multi-task segmentation learning to solve the robust multi-object tracking problem for environment perception in autonomous vehicle scenarios. Presents the novel reliability control strategies of intelligent vehicles to optimize the dynamic tracking performance and investigates the completely unknown autonomous vehicle tracking issues with actuator faults.

Download or read book Environmental Perception Technology for Unmanned Systems written by Xin Bi and published by Springer Nature. This book was released on 2020-09-30 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the principles and technology of environmental perception in unmanned systems. With the rapid development of a new generation of information technologies such as automatic control and information perception, a new generation of robots and unmanned systems will also take on new importance. This book first reviews the development of autonomous systems and subsequently introduces readers to the technical characteristics and main technologies of the sensor. Lastly, it addresses aspects including autonomous path planning, intelligent perception and autonomous control technology under uncertain conditions. For the first time, the book systematically introduces the core technology of autonomous system information perception.

Download or read book Robust Environmental Perception and Reliability Control for Intelligent Vehicles written by Huihui Pan (Of Haerbin gong ye da xue) and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book presents the most recent state-of-the-art algorithms on robust environmental perception and reliability control for intelligent vehicle systems. By integrating object detection, semantic segmentation, trajectory prediction, multi-object tracking, multi-sensor fusion, and reliability control in a systematic way, this book is aimed at guaranteeing that intelligent vehicles can run safely in complex road traffic scenes. Adopts the multi-sensor data fusion-based neural networks to environmental perception fault tolerance algorithms, solving the problem of perception reliability when some sensors fail by using data redundancy. Presents the camera-based monocular approach to implement the robust perception tasks, which introduces sequential feature association and depth hint augmentation, and introduces seven adaptive methods. Proposes efficient and robust semantic segmentation of traffic scenes through real-time deep dual-resolution networks and representation separation of vision transformers. Focuses on trajectory prediction and proposes phased and progressive trajectory prediction methods that is more consistent with human psychological characteristics, which is able to take both social interactions and personal intentions into account. Puts forward methods based on conditional random field and multi-task segmentation learning to solve the robust multi-object tracking problem for environment perception in autonomous vehicle scenarios. Presents the novel reliability control strategies of intelligent vehicles to optimize the dynamic tracking performance and investigates the completely unknown autonomous vehicle tracking issues with actuator faults."--

Download or read book Machine Learning Techniques for Autonomous Multi Sensor Long Range Environmental Perception System written by Muhammad Abdul Haseeb and published by . This book was released on 2020 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: An environment perception system is one of the most critical components of an automated vehicle, which is defined as a vehicle where the driver does not require to monitor the vehicle's behavior and its surroundings during driving. This thesis addresses some of the main challenges in the development of vision-based environment perception methods for automated driving, focusing on railway vehicles. The thesis aims at developing methods for detecting obstacles on the rail tracks in front of a moving train to reduce the number of collisions between trains and various obstacles, thus increasing the safety of rail transport. In the field of autonomous obstacle detection for automated driving, besides recognising the objects on the way, the crucial information for collision avoidance is estimated distances between the vehicle and the recognised objects (e.g. cars, pedestrians, cyclists). With the limited capabilities of current state-of-the-art sensor-based environment perception approaches, it is unrealistic to detect distant objects and estimates the distance to them. Mid-to-long-range obstacle detection system is one of the fundamental requirements for heavy vehicles such as railway vehicles or trucks, due to required long braking distance. However, this problem is unaddressed in the computer vision community. The emphasis of this thesis is on the development of robust and reliable algorithms for real-time vision-based mid-to-long-range obstacle detection. In this thesis, the algorithms for obstacle detection from single cameras were developed and evaluated on images captured from RGB, Thermal and Night-Vision Cameras. The developed algorithms are based on advanced machine/deep learning techniques. The development of machine-learning-based algorithms was supported by a novel mid-to-long-range obstacle detection dataset for railways that is proposed in the thesis, which compiles annotated images with the object class, bounding box, and ground truth distance to the object. The developed novel methods for autonomous long-range obstacle detection, tracking, and distance estimation for railways were evaluated on real-world images, which were recorded in different illumination and weather conditions by the obstacle detection system mounted on a static test-bed set-up on the straight rail track and as well on a moving train. Although the focus is on railways, the developed algorithms are also capable to use for road vehicles, hence evaluated on the images of road-scene captured by a camera mounted on moving cars.

Download or read book Safety Assessment of Environment Perception in Automated Driving Vehicles written by Mario Berk and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Autonomous Driving written by Markus Maurer and published by Springer. This book was released on 2016-05-21 with total page 698 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book takes a look at fully automated, autonomous vehicles and discusses many open questions: How can autonomous vehicles be integrated into the current transportation system with diverse users and human drivers? Where do automated vehicles fall under current legal frameworks? What risks are associated with automation and how will society respond to these risks? How will the marketplace react to automated vehicles and what changes may be necessary for companies? Experts from Germany and the United States define key societal, engineering, and mobility issues related to the automation of vehicles. They discuss the decisions programmers of automated vehicles must make to enable vehicles to perceive their environment, interact with other road users, and choose actions that may have ethical consequences. The authors further identify expectations and concerns that will form the basis for individual and societal acceptance of autonomous driving. While the safety benefits of such vehicles are tremendous, the authors demonstrate that these benefits will only be achieved if vehicles have an appropriate safety concept at the heart of their design. Realizing the potential of automated vehicles to reorganize traffic and transform mobility of people and goods requires similar care in the design of vehicles and networks. By covering all of these topics, the book aims to provide a current, comprehensive, and scientifically sound treatment of the emerging field of “autonomous driving".

Download or read book Autonomous Vehicle Environmental Perception Using Camera Bird s Eye View and High Definition Map written by and published by . This book was released on 2021 with total page 55 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Environment Perception for Autonomous Vehicles in Challenging Conditions Using Stereo Vision written by Roberto Galán Guzmán and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multi sensor Environment Perception for Automated Vehicles with Semantic Evidential Grid Maps written by Sven Richter and published by . This book was released on 2023* with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Automated Driving written by Daniel Watzenig and published by Springer. This book was released on 2016-09-23 with total page 619 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main topics of this book include advanced control, cognitive data processing, high performance computing, functional safety, and comprehensive validation. These topics are seen as technological bricks to drive forward automated driving. The current state of the art of automated vehicle research, development and innovation is given. The book also addresses industry-driven roadmaps for major new technology advances as well as collaborative European initiatives supporting the evolvement of automated driving. Various examples highlight the state of development of automated driving as well as the way forward. The book will be of interest to academics and researchers within engineering, graduate students, automotive engineers at OEMs and suppliers, ICT and software engineers, managers, and other decision-makers.

Download or read book Virtual Sensorics Simulated Environmental Perception for Automated Driving Systems written by Timo Hanke and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Real time Forward Urban Environment Perception for an Autonomous Ground Vehicle Using Computer Vision and LIDAR written by Christopher Richard Greco and published by . This book was released on 2008 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of autonomous vehicle research is growing rapidly. The Congressional mandate for the military to use unmanned vehicles has, in large part, sparked this growth. In conjunction with this mandate, DARPA sponsored the Urban Challenge, a competition to create fully autonomous vehicles that can operate in urban settings. An extremely important feature of autonomous vehicles, especially in urban locations, is their ability to perceive their environment. The research presented in this thesis is directed toward providing an autonomous vehicle with real-time data that efficiently and compactly represents its forward environment as it navigates an urban area. The information extracted from the environment for this application consists of stop line locations, lane information, and obstacle locations, using a single camera and LIDAR scanner. A road/non-road binary mask is first segmented. From the road information in the mask, the current traveling lane of the vehicle is detected using a minimum distance transform and tracked between frames. The stop lines and obstacles are detected from the non-road information in the mask. Stop lines are detected using a variation of vertical profiling, and obstacles are detected using shape descriptors. A laser rangefinder is used in conjunction with the camera in a primitive form of sensor fusion to create a list of obstacles in the forward environment. Obstacle boundaries, lane points, and stop line centers are then translated from image coordinates to UTM coordinates using a homography transform created during the camera calibration procedure. A novel system for rapid camera calibration was also implemented. Algorithms investigated during the development phase of the project are included in the text for the purposes of explaining design decisions and providing direction to researchers who will continue the work in this field. The results were promising, performing the tasks fairly accurately at a rate of about 20 frames per second, using an Intel Core2 Duo processor with 2 GB RAM.

Download or read book Video based Environment Perception for Automated Driving Using Deep Neural Networks written by Niels Ole Salscheider and published by . This book was released on 2021* with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Sustainability Prospects for Autonomous Vehicles written by George T. Martin and published by Routledge. This book was released on 2019-05-31 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Autonomous Vehicle (AV) has been strongly heralded as the most exciting innovation in automobility for decades. Autonomous Vehicles are no longer an innovation of the future (seen only in science fiction) but are now being road-tested for use. And yet while the technical and economic success and possibilities of the AV have been widely debated, there has been a notable lack of discussion around the social, behavioural, and environmental implications. This book is the first to address these issues and to deeply consider the environmental and social sustainability outlook for the AV and how it will impact on communities. Environmental and social sustainability are goals unlike those of technical development (a new tool) and economic development (a new investment). The goal of sustainability is development of societies that live well and equitably within their ecological limits. Is it reasonable and desirable that only technical and economic success comprise the swelling AV parade, or should we be looking at the wider impacts on personal well-being, wider society, and the environment? The uptake for AVs looks to be lengthy, disjointed, and episodic, in large measure because it faces a range of known unknown risks. This book assesses the environmental and social sustainability potential for AVs based on their prospective energy use and their impacts on climate change, urban landscapes, public health, mobility inequalities, and individual and social well-being. It examines public attitudes about AV use and its risk of fostering a rebound effect that compromises potential sustainability gains. The book concludes with a discussion of critical issues involved in sustainable AV diffusion.

Download or read book AI enabled Technologies for Autonomous and Connected Vehicles written by Yi Lu Murphey and published by Springer Nature. This book was released on 2022-09-07 with total page 563 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reports on cutting-edge research and advances in the field of intelligent vehicle systems. It presents a broad range of AI-enabled technologies, with a focus on automated, autonomous and connected vehicle systems. It covers advanced machine learning technologies, including deep and reinforcement learning algorithms, transfer learning and learning from big data, as well as control theory applied to mobility and vehicle systems. Furthermore, it reports on cutting-edge technologies for environmental perception and vehicle-to-everything (V2X), discussing socioeconomic and environmental implications, and aspects related to human factors and energy-efficiency alike, of automated mobility. Gathering chapters written by renowned researchers and professionals, this book offers a good balance of theoretical and practical knowledge. It provides researchers, practitioners and policy makers with a comprehensive and timely guide on the field of autonomous driving technologies.

Download or read book Urban Environment Perception and Navigation Using Robotic Vision written by Giovani Bernardes Vitor and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of autonomous vehicles capable of getting around on urban roads can provide important benefits in reducing accidents, in increasing life comfort and also in providing cost savings. Intelligent vehicles for example often base their decisions on observations obtained from various sensors such as LIDAR, GPS and Cameras. Actually, camera sensors have been receiving large attention due to they are cheap, easy to employ and provide rich data information. Inner-city environments represent an interesting but also very challenging scenario in this context,where the road layout may be very complex, the presence of objects such as trees, bicycles,cars might generate partial observations and also these observations are often noisy or even missing due to heavy occlusions. Thus, perception process by nature needs to be able to dea lwith uncertainties in the knowledge of the world around the car. While highway navigation and autonomous driving using a prior knowledge of the environment have been demonstrating successfully,understanding and navigating general inner-city scenarios with little prior knowledge remains an unsolved problem. In this thesis, this perception problem is analyzed for driving in the inner-city environments associated with the capacity to perform a safe displacement basedon decision-making process in autonomous navigation. It is designed a perception system that allows robotic-cars to drive autonomously on roads, with out the need to adapt the infrastructure,without requiring previous knowledge of the environment and considering the presenceof dynamic objects such as cars. It is proposed a novel method based on machine learning to extract the semantic context using a pair of stereo images, which is merged in an evidential grid to model the uncertainties of an unknown urban environment, applying the Dempster-Shafer theory. To make decisions in path-planning, it is applied the virtual tentacle approach to generate possible paths starting from ego-referenced car and based on it, two news strategies are proposed. First one, a new strategy to select the correct path to better avoid obstacles and tofollow the local task in the context of hybrid navigation, and second, a new closed loop control based on visual odometry and virtual tentacle is modeled to path-following execution. Finally, a complete automotive system integrating the perception, path-planning and control modules are implemented and experimentally validated in real situations using an experimental autonomous car, where the results show that the developed approach successfully performs a safe local navigation based on camera sensors.