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Lane Precise Localization With Production Vehicle Sensors And Application To Augmented Reality Navigation
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Book Synopsis Lane-Precise Localization with Production Vehicle Sensors and Application to Augmented Reality Navigation by : Rabe, Johannes
Download or read book Lane-Precise Localization with Production Vehicle Sensors and Application to Augmented Reality Navigation written by Rabe, Johannes and published by KIT Scientific Publishing. This book was released on 2019-01-10 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This works describes an approach to lane-precise localization on current digital maps. A particle filter fuses data from production vehicle sensors, such as GPS, radar, and camera. Performance evaluations on more than 200 km of data show that the proposed algorithm can reliably determine the current lane. Furthermore, a possible architecture for an intuitive route guidance system based on Augmented Reality is proposed together with a lane-change recommendation for unclear situations.
Book Synopsis Lane-Precise Localization With Production Vehicle Sensors and Application to Augmented Reality Navigation by : Johannes Rabe
Download or read book Lane-Precise Localization With Production Vehicle Sensors and Application to Augmented Reality Navigation written by Johannes Rabe and published by . This book was released on 2020-10-09 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: This works describes an approach to lane-precise localization on current digital maps. A particle filter fuses data from production vehicle sensors, such as GPS, radar, and camera. Performance evaluations on more than 200 km of data show that the proposed algorithm can reliably determine the current lane. Furthermore, a possible architecture for an intuitive route guidance system based on Augmented Reality is proposed together with a lane-change recommendation for unclear situations. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
Book Synopsis Probabilistic Motion Planning for Automated Vehicles by : Naumann, Maximilian
Download or read book Probabilistic Motion Planning for Automated Vehicles written by Naumann, Maximilian and published by KIT Scientific Publishing. This book was released on 2021-02-25 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: In motion planning for automated vehicles, a thorough uncertainty consideration is crucial to facilitate safe and convenient driving behavior. This work presents three motion planning approaches which are targeted towards the predominant uncertainties in different scenarios, along with an extended safety verification framework. The approaches consider uncertainties from imperfect perception, occlusions and limited sensor range, and also those in the behavior of other traffic participants.
Book Synopsis Motion Planning for Autonomous Vehicles in Partially Observable Environments by : Taş, Ömer Şahin
Download or read book Motion Planning for Autonomous Vehicles in Partially Observable Environments written by Taş, Ömer Şahin and published by KIT Scientific Publishing. This book was released on 2023-10-23 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work develops a motion planner that compensates the deficiencies from perception modules by exploiting the reaction capabilities of a vehicle. The work analyzes present uncertainties and defines driving objectives together with constraints that ensure safety. The resulting problem is solved in real-time, in two distinct ways: first, with nonlinear optimization, and secondly, by framing it as a partially observable Markov decision process and approximating the solution with sampling.
Book Synopsis Belief State Planning for Autonomous Driving: Planning with Interaction, Uncertain Prediction and Uncertain Perception by : Hubmann, Constantin
Download or read book Belief State Planning for Autonomous Driving: Planning with Interaction, Uncertain Prediction and Uncertain Perception written by Hubmann, Constantin and published by KIT Scientific Publishing. This book was released on 2021-09-13 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work presents a behavior planning algorithm for automated driving in urban environments with an uncertain and dynamic nature. The algorithm allows to consider the prediction uncertainty (e.g. different intentions), perception uncertainty (e.g. occlusions) as well as the uncertain interactive behavior of the other agents explicitly. Simulating the most likely future scenarios allows to find an optimal policy online that enables non-conservative planning under uncertainty.
Book Synopsis Novel Aggregated Solutions for Robust Visual Tracking in Traffic Scenarios by : Tian, Wei
Download or read book Novel Aggregated Solutions for Robust Visual Tracking in Traffic Scenarios written by Tian, Wei and published by KIT Scientific Publishing. This book was released on 2019-05-21 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work proposes novel approaches for object tracking in challenging scenarios like severe occlusion, deteriorated vision and long range multi-object reidenti?cation. All these solutions are only based on image sequence captured by a monocular camera and do not require additional sensors. Experiments on standard benchmarks demonstrate an improved state-of-the-art performance of these approaches. Since all the presented approaches are smartly designed, they can run at a real-time speed.
Book Synopsis Autonomous Road Vehicles Localization Using Satellites, Lane Markings and Vision by : Zui Tao
Download or read book Autonomous Road Vehicles Localization Using Satellites, Lane Markings and Vision written by Zui Tao and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Estimating the pose (position and attitude) in real-time is a key function for road autonomous vehicles. This thesis aims at studying vehicle localization performance using low cost automotive sensors. Three kinds of sensors are considered : dead reckoning (DR) sensors that already exist in modern vehicles, mono-frequency GNSS (Global navigation satellite system) receivers with patch antennas and a frontlooking lane detection camera. Highly accurate maps enhanced with road features are also key components for autonomous vehicle navigation. In this work, a lane marking map with decimeter-level accuracy is considered. The localization problem is studied in a local East-North-Up (ENU) working frame. Indeed, the localization outputs are used in real-time as inputs to a path planner and a motion generator to make a valet vehicle able to drive autonomously at low speed with nobody on-board the car. The use of a lane detection camera makes possible to exploit lane marking information stored in the georeferenced map. A lane marking detection module detects the vehicle's host lane and provides the lateral distance between the detected lane marking and the vehicle. The camera is also able to identify the type of the detected lane markings (e.g., solid or dashed). Since the camera gives relative measurements, the important step is to link the measures with the vehicle's state. A refined camera observation model is proposed. It expresses the camera metric measurements as a function of the vehicle's state vector and the parameters of the detected lane markings. However, the use of a camera alone has some limitations. For example, lane markings can be missing in some parts of the navigation area and the camera sometimes fails to detect the lane markings in particular at cross-roads. GNSS, which is mandatory for cold start initialization, can be used also continuously in the multi-sensor localization system as done often when GNSS compensates for the DR drift. GNSS positioning errors can't be modeled as white noises in particular with low cost mono-frequency receivers working in a standalone way, due to the unknown delays when the satellites signals cross the atmosphere and real-time satellites orbits errors. GNSS can also be affected by strong biases which are mainly due to multipath effect. This thesis studies GNSS biases shaping models that are used in the localization solver by augmenting the state vector. An abrupt bias due to multipath is seen as an outlier that has to be rejected by the filter. Depending on the information flows between the GNSS receiver and the other components of the localization system, data-fusion architectures are commonly referred to as loosely coupled (GNSS fixes and velocities) and tightly coupled (raw pseudoranges and Dopplers for the satellites in view). This thesis investigates both approaches. In particular, a road-invariant approach is proposed to handle a refined modeling of the GNSS error in the loosely coupled approach since the camera can only improve the localization performance in the lateral direction of the road. Finally, this research discusses some map-matching issues for instance when the uncertainty domain of the vehicle state becomes large if the camera is blind. It is challenging in this case to distinguish between different lanes when the camera retrieves lane marking measurements.As many outdoor experiments have been carried out with equipped vehicles, every problem addressed in this thesis is evaluated with real data. The different studied approaches that perform the data fusion of DR, GNSS, camera and lane marking map are compared and several conclusions are drawn on the fusion architecture choice.
Book Synopsis Automatic Laser Calibration, Mapping, and Localization for Autonomous Vehicles by : Jesse Sol Levinson
Download or read book Automatic Laser Calibration, Mapping, and Localization for Autonomous Vehicles written by Jesse Sol Levinson and published by Stanford University. This book was released on 2011 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation presents several related algorithms that enable important capabilities for self-driving vehicles. Using a rotating multi-beam laser rangefinder to sense the world, our vehicle scans millions of 3D points every second. Calibrating these sensors plays a crucial role in accurate perception, but manual calibration is unreasonably tedious, and generally inaccurate. As an alternative, we present an unsupervised algorithm for automatically calibrating both the intrinsics and extrinsics of the laser unit from only seconds of driving in an arbitrary and unknown environment. We show that the results are not only vastly easier to obtain than traditional calibration techniques, they are also more accurate. A second key challenge in autonomous navigation is reliable localization in the face of uncertainty. Using our calibrated sensors, we obtain high resolution infrared reflectivity readings of the world. From these, we build large-scale self-consistent probabilistic laser maps of urban scenes, and show that we can reliably localize a vehicle against these maps to within centimeters, even in dynamic environments, by fusing noisy GPS and IMU readings with the laser in realtime. We also present a localization algorithm that was used in the DARPA Urban Challenge, which operated without a prerecorded laser map, and allowed our vehicle to complete the entire six-hour course without a single localization failure. Finally, we present a collection of algorithms for the mapping and detection of traffic lights in realtime. These methods use a combination of computer-vision techniques and probabilistic approaches to incorporating uncertainty in order to allow our vehicle to reliably ascertain the state of traffic-light-controlled intersections.
Book Synopsis Precise Localization in 3D Prior Map for Autonomous Driving by : Mohamed Lamine Tazir
Download or read book Precise Localization in 3D Prior Map for Autonomous Driving written by Mohamed Lamine Tazir and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The concept of self-driving vehicles is becoming a happening reality and will soon share our roads with other vehicles -autonomous or not-. For a self-driving car to move around in its environment in a securely, it needs to sense to its immediate environment and most importantly localize itself to be able to plan a safe trajectory to follow. Therefore, to perform tasks suchas trajectory planning and navigation, a precise localization is of upmost importance. This would further allow the vehicle toconstantly plan and predict an optimal path in order to weave through cluttered spaces by avoiding collisions with other agentssharing the same space as the latter. For years, the Global Positioning System (GPS) has been a widespread complementary solution for navigation. The latter allows only a limited precision (range of several meters). Although the Differential GPSand the Real Time Kinematic (RTK) systems have reached considerable accuracy, these systems remain sensitive to signal masking and multiple reflections, offering poor reliability in dense urban areas. All these deficiencies make these systems simply unsuitable to handle hard real time constraints such as collision avoidance. A prevailing alternative that has attracted interest recently, is to use upload a prior map in the system so that the agent can have a reliable support to lean on. Indeed,maps facilitate the navigation process and add an extra layer of security and other dimensions of semantic understanding. The vehicle uses its onboard sensors to compare what it perceives at a given instant to what is stored in the backend memory ofthe system. In this way, the autonomous vehicle can actually anticipate and predict its actions accordingly.The purpose of this thesis is to develop tools allowing an accurate localization task in order to deal with some complex navigation tasks outlined above. Localization is mainly performed by matching a 3D prior map with incoming point cloudstructures as the vehicle moves. Three main objectives are set out leading with two distinct phases deployed (the map building and the localization). The first allows the construction of the map, with centimeter accuracy using static or dynamic laser surveying technique. Explicit details about the experimental setup and data acquisition campaigns thoroughly carried outduring the course of this work are given. The idea is to construct efficient maps liable to be updated in the long run so thatthe environment representation contained in the 3D models are compact and robust. Moreover, map-building invariant on any dedicated infrastructure is of the paramount importance of this work in order to rhyme with the concept of flexible mapping and localization. In order to build maps incrementally, we rely on a self-implementation of state of the art iterative closest point (ICP) algorithm, which is then upgraded with new variants and compared to other implemented versions available inthe literature. However, obtaining accurate maps requires very dense point clouds, which make them inefficient for real-time use. Inthis context, the second objective deals with points cloud reduction. The proposed approach is based on the use of both colorinformation and the geometry of the scene. It aims to find sets of 3D points with the same color in a very small region and replacing each set with one point. As a result, the volume of the map will be significantly reduced, while the proprieties of this map such as the shape and color of scanned objects remain preserved.The third objective resort to efficient, precise and reliable localization once the maps are built and treated. For this purpose, the online data should be accurate, fast with low computational effort whilst maintaining a coherent model of the explored space. To this end, the Velodyne HDL-32 comes into play. (...).
Book Synopsis Special Issue of the Manufacturing Engineering Society 2019 (SIMES-2019) by : Eva M. Rubio
Download or read book Special Issue of the Manufacturing Engineering Society 2019 (SIMES-2019) written by Eva M. Rubio and published by MDPI. This book was released on 2020-07-03 with total page 534 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book derives from the Special Issue of the Manufacturing Engineering Society 2019 (SIMES-2019) that has been launched as a joint issue of the journals Materials and Applied Sciences. The 29 contributions published in this Special Issue of Materials present cutting-edge advances in the field of manufacturing engineering focusing on additive manufacturing and 3D printing; advances and innovations in manufacturing processes; sustainable and green manufacturing; manufacturing of new materials; metrology and quality in manufacturing; industry 4.0; design, modeling, and simulation in manufacturing engineering; and manufacturing engineering and society. Among them, the topic "Additive Manufacturing and 3D Printing" has attracted a large number of contributions in this journal due to its widespread popularity and potential.
Book Synopsis Adaptive Kalman Filtering Methods for Low-cost GPS/INS Localization for Autonomous Vehicles by : Adam Werries
Download or read book Adaptive Kalman Filtering Methods for Low-cost GPS/INS Localization for Autonomous Vehicles written by Adam Werries and published by . This book was released on 2016 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt: For autonomous vehicles, navigation systems must be accurate enough to provide lane-level localization. High-accuracy sensors are available but not cost-effective for production use. Although prone to significant error in poor circumstances, even low-cost GPS systems are able to correct Inertial Navigation Systems to limit the effects of dead reckoning error over short periods between sufficiently accurate GPS updates. Kalman filters are a standard approach for GPS/INS integration, but require careful tuning in order to achieve quality results. This creates a motivation for a Kalman filter which is able to adapt to different sensors and circumstances on its own. Typically for adaptive filters, either the process (Q) or measurement (R) noise covariance matrix of Kalman filters is adapted, and the other is fixed to values estimated a priori. We show that intelligently adapting both matrices in an intelligent manner can provide a more accurate navigation solution.
Book Synopsis Local Sensor Data Fusion and Its Application to Autonomous Vehicle Navigation by : Phil Osteen
Download or read book Local Sensor Data Fusion and Its Application to Autonomous Vehicle Navigation written by Phil Osteen and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: ABSTRACT: The rate of development of autonomous vehicles over the last five years has been remarkable. Advancements have been made at such a pace that between DARPA's first Grand Challenge in 2004 and the Urban Challenge in 2007, vehicles have gone from failing to complete basic navigation tasks to successfully navigating urban streets. Significant progress has been made in the controls and planning areas, with navigation abilities improving as a partial result of improvements in Global Positioning precision. Hardware advancements have been made as well, with both computers and sensors increasing data storage and processing capabilities. As a result, recent research has focused on combining the advanced computational abilities of computers with the increasingly relevant data provided by local sensors. This thesis deals with combining advancements in the fields of sensing and controls to improve upon existing autonomous navigation architectures. The Lane Finder Arbiter, a software component created for this research, provides an interface between raw sensing components and vehicle navigation components. The problem statement is first described, followed by a review of similar research and a description of prior research within the Center of Intelligent Machines and Robotics (CIMAR) that led to the creation of the Lane Finder Arbiter. The Lane Finder Arbiter, the focus of the thesis, is then described. The statistical methods used for this research are then discussed, and finally the results obtained from testing are analyzed. These results are used to draw conclusions about the Lane Finder Arbiter's current strengths, as well as possible future improvements to the new navigation architecture.
Book Synopsis Lane-level Vehicle Localization with Integrity Monitoring for Data Aggregation by : Franck Li
Download or read book Lane-level Vehicle Localization with Integrity Monitoring for Data Aggregation written by Franck Li and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The information stored in digital road maps has become very important for intelligent vehicles. As intelligent vehicles address more complex environments, the accuracy requirements for this information have increased. Regarded as a geographic database, digital road maps contain contextual information about the road network, crucial for a good understanding of the environment. When combined with data acquired from on-board sensors, a better representation of the environment can be made, improving the vehicle's situation understanding. Sensors performance can vary drastically depending on the location of the vehicle, mainly due to environmental factors. Comparatively, a map can provide prior information more reliably but to do so, it depends on another essential component: a localization system. Global Navigation Satellite Systems (GNSS) are commonly used in automotive to provide an absolute positioning of the vehicle, but its accuracy is not perfect: GNSS are prone to errors, also depending greatly on the environment (e.g., multipaths). Perception and localization systems are two important components of an intelligent vehicle whose performances vary in function of the vehicle location. This research focuses on their common denominator, the digital road map, and its use as a tool to assess their performance. The idea developed during this thesis is to use the map as a learning canvas, to store georeferenced information about the performance of the sensors during repetitive travels. This requires a robust localization with respect to the map to be available, through a process of map-matching. The main problematic is the discrepancy between the accuracy of the map and of the GNSS, creating ambiguous situations. This thesis develops a map-matching algorithm designed to cope with these ambiguities by providing multiple hypotheses when necessary. The objective is to ensure the integrity of the result by returning a hypothesis set containing the correct matching with high probability. The method relies on proprioceptive sensors via a dead-reckoning approach aided by the map. A coherence checking procedure using GNSS redundant information is then applied to isolate a single map-matching result that can be used to write learning data with confidence in the map. The possibility to handle the digital map in read/write operation has been assessed and the whole writing procedure has been tested on data recorded by test vehicles on open roads.
Book Synopsis Template-Based Feature Map Creation Using GPS and 2D LIDAR Scan Intensity and Height Extrema by : Vahan Kazandjian
Download or read book Template-Based Feature Map Creation Using GPS and 2D LIDAR Scan Intensity and Height Extrema written by Vahan Kazandjian and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many existing methods for detecting lane geometries require the use of image data or LIDAR data features. Of the two sensors, LIDAR is the primary means of obtaining direct geometric information as well as reflectivity information. The challenge with using LIDAR data is understanding the relationships between features in each scan and creating a lane map that a vehicle can use to localize itself. The purpose of this study is to develop a robust method for aligning features in LIDAR scans, particularly aligning intensity and geometry both with each other and with repeated scans, even in scenarios where scan features, such as lane lines, are worn, occluded, or missing. This study also develops methods of templating features to produce scan-level maps of road features. To create a map of these complex lane topologies for use with a vehicle localization algorithm, optimal extrema filtering of LIDAR data is used to independently identify the position of lane and road boundary features in multiple lanes at once. Then, a filtering and averaging process is applied to these features to align LIDAR scans and estimate centerline positions for pairs of lane markings. By using the intersection of lane centerline predictions, the areas where lane features -- lines, geometric extrema, etc. -- cluster are stored to produce a template associated with a specific road location. When applied to data from a closed-loop test track, the algorithm presented in this thesis was found to identify lane markers with 9.8 mm 2-[sigma] precision and height features with 35.6 mm 2-[sigma] precision. Creating accurate maps lane features enables autonomous vehicles to operate in more complex situations with less need for human intervention. Path planning algorithms can use the a priori information about when and where lane topology changes to optimize maneuvers. In addition, maps of individual lane lines, road boundaries, and centerlines can be used to improve real-time vehicle localization in scenarios where road features are occluded, worn, or missing.
Book Synopsis Springer Handbook of Automation by : Shimon Y. Nof
Download or read book Springer Handbook of Automation written by Shimon Y. Nof and published by Springer Nature. This book was released on 2023-06-16 with total page 1533 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook incorporates new developments in automation. It also presents a widespread and well-structured conglomeration of new emerging application areas, such as medical systems and health, transportation, security and maintenance, service, construction and retail as well as production or logistics. The handbook is not only an ideal resource for automation experts but also for people new to this expanding field.
Book Synopsis Localization Algorithms and Strategies for Wireless Sensor Networks: Monitoring and Surveillance Techniques for Target Tracking by : Mao, Guoqiang
Download or read book Localization Algorithms and Strategies for Wireless Sensor Networks: Monitoring and Surveillance Techniques for Target Tracking written by Mao, Guoqiang and published by IGI Global. This book was released on 2009-05-31 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wireless localization techniques are an area that has attracted interest from both industry and academia, with self-localization capability providing a highly desirable characteristic of wireless sensor networks. Localization Algorithms and Strategies for Wireless Sensor Networks encompasses the significant and fast growing area of wireless localization techniques. This book provides comprehensive and up-to-date coverage of topics and fundamental theories underpinning measurement techniques and localization algorithms. A useful compilation for academicians, researchers, and practitioners, this Premier Reference Source contains relevant references and the latest studies emerging out of the wireless sensor network field.
Book Synopsis Human Factors in Augmented Reality Environments by : Weidong Huang
Download or read book Human Factors in Augmented Reality Environments written by Weidong Huang and published by Springer Science & Business Media. This book was released on 2012-09-19 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in hardware and networking have made possible a wide use of augmented reality (AR) technologies. However, simply putting those hardware and technologies together does not make a “good” system for end users to use. New design principles and evaluation methods specific to this emerging area are urgently needed to keep up with the advance in technologies. Human Factors in Augmented Reality Environments is the first book on human factors in AR, addressing issues related to design, development, evaluation and application of AR systems. Topics include surveys, case studies, evaluation methods and metrics, HCI theories and design principles, human factors and lessons learned and experience obtained from developing, deploying or evaluating AR systems. The contributors for this cutting-edge volume are well-established researchers from diverse disciplines including psychologists, artists, engineers and scientists. Human Factors in Augmented Reality Environments is designed for a professional audience composed of practitioners and researchers working in the field of AR and human-computer interaction. Advanced-level students in computer science and engineering will also find this book useful as a secondary text or reference.
