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Multi Sensor Multi Object Tracking In Autonomous Vehicles
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Book Synopsis Multi Sensor Multi Object Tracking in Autonomous Vehicles by : Surya Kollazhi Manghat
Download or read book Multi Sensor Multi Object Tracking in Autonomous Vehicles written by Surya Kollazhi Manghat and published by . This book was released on 2019 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: Self driving cars becoming more popular nowadays, which transport with it's own intelligence and take appropriate actions at adequate time. Safety is the key factor in driving environment. A simple fail of action can cause many fatalities. Computer Vision has major part in achieving this, it help the autonomous vehicle to perceive the surroundings. Detection is a very popular technique in helping to capture the surrounding for an autonomous car. At the same time tracking also has important role in this by providing dynamic of detected objects. Autonomous cars combine a variety of sensors such as RADAR, LiDAR, sonar, GPS, odometry and inertial measurement units to perceive their surroundings. Driver-assistive technologies like Adaptive Cruise Control, Forward Collision Warning system (FCW) and Collision Mitigation by Breaking (CMbB) ensure safety while driving. Perceiving the information from environment include setting up sensors on the car. These sensors will collect the data it sees and this will be further processed for taking actions. The sensor system can be a single sensor or multiple sensor. Different sensors have different strengths and weaknesses which makes the combination of them important for technologies like Autonomous Driving. Each sensor will have a limit of accuracy on it's readings, so multi sensor system can help to overcome this defects. This thesis is an attempt to develop a multi sensor multi object tracking method to perceive the surrounding of the ego vehicle. When the Object detection gives information about the presence of objects in a frame, Object Tracking goes beyond simple observation to more useful action of monitoring objects. The experimental results conducted on KITTI dataset indicate that our proposed state estimation system for Multi Object Tracking works well in various challenging environments.
Book Synopsis Multi-sensor Fusion for Autonomous Driving by : Xinyu Zhang
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:
Book Synopsis Robust Environmental Perception and Reliability Control for Intelligent Vehicles by : Huihui Pan
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.
Book Synopsis Probabilistic Multi-object Tracking for Autonomous Vehicles by : Michael Motro
Download or read book Probabilistic Multi-object Tracking for Autonomous Vehicles written by Michael Motro and published by . This book was released on 2019 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interactive robots such as self-driving cars require accurate hardware and methods to locate relevant objects such as other traffic participants. They also must predict other participants' actions or understand their role in the environment. Given imperfect information about present objects at each time, a multi-object tracker maintains an estimate of all present relevant objects and infers motion or other information that can be deduced from viewing an object over time. Trackers are often built around a probabilistic model that includes known characteristics of object motion and sensor behavior. This thesis discusses several details for designing a probabilistic multi-object tracker for vehicular environments, as well as ways to utilize probabilistic tracked estimates for autonomous vehicle applications. The increasingly complex environments perceived by robots have demanded new paradigms of perception. In particular, camera and laser-based perception of urban settings is solved using learned algorithms that directly transform raw data into object estimates. We present a probabilistic model of modern object detectors that can be integrated with standard trackers. The primary effects that are modelled are line-of-sight limitations to sensor detection, and correlation in algorithmic detection errors over time. Each of these modifications are shown to improve performance on a public benchmark for vehicle tracking, without fundamental modifications to the tracking algorithm. Accurate tracking can require intensive computation on its own. We examine the implementation of multiple hypothesis tracking, a high-performance probabilistic tracker, and improve the computational efficiency of its data association algorithm in several ways. The modified algorithm is tested on vehicular tracking data as well as simulated large-scale and multisensor problems. The improved speed of the algorithm allows for more hypotheses to be propagated at a given speed, which in turn improves tracking performance. In addition to improving the current estimate of the environment, tracking enables prediction of the future environment by determining object motion and history. The uncertainty of these estimates can be quantified by a probabilistic tracker and should be considered when making predictions or deciding actions. However, probabilistic estimates are difficult to translate into interpretable and actionable concepts, such detection of impending collisions between objects. We disambiguate the error rate in collision detection into inevitable errors from uncertain object estimation and further errors incurred by fast approximate calculation of the probability of collision from these estimates. Various methods for collision detection from uncertain data are compared and tested on vehicle simulations. Automated overtaking assistants are studied as a specific application of collision detection. These assistants alert drivers in advance that entering the opposite lane to pass a slower vehicle will be unsafe. We characterize the expected design of these systems, including sensor or communication accuracy and limitations as well as driver variability and uncertainty in future motion. Overtaking assistant simulations demonstrate that the assistant can fulfill its purpose at expected levels of tracking and prediction uncertainty, provided that the chosen sensor or communicating device has a sufficient operating distance
Book Synopsis Multi-sensor Multi-object Tracking for Connected Automated Driving with Distributed Sensors by : Martin Herrmann
Download or read book Multi-sensor Multi-object Tracking for Connected Automated Driving with Distributed Sensors written by Martin Herrmann and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multi-Object, Multi-Sensor Detection and Tracking of Pedestrians on a Mobile Robot by : Lucas Xavier De la Garza
Download or read book Multi-Object, Multi-Sensor Detection and Tracking of Pedestrians on a Mobile Robot written by Lucas Xavier De la Garza and published by . This book was released on 2016 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous mobile robots will play an important role in human society in the near future. In particular, many autonomous mobile robots will perform long-term tasks in the same environments that humans reside, sometimes even interacting with people face-to-face. Examples of such robots include self-driving cars, domestic service robots, autonomous couriers, and robotic tour guides. Robots that perform these missions must plan based on accurate, robust information about the local dynamic state of the nearby pedestrians; this work explores the means of attaining dynamic scene information on person-scale mobile robots. From a multi-object tracking perspective, varying environment backgrounds, sensor noise, false detections (clutter), occlusion, and imperfect measurement extraction make inferring information about the dynamic scene difficult. Pedestrians also have certain quirks that pose additional challenges as trackable objects - they walk in groups, have difficult-to-predict motion, and vary in appearance (such as differing clothes and height). In this work, a suite of five different tracking "architectures" are developed and compared, leveraging sensor data from a 2D lidar sensor and an array of cameras with non-overlapping fields of view. In each architecture, the multi-sensor data are fused in a recursive Bayesian multi-object tracker, with certain differences in modeling representation and measurement pre-processing. These tracking algorithms are evaluated quantitatively in a set of real-world experiments, and the overall tracking performance of each architecture is evaluated with a set of rigorous multiobject metric scores. It is found that the high recall rate of the lidar detection complements the higher-precision but lower-recall visual detection, and that some simple pre-processing steps are of benefit in high-clutter scenarios.
Book Synopsis Sensor Fusion for 3D Object Detection for Autonomous Vehicles by : Yahya Massoud
Download or read book Sensor Fusion for 3D Object Detection for Autonomous Vehicles written by Yahya Massoud and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Thanks to the major advancements in hardware and computational power, sensor technology, and artificial intelligence, the race for fully autonomous driving systems is heating up. With a countless number of challenging conditions and driving scenarios, researchers are tackling the most challenging problems in driverless cars. One of the most critical components is the perception module, which enables an autonomous vehicle to "see" and "understand" its surrounding environment. Given that modern vehicles can have large number of sensors and available data streams, this thesis presents a deep learning-based framework that leverages multimodal data - i.e. sensor fusion, to perform the task of 3D object detection and localization. We provide an extensive review of the advancements of deep learning-based methods in computer vision, specifically in 2D and 3D object detection tasks. We also study the progress of the literature in both single-sensor and multi-sensor data fusion techniques. Furthermore, we present an in-depth explanation of our proposed approach that performs sensor fusion using input streams from LiDAR and Camera sensors, aiming to simultaneously perform 2D, 3D, and Bird's Eye View detection. Our experiments highlight the importance of learnable data fusion mechanisms and multi-task learning, the impact of different CNN design decisions, speed-accuracy tradeoffs, and ways to deal with overfitting in multi-sensor data fusion frameworks.
Book Synopsis 3D Online Multi-object Tracking for Autonomous Driving by : Venkateshwaran Balasubramanian
Download or read book 3D Online Multi-object Tracking for Autonomous Driving written by Venkateshwaran Balasubramanian and published by . This book was released on 2019 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research work focuses on exploring a novel 3D multi-object tracking architecture: 'FANTrack: 3D Multi-Object Tracking with Feature Association Network' for autonomous driving, based on tracking by detection and online tracking strategies using deep learning architectures for data association. The problem of multi-target tracking aims to assign noisy detections to a-priori unknown and time-varying number of tracked objects across a sequence of frames. A majority of the existing solutions focus on either tediously designing cost functions or formulating the task of data association as a complex optimization problem that can be solved effectively. Instead, we exploit the power of deep learning to formulate the data association problem as inference in a CNN. To this end, we propose to learn a similarity function that combines cues from both image and spatial features of objects. The proposed approach consists of a similarity network that predicts the similarity scores of the object pairs and builds a local similarity map. Another network formulates the data association problem as inference in a CNN by using the similarity scores and spatial information. The model learns to perform global assignments in 3D purely from data, handles noisy detections and a varying number of targets, and is easy to train. Experiments on the challenging Kitti dataset show competitive results with the state of the art. The model is finally implemented in ROS and deployed on our autonomous vehicle to show the robustness and online tracking capabilities. The proposed tracker runs alongside the object detector utilizing the resources efficiently.
Book Synopsis Sensor Fusion in Localization, Mapping and Tracking by : Constantin Wellhausen
Download or read book Sensor Fusion in Localization, Mapping and Tracking written by Constantin Wellhausen and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Making autonomous driving possible requires extensive information about the surroundings as well as the state of the vehicle. While specific information can be obtained through singular sensors, a full estimation requires a multi sensory approach, including redundant sources of information to increase robustness. This thesis gives an overview of tasks that arise in sensor fusion in autonomous driving, and presents solutions at a high level of detail, including derivations and parameters where required to enable re-implementation. The thesis includes theoretical considerations of the approaches as well as practical evaluations. Evaluations are also included for approaches that did not prove to solve their tasks robustly. This follows the belief that both results further the state of the art by giving researchers ideas about suitable and unsuitable approaches, where otherwise the unsuitable approaches may be re-implemented multiple times with similar results. The thesis focuses on model-based methods, also referred to in the following as classical methods, with a special focus on probabilistic and evidential theories. Methods based on deep learning are explicitly not covered to maintain explainability and robustness which would otherwise strongly rely on the available training data. The main focus of the work lies in three main fields of autonomous driving: localization, which estimates the state of the ego-vehicle, mapping or obstacle detection, where drivable areas are identified, and object detection and tracking, which estimates the state of all surrounding traffic participants. All algorithms are designed with the requirements of autonomous driving in mind, with a focus on robustness, real-time capability and usability of the approaches in all potential scenarios that may arise in urban driving. In localization the state of the vehicle is determined. While traditionally global positioning systems such as a Global Navigation Satellite System (GNSS) are often used for this task, they are prone to errors and may produce jumps in the position estimate which may cause unexpected and dangerous behavior. The focus of research in this thesis is the development of a localization system which produces a smooth state estimate without any jumps. For this two localization approaches are developed and executed in parallel. One localization is performed without global information to avoid jumps. This however only provides odometry, which drifts over time and does not give global positioning. To provide this information the second localization includes GNSS information, thus providing a global estimate which is free of global drift. Additionally the use of LiDAR odometry for improving the localization accuracy is evaluated. For mapping the focus of this thesis is on providing a computationally efficient mapping system which is capable of being used in arbitrarily large areas with no predefined size. This is achieved by mapping only the direct environment of the vehicle, with older information in the map being discarded. This is motivated by the observation that the environment in autonomous driving is highly dynamic and must be mapped anew every time the vehicles sensors observe an area. The provided map gives subsequent algorithms information about areas where the vehicle can or cannot drive. For this an occupancy grid map is used, which discretizes the map into cells of a fixed size, with each cell estimating whether its corresponding space in the world is occupied. However the grid map is not created for the entire area which could potentially be visited, as this may be very large and potentially impossible to represent in the working memory. Instead the map is created only for a window around the vehicle, with the vehicle roughly in the center. A hierarchical map organization is used to allow efficient moving of the window as the vehicle moves through an area. For the hierarchical map different data structures are evaluated for their time and space complexity in order to find the most suitable implementation for the presented mapping approach. Finally for tracking a late-fusion approach to the multi-sensor fusion task of estimating states of all other traffic participants is presented. Object detections are obtained from LiDAR, camera and Radar sensors, with an additional source of information being obtained from vehicle-to-everything communication which is also fused in the late fusion. The late fusion is developed for easy extendability and with arbitrary object detection algorithms in mind. For the first evaluation it relies on black box object detections provided by the sensors. In the second part of the research in object tracking multiple algorithms for object detection on LiDAR data are evaluated for the use in the object tracking framework to ease the reliance on black box implementations. A focus is set on detecting objects from motion, where three different approaches are evaluated for motion estimation in LiDAR data: LiDAR optical flow, evidential dynamic mapping and normal distribution transforms. The thesis contains both theoretical contributions and practical implementation considerations for the presented approaches with a high degree of detail including all necessary derivations. All results are implemented and evaluated on an autonomous vehicle and real-world data. With the developed algorithms autonomous driving is realized for urban areas.
Book Synopsis Random Finite Set Information-theoretic Sensor Control for Autonomous Multi-sensor Multi-object Surveillance by : Keith Allen LeGrand
Download or read book Random Finite Set Information-theoretic Sensor Control for Autonomous Multi-sensor Multi-object Surveillance written by Keith Allen LeGrand and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tracking multiple moving objects in complex environments is a key objective of many robotic and aerospace surveillance systems. In the Bayesian multi-object tracking framework, noisy sensor measurements are assimilated over time to form probabilistic beliefs, namely probability densities, of the multi-object state by virtue of Bayes' rule. This dissertation shows that, using probabilistic beliefs and environmental feedback, intelligent sensors can also optimize the value of information gathered in real time by means of information-driven control. In particular, it is shown that in object tracking applications, sensor actions can be optimized based on the expected reduction in uncertainty or information gain estimated from probabilistic beliefs for future sensor measurements. When compared to traditional estimation problems, the problem of estimating the information value for multi-object surveillance is more challenging due to unknown object-measurement association and unknown object existence. The advent of random finite set (RFS) theory has provided a formalism for quantifying and estimating information gain in multi-object tracking problems. However, direct computation of many relevant RFS functions, including posterior density functions and predicted information gain functions, is often intractable and requires principled approximation. This dissertation presents new theory, approximations, and algorithms related to autonomous multi-sensor multi-object surveillance. A new approach is presented for systematically incorporating ambiguous inclusion/exclusion type evidence, such as the non-detection of an object within a known sensor field-of-view (FoV). The resulting state estimation problem is nonlinear and solved using a new Gaussian mixture approximation achieved through recursive component splitting.Based on this approximation, a novel Gaussian mixture Bernoulli filter for imprecise measurements is derived. The filter can accommodate "soft" data from human sources and is demonstrated in a tracking problem using only natural language statements as inputs. This dissertation further investigates the relationship between bounded FoVs and cardinality distributions for a representative selection of multi-object distributions. These new FoV cardinality distributions can be used for sensor planning, as is demonstrated through a problem involving a multi-Bernoulli process with up to one hundred potential objects. Finally, a new tractable approximation is presented for RFS expected information gain that is applicable to sensor control in multi-sensor multi-object search-while-tracking problems. Unlike existing RFS approaches, the approximation presented in this dissertation accounts for multiple measurement outcomes due to noise, missed detections, false alarms, and object appearance/disappearance. The effectiveness of the information-driven sensor control is demonstrated through a multi-vehicle search-while-tracking experiment using real video data from a remote optical sensor.
Book Synopsis 3D Object Detection and Tracking for Autonomous Vehicles by : Su Pang
Download or read book 3D Object Detection and Tracking for Autonomous Vehicles written by Su Pang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous driving systems require accurate 3D object detection and tracking to achieve reliable path planning and navigation. For object detection, there have been significant advances in neural networks for single-modality approaches. However, it has been surprisingly difficult to train networks to use multiple modalities in a way that demonstrates gain over single-modality networks. In this dissertation, we first propose three networks for Camera-LiDAR and Camera-Radar fusion. For Camera-LiDAR fusion, CLOCs (Camera-LiDAR Object Candidates fusion) and Fast-CLOCs are presented. CLOCs fusion provides a multi-modal fusion framework that significantly improves the performance of single-modality detectors. CLOCs operates on the combined output candidates before Non-Maximum Suppression (NMS) of any 2D and any 3D detector, and is trained to leverage their geometric and semantic consistencies to produce more accurate 3D detection results. Fast-CLOCs can run in near real-time with less computational requirements compared to CLOCs. Fast-CLOCs eliminates the separate heavy 2D detector, and instead uses a 3D detector-cued 2D image detector (3D-Q-2D) to reduce memory and computation. For Camera-Radar fusion, we propose TransCAR, a Transformer-based Camera-And-Radar fusion solution for 3D object detection. The cross-attention layer within the transformer decoder can adaptively learn the soft-association between the radar features and vision queries instead of hard-association based on sensor calibration only. Then, we propose to solve the 3D multiple object tracking (MOT) problem for autonomous driving applications using a random finite set-based (RFS) Multiple Measurement Models filter (RFS-M3). In particular, we propose multiple measurement models for a Poisson multi-Bernoulli mixture (PMBM) filter in support of different application scenarios. Our RFS-M3 filter can naturally model these uncertainties accurately and elegantly. We combine learning-based detections with our RFS-M3 tracker by incorporating the detection confidence score into the PMBM prediction and update step. We have evaluated our CLOCs, Fast-CLOCs and TransCAR fusion-based 3D detector and RFS-M3 3D tracker using challenging datasets including KITTI, nuScenes, Argoverse and Waymo that are released by academia and industry leaders. Superior experimental results demonstrated the effectiveness of the proposed approaches.
Book Synopsis Determining Spatial Relevancy of Objects for Improved Development of Multi-object Tracking in Autonomous Driving Systems by :
Download or read book Determining Spatial Relevancy of Objects for Improved Development of Multi-object Tracking in Autonomous Driving Systems written by and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The perception system for autonomous vehicles (AVs) typically outputs all the objects it can observe in a scene. This is significantly more objects than what the AV would interact with, and far more than any human driver focuses on during a driving task. Validating the perception system on all the observed objects could penalize its performance based on objects that will never interact with the AV or affect its planned trajectory. This dissertation outlines a strategy for identifying a subset of objects, referred to as Spatially Relevant Objects (SRO), that the perception system must perform exceptionally well on. This is valuable for several reasons and has many applications. For example, it can be used to determine the set of objects that should be included in the verification dataset for the perception system and thereby have a more efficient development cycle. Additionally, when evaluating the perception system on the SRO subset, the computed metrics not only evaluate the performance but also consider the real-world safety of the perception system, and the results would heavily support the safety case arguments. Finally, determining spatially relevant objects using a representative dataset then plotting their footprints relative to the AV can help determine and confirm the necessary sensing requirements and field of view coverage by prioritizing areas where SROs are most likely to appear. This is done without ignoring the fact that the world contains objects of different classes with different kinematics and could behave in a non-compliant way. The preliminary finding of applying our system showed that we can measure the performance of the perception system using a subset that averages about 11% of the observed objects without compromising the safety of the AV.
Book Synopsis Autonomous Intelligent Vehicles by : Hong Cheng
Download or read book Autonomous Intelligent Vehicles written by Hong Cheng and published by Springer Science & Business Media. This book was released on 2011-11-15 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: This important text/reference presents state-of-the-art research on intelligent vehicles, covering not only topics of object/obstacle detection and recognition, but also aspects of vehicle motion control. With an emphasis on both high-level concepts, and practical detail, the text links theory, algorithms, and issues of hardware and software implementation in intelligent vehicle research. Topics and features: presents a thorough introduction to the development and latest progress in intelligent vehicle research, and proposes a basic framework; provides detection and tracking algorithms for structured and unstructured roads, as well as on-road vehicle detection and tracking algorithms using boosted Gabor features; discusses an approach for multiple sensor-based multiple-object tracking, in addition to an integrated DGPS/IMU positioning approach; examines a vehicle navigation approach using global views; introduces algorithms for lateral and longitudinal vehicle motion control.
Book Synopsis Robust Environmental Perception and Reliability Control for Intelligent Vehicles by : Huihui Pan (Of Haerbin gong ye da xue)
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."--
Book Synopsis Advances in Physical Agents II by : Luis M. Bergasa
Download or read book Advances in Physical Agents II written by Luis M. Bergasa and published by Springer Nature. This book was released on 2020-11-02 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book reports on cutting-edge Artificial Intelligence (AI) theories and methods aimed at the control and coordination of agents acting and moving in a dynamic environment. It covers a wide range of topics relating to: autonomous navigation, localization and mapping; mobile and social robots; multiagent systems; human-robot interaction; perception systems; and deep-learning techniques applied to the robotics. Based on the 21st edition of the International Workshop of Physical Agents (WAF 2020), held virtually on November 19-20, 2020, from Alcalá de Henares, Madrid, Spain, this book offers a snapshot of the state-of-the-art in the field of physical agents, with a special emphasis on novel AI techniques in perception, navigation and human robot interaction for autonomous systems.
Book Synopsis Interlacing Self-Localization, Moving Object Tracking and Mapping for 3D Range Sensors by : Frank Moosmann
Download or read book Interlacing Self-Localization, Moving Object Tracking and Mapping for 3D Range Sensors written by Frank Moosmann and published by KIT Scientific Publishing. This book was released on 2014-05-13 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work presents a solution for autonomous vehicles to detect arbitrary moving traffic participants and to precisely determine the motion of the vehicle. The solution is based on three-dimensional images captured with modern range sensors like e.g. high-resolution laser scanners. As result, objects are tracked and a detailed 3D model is built for each object and for the static environment. The performance is demonstrated in challenging urban environments that contain many different objects.
Book Synopsis Object Detection and Tracking for Autonomous Driving by MATLAB Toolbox by : Mengze Niu
Download or read book Object Detection and Tracking for Autonomous Driving by MATLAB Toolbox written by Mengze Niu and published by . This book was released on 2018 with total page 67 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis introduces the application of MATLAB for object detection in autonomous driving. Detecting moving objects is important for autonomous driving, because it provides information and help behavior understanding. In this thesis, image processing computer version toolbox and automated driving toolbox applied to KITTI dataset [1] and actual experiments in the autonomous driving. Camera Calibrator used for sensor calibration. Kalman filter algorithm applied for object tracking. Some other detection techniques also introduced in the thesis. The first part of this thesis introduces the algorithm of sensor calibration and multiple different objects detection. The second part is to apply the algorithm to KITTI dataset and analyze KITTI data format. Lidar and GPS dataset would also introduce in this part, which may help for next step of sensor fusion. The third part is actual experiment tests in the autonomous driving. The testing roads are between CAR to CAR WEST and campus roads. In addition, a similar dataset like KITTI created according to actual experiments, including tracklet label and sensor calibration.
