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Adding Temporal Information To Lidar Semantic Segmentation For Autonomous Vehicles
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Book Synopsis Adding Temporal Information to LiDAR Semantic Segmentation for Autonomous Vehicles by : Mohammed Anany
Download or read book Adding Temporal Information to LiDAR Semantic Segmentation for Autonomous Vehicles written by Mohammed Anany and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Semantic segmentation is an essential technique to achieve scene understanding for various domains and applications. Particularly, it is of crucial importance in autonomous driving applications. Autonomous vehicles usually rely on cameras and light detection and ranging (LiDAR) sensors to gain contextual information from the environment. Semantic segmentation has been employed to process images and point clouds that were captured from cameras and LiDAR sensors respectively. One important research direction to consider is investigating the impact of utilizing temporal information in the domain of semantic segmentation. Many contributions exist in the field with regards to utilizing temporal information for semantic segmentation on 2D images. However, few studies tackled the usage of temporal information for semantic segmentation on 3D point clouds. Recent studies experimented with scan clustering and bayes filters, however, none were conducted using recurrent networks. Various techniques of semantic segmentation of 3D point clouds are explored, and the best fit to serve as baseline was SqueezeSeg V2. In this work, we introduce a Convolutional-LSTM layer in the model and adjust the "skip" connectors in the architecture, resulting in a mean Intersection over Union (mIoU) of 36%, which improves on the baseline by almost 3%. Recently, we repeated the same experiment on SqueezeSeg V3, a recently published network, which achieved a mIoU of 45.3, improving on its baseline by 2.13%. These results were obtained using sequences 00 to 10 of Semantic KITTI dataset.
Book Synopsis Camera and LiDAR Fusion For Point Cloud Semantic Segmentation by : Ali Abdelkader
Download or read book Camera and LiDAR Fusion For Point Cloud Semantic Segmentation written by Ali Abdelkader and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Perception is a fundamental component of any autonomous driving system. Semantic segmentation is the perception task of assigning semantic class labels to sensor inputs. While autonomous driving systems are currently equipped with a suite of sensors, much focus in the literature has been on semantic segmentation of camera images only. Research in the fusion of different sensor modalities for semantic segmentation has not been investigated as much. Deep learning models based on transformer architectures have proven successful in many tasks in computer vision and natural language processing. This work explores the use of deep learning transformers to fuse information from LiDAR and camera sensors to improve the segmentation of LiDAR point clouds. It also addresses the question of which fusion level in this deep learning framework provides better performance. This was done following an empirical approach in which different fusion models were designed and evaluated against each other using SemanticKITTI dataset.
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 Performance Enhancement of Wide-range Perception Issues for Autonomous Vehicles by : Suvash Sharma
Download or read book Performance Enhancement of Wide-range Perception Issues for Autonomous Vehicles written by Suvash Sharma and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the mission-critical nature of the autonomous driving application, underlying algorithms for scene understanding should be given special care during their development. Mostly, they should be designed with precise consideration of accuracy and run-time. Accuracy should be considered strictly which if compromised leads to faulty interpretation of the environment that may ultimately result in accidental scenarios. On the other hand, run-time holds an important position as the delayed understanding of the scene would hamper the real-time response of the vehicle which again leads to unforeseen accidental cases. These factors come as the functions of several factors such as the design and complexity of the algorithms, nature of the encountered objects or events in the environment, weather-induced effects, etc. In this work, several novel scene understanding algorithms in terms- of semantic segmentation are devised. First, a transfer learning technique is proposed in order to transfer the knowledge from the data-rich domain to a data-scarce off-road driving domain for semantic segmentation such that the learned information is efficiently transferred from one domain to another while reducing run-time and increasing the accuracy. Second, the performance of several segmentation algorithms is assessed under the easy-to-severe rainy condition and two methods for achieving the robustness are proposed. Third, a new method of eradicating the rain from the input images is proposed. Since autonomous vehicles are rich in sensors and each of them has the capability of representing different types of information, it is worth fusing the information from all the possible sensors. Forth, a fusion mechanism with a novel algorithm that facilitates the use of local and non-local attention in a cross-modal scenario with RGB camera images and lidar-based images for road detection using semantic segmentation is executed and validated for different driving scenarios. Fifth, a conceptually new method of off-road driving trail representation, called Traversability, is introduced. To establish the correlation between a vehicle’s capability and the level of difficulty of the driving trail, a new dataset called CaT (CAVS Traversability) is introduced. This dataset is very helpful for future research in several off-road driving applications including military purposes, robotic navigation, etc.
Book Synopsis Improving LiDAR Semantic Segmentation on Minority Classes and Generalization Capability for Autonomous Driving by :
Download or read book Improving LiDAR Semantic Segmentation on Minority Classes and Generalization Capability for Autonomous Driving written by and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Multimodal Panoptic Segmentation of 3D Point Clouds by : Dürr, Fabian
Download or read book Multimodal Panoptic Segmentation of 3D Point Clouds written by Dürr, Fabian and published by KIT Scientific Publishing. This book was released on 2023-10-09 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: The understanding and interpretation of complex 3D environments is a key challenge of autonomous driving. Lidar sensors and their recorded point clouds are particularly interesting for this challenge since they provide accurate 3D information about the environment. This work presents a multimodal approach based on deep learning for panoptic segmentation of 3D point clouds. It builds upon and combines the three key aspects multi view architecture, temporal feature fusion, and deep sensor fusion.
Book Synopsis Creating Autonomous Vehicle Systems by : Shaoshan Liu
Download or read book Creating Autonomous Vehicle Systems written by Shaoshan Liu and published by Morgan & Claypool Publishers. This book was released on 2017-10-25 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first technical overview of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences of creating autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions about its actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, we are able to test new algorithms and update the HD map—plus, train better recognition, tracking, and decision models. This book consists of nine chapters. Chapter 1 provides an overview of autonomous vehicle systems; Chapter 2 focuses on localization technologies; Chapter 3 discusses traditional techniques used for perception; Chapter 4 discusses deep learning based techniques for perception; Chapter 5 introduces the planning and control sub-system, especially prediction and routing technologies; Chapter 6 focuses on motion planning and feedback control of the planning and control subsystem; Chapter 7 introduces reinforcement learning-based planning and control; Chapter 8 delves into the details of client systems design; and Chapter 9 provides the details of cloud platforms for autonomous driving. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find plenty of references for an effective, deeper exploration of the various technologies.
Book Synopsis Local and Cooperative Autonomous Vehicle Perception from Synthetic Datasets by : Braden Hurl
Download or read book Local and Cooperative Autonomous Vehicle Perception from Synthetic Datasets written by Braden Hurl and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this work is to increase the performance of autonomous vehicle 3D object detection using synthetic data. This work introduces the Precise Synthetic Image and LiDAR (PreSIL) dataset for autonomous vehicle perception. Grand Theft Auto V (GTA V), a commercial video game, has a large, detailed world with realistic graphics, which provides a diverse data collection environment. Existing works creating synthetic Light Detection and Ranging (LiDAR) data for autonomous driving with GTA V have not released their datasets, rely on an in-game raycasting function which represents people as cylinders, and can fail to capture vehicles past 30 metres. This work describes a novel LiDAR simulator within GTA V which collides with detailed models for all entities no matter the type or position. The PreSIL dataset consists of over 50,000 frames and includes high-definition images with full resolution depth information, semantic segmentation (images), point-wise segmentation (point clouds), and detailed annotations for all vehicles and people. Collecting additional data with the PreSIL framework is entirely automatic and requires no human intervention of any kind. The effectiveness of the PreSIL dataset is demonstrated by showing an improvement of up to 5% average precision on the KITTI 3D Object Detection benchmark challenge when state-of-the-art 3D object detection networks are pre-trained with the PreSIL dataset. The PreSIL dataset and generation code are available at https://tinyurl.com/y3tb9sxy Synthetic data also enables data generation which is genuinely hard to create in the real world. In the next major chapter of this thesis, a new synthethic dataset, the TruPercept dataset, is created with perceptual information from multiple viewpoints. A novel system is proposed for cooperative perception, perception including information from multiple viewpoints. The TruPercept model is presented. TruPercept integrates trust modelling for vehicular ad hoc networks (VANETs) with information from perception, with a focus on 3D object detection. A discussion is presented on how this might create a safer driving experience for fully autonomous vehicles. The TruPercept dataset is used to experimentally evaluate the TruPercept model against traditional local perception (single viewpoint) models. The TruPercept model is also contrasted with existing methods for trust modeling used in ad hoc network environments. This thesis also offers insights into how V2V communication for perception can be managed through trust modeling, aiming to improve object detection accuracy, across contexts with varying ease of observability. The TruPercept model and data are available at https://tinyurl.com/y2nwy52o.
Book Synopsis Spatiotemporal Occupancy Prediction for Autonomous Driving by : Maneekwan Toyungyernsub
Download or read book Spatiotemporal Occupancy Prediction for Autonomous Driving written by Maneekwan Toyungyernsub and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advancements in robotics, computer vision, machine learning and hardware have contributed to impressive developments of autonomous vehicles. However, there still exist challenges that must be tackled in order for the autonomous vehicles to be safely and seamlessly integrated into human environments. This is particularly the case in dense and cluttered urban settings. Autonomous vehicles must be able to understand and anticipate how their surroundings will evolve in both time and space. This capability will allow the autonomous vehicles to proactively plan safe trajectories and avoid other traffic agents. A common prediction approach is an agent-centric method (e.g., pedestrian or vehicle trajectory prediction). These methods require detection and tracking of all agents in the environment since trajectory prediction is performed on each agent. An alternative approach is a map-based (e.g., occupancy grid map) prediction method where the entire environment is discretized into grid cells and the collective occupancy probabilities for each grid cell are predicted. Hence, object detection and tracking capability is generally not needed. This makes a map-based occupancy prediction approach more robust to partial object occlusions and is capable of handling any arbitrary number of agents in the environments. However, a common problem with occupancy grid map prediction is the vanishing of objects from the predictions, especially at longer time horizons. In this thesis, we consider the problem of spatiotemporal environment prediction in urban environments. We merge tools from robotics, computer vision and deep learning to develop spatiotemporal occupancy prediction frameworks that leverage environment information. In our first research work, we developed an occupancy prediction methodology that leverages environment dynamic information, in terms of static-dynamic parts of the environment. Our model learns to predict the spatiotemporal evolution of the static and dynamic parts of the environment input separately and outputs the final occupancy grid map predictions of the entire environment. In our second research work, we further developed the prediction framework to be modular, by adding a learning-based static-dynamic segmentation module upstream of the occupancy prediction module. The addition addressed previous limitations that require the static and dynamic parts of the environment to be known in advance. Lastly, we developed an environment prediction framework that leverages environment semantic information. Our proposed model consists of two sub-modules, which are future semantic segmentation prediction and occupancy prediction. We proposed to represent environment semantics in the form of semantic gird maps that are similar to the occupancy grid representation. This allows a direct flow of semantic information to the occupancy prediction sub-module. Experiments validated on the real-world driving dataset show that our methods outperform other state-of-the-art models and reduce the issue of vanishing object in the predictions at longer time horizons.
Book Synopsis Multimodal Spatio-temporal Deep Learning Framework for 3D Object Detection in Instrumented Vehicles by : Venkatesh Gurram Munirathnam
Download or read book Multimodal Spatio-temporal Deep Learning Framework for 3D Object Detection in Instrumented Vehicles written by Venkatesh Gurram Munirathnam and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents the utilization of multiple modalities, such as image and lidar, to incorporate spatio-temporal information from sequence data into deep learning architectures for 3Dobject detection in instrumented vehicles. The race to autonomy in instrumented vehicles or self-driving cars has stimulated significant research in developing autonomous driver assistance systems (ADAS) technologies related explicitly to perception systems. Object detection plays a crucial role in perception systems by providing spatial information to its subsequent modules; hence, accurate detection is a significant task supporting autonomous driving. The advent of deep learning in computer vision applications and the availability of multiple sensing modalities such as 360° imaging, lidar, and radar have led to state-of-the-art 2D and 3Dobject detection architectures. Most current state-of-the-art 3D object detection frameworks consider single-frame reference. However, these methods do not utilize temporal information associated with the objects or scenes from the sequence data. Thus, the present research hypothesizes that multimodal temporal information can contribute to bridging the gap between 2D and 3D metric space by improving the accuracy of deep learning frameworks for 3D object estimations. The thesis presents understanding multimodal data representations and selecting hyper-parameters using public datasets such as KITTI and nuScenes with Frustum-ConvNet as a baseline architecture. Secondly, an attention mechanism was employed along with convolutional-LSTM to extract spatial-temporal information from sequence data to improve 3D estimations and to aid the architecture in focusing on salient lidar point cloud features. Finally, various fusion strategies are applied to fuse the modalities and temporal information into the architecture to assess its efficacy on performance and computational complexity. Overall, this thesis has established the importance and utility of multimodal systems for refined 3D object detection and proposed a complex pipeline incorporating spatial, temporal and attention mechanisms to improve specific, and general class accuracy demonstrated on key autonomous driving data sets.
Book Synopsis Person Re-Identification by : Shaogang Gong
Download or read book Person Re-Identification written by Shaogang Gong and published by Springer Science & Business Media. This book was released on 2014-01-03 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first book of its kind dedicated to the challenge of person re-identification, this text provides an in-depth, multidisciplinary discussion of recent developments and state-of-the-art methods. Features: introduces examples of robust feature representations, reviews salient feature weighting and selection mechanisms and examines the benefits of semantic attributes; describes how to segregate meaningful body parts from background clutter; examines the use of 3D depth images and contextual constraints derived from the visual appearance of a group; reviews approaches to feature transfer function and distance metric learning and discusses potential solutions to issues of data scalability and identity inference; investigates the limitations of existing benchmark datasets, presents strategies for camera topology inference and describes techniques for improving post-rank search efficiency; explores the design rationale and implementation considerations of building a practical re-identification system.
Book Synopsis A Study on the Effect of Multispectral LiDAR Data on Automated Semantic Segmentation of 3D-point Clouds by : Valentin Vierhub-Lorenz
Download or read book A Study on the Effect of Multispectral LiDAR Data on Automated Semantic Segmentation of 3D-point Clouds written by Valentin Vierhub-Lorenz and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Mobile mapping is an application field of ever-increasing relevance. Data of the surrounding environment is typically captured using combinations of LiDAR systems and cameras. The large amounts of measurement data are then processed and interpreted, which is often done automated using neural networks. For the evaluation the data of the LiDAR and the cameras needs to be fused, which requires a reliable calibration of the sensors. Segmentation solemnly on the LiDAR data drastically decreases the amount of data and makes the complex data fusion process obsolete but on the other hand often performs poorly due to the lack of information about the surface remission properties. The work at hand evaluates the effect of a novel multispectral LiDAR system on automated semantic segmentation of 3D-point clouds to overcome this downside. Besides the presentation of the multispectral LiDAR system and its implementation on a mobile mapping vehicle, the point cloud processing and the training of the CNN are described in detail. The results show a significant increase in the mIoU when using the additional information from the multispectral channel compared to just 3D and intensity information. The impact on the IoU was found to be strongly dependent on the class
Book Synopsis Real-time Forward Urban Environment Perception for an Autonomous Ground Vehicle Using Computer Vision and LIDAR by : Christopher Richard Greco
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.
Book Synopsis Deep Learning for Robot Perception and Cognition by : Alexandros Iosifidis
Download or read book Deep Learning for Robot Perception and Cognition written by Alexandros Iosifidis and published by Academic Press. This book was released on 2022-02-04 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. Presents deep learning principles and methodologies Explains the principles of applying end-to-end learning in robotics applications Presents how to design and train deep learning models Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more Uses robotic simulation environments for training deep learning models Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis
Book Synopsis Towards Recognition as a Regularizer in Autonomous Driving by : Aseem Behl
Download or read book Towards Recognition as a Regularizer in Autonomous Driving written by Aseem Behl and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous driving promises great potential for social and economic benefits. While autonomous driving remains an unsolved problem, recent advances in computer vision have enabled considerable progress in development of autonomous vehicles. For instance, recognition methods such as object detection, semantic and instance semantic segmentation affords the self driving vehicles with the precise understanding of their surroundings which is critical to safe autonomous driving. Furthermore, with the advent of deep learning, machine recognition methods have reached human-like performance. Therefore, in this thesis, we propose different methods to exploit semantic cues from state-of-the-art recognition methods to improve performance of other tasks required to solve autonomous driving. To this end, in this thesis, we examine the effectiveness of recognition as a regularizer in two prominent problems in autonomous driving, namely, scene flow estimation and end-to-end learned autonomous driving. Besides recognizing traffic participants and predicting their position, an autonomous car needs to precisely predict their 3D position in the future for tasks like navigation and planning. Scene flow is a prominent representation for such motion estimation where a 3D position and 3D motion vector is associated with every observed surface point in the scene. However, existing methods for 3D scene flow estimation often fail in the presence of large displacement or local ambiguities, e.g., at texture-less or reflective surfaces which are omnipresent in dynamic road scenes. Therefore, first, we address the problem of large displacements or local ambiguities by exploiting recognition cues such as semantic grouping and fine-grained geometric recognition to regularize scene flow estimation. We compute these cues using CNNs trained on a newly annotated dataset of stereo images and integrate them into a CRF-based model for robust 3D scene flow estimation. We also investigate the importance of recognition granularity, from coarse 2D bounding box estimates over 2D instance segmentations to fine-grained 3D object part predictions. From this study, we show that regularization from recognition cues significantly boosts the scene flow accuracy, in particular in challenging foreground regions. Secondly, we conclude that the instance segmentation cue is by far strongest, in our setting. Alongside, we demonstrate the effectiveness of our method on challenging scene flow benchmarks. In contrast to cameras, laser scanners provide a 360 degree field of view with just one sensor, are generally unaffected by lighting conditions, and do not suffer from the quadratic error behavior of stereo cameras. Therefore, second, in this work, we extend the idea of semantic grouping as a regularizer for 3D motion to 3D point cloud measurements from LIDAR sensor observations. We achieve this goal by jointly predicting 3D scene flow as well as the 3D bounding box and semantically grouping the motion vectors using 3D object detections. In order to semantically group the motion vectors using 3D object detections, we need to predict pointiwise rigid motion. We show that the traditional global representation of rigid body motion prohibits inference by CNNs, and propose a translation equivariant representation to circumvent this problem and amenable to CNN learning. For training our deep network, we augment real scans from with virtual objects, realistically modeling occlusions and simulating sensor noise. A thorough comparison with classic and learning-based techniques highlights the robustness of the proposed approach. It is well known that recognition cues such as semantic segmentation can be used as an effective intermediate representation to regularize learning end-to-end learned driving policies. However, the task of street scene semantic segmentation requires expensive annotations. Furthermore, segmentation algorithms are often trained irrespective of the actual driving task, using auxiliary image-space loss functions which are not guaranteed to maximize driving metrics such as safety or distance traveled per intervention. Therefore, third, in this work, we analyze several recognition-based intermediate representations for end-to-end learned driving policies. We seek to quantify the impact of reducing segmentation annotation costs on learned behavior cloning agents. We systematically study the trade-off between annotation efficiency and driving performance, i.e., the types of classes labeled, the number of image samples used to learn the visual abstraction model, and their granularity (e.g., object masks vs. 2D bounding boxes). Our analysis uncovers several practical insights into how segmentation-based visual abstractions can be exploited in a more label efficient manner. Surprisingly, we find that state-of-the-art driving performance can be achieved with orders of magnitude reduction in annotation cost.
Book Synopsis Real-time Traffic Monitoring Using Airborne Lidar by : Rafael Akio Alves Watanabe
Download or read book Real-time Traffic Monitoring Using Airborne Lidar written by Rafael Akio Alves Watanabe and published by . This book was released on 2019 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: The expansion of deployed traffic monitoring systems and information transmission is a crucial step towards increasing the efficiency, reliability and safety of vehicular transportation. Under the current context in terrestrial transportation, the implementation of real-time traffic analysis mechanisms can provide more insight into the network, leading to better informed decisions on how to direct traffic and plan roadways. In the future, as we move towards the integration of fully autonomous vehicles to a transportation network with human drivers, the extraction and processing of real time data will become even more crucial to ensure safe transition. In this paper we present a real time data analysis system for in-flight vehicle detection as an option for the expansion of traffic monitoring. The presented solution is able to perform typical post-flight processing in real time, with minimal computational and power requirements, which allows its implementation on light-weight UAS. It utilizes adaptive segmentation and 3D convolutions that take advantage of the structure of the LiDAR point cloud, to identify vehicles and their respective positions within 3D point cloud segments that may include background clutter. All the necessary positioning information required to run the algorithm are introduced along with a detailed description for the computational steps extracting the desired features from the raw data. We provide the timing constraints for the system and evaluate its performance while considering different optimization variables and computation capabilities.
