Deep Learning Based Multi Modal Perception And Semi Automatic Labelling Algorithms For Automotive Sensor Data
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Author |
: Christian Haase-Schütz |
Publisher |
: BoD – Books on Demand |
Total Pages |
: 334 |
Release |
: 2023 |
ISBN-10 |
: 9783737611565 |
ISBN-13 |
: 3737611564 |
Rating |
: 4/5 (65 Downloads) |
Synopsis Deep Learning Based Multi-modal Perception and Semi-automatic Labelling Algorithms for Automotive Sensor Data by : Christian Haase-Schütz
Author |
: Xinyu Zhang |
Publisher |
: Springer Nature |
Total Pages |
: 237 |
Release |
: |
ISBN-10 |
: 9789819932801 |
ISBN-13 |
: 9819932807 |
Rating |
: 4/5 (01 Downloads) |
Synopsis Multi-sensor Fusion for Autonomous Driving by : Xinyu Zhang
Author |
: Blaž Škrlj |
Publisher |
: Springer Nature |
Total Pages |
: 78 |
Release |
: |
ISBN-10 |
: 9783031570162 |
ISBN-13 |
: 3031570162 |
Rating |
: 4/5 (62 Downloads) |
Synopsis From Unimodal to Multimodal Machine Learning by : Blaž Škrlj
Author |
: Xinghua Liu |
Publisher |
: John Wiley & Sons |
Total Pages |
: 228 |
Release |
: 2022-09-21 |
ISBN-10 |
: 9781119876014 |
ISBN-13 |
: 111987601X |
Rating |
: 4/5 (14 Downloads) |
Synopsis Multimodal Perception and Secure State Estimation for Robotic Mobility Platforms by : Xinghua Liu
Multimodal Perception and Secure State Estimation for Robotic Mobility Platforms Enables readers to understand important new trends in multimodal perception for mobile robotics This book provides a novel perspective on secure state estimation and multimodal perception for robotic mobility platforms such as autonomous vehicles. It thoroughly evaluates filter-based secure dynamic pose estimation approaches for autonomous vehicles over multiple attack signals and shows that they outperform conventional Kalman filtered results. As a modern learning resource, it contains extensive simulative and experimental results that have been successfully implemented on various models and real platforms. To aid in reader comprehension, detailed and illustrative examples on algorithm implementation and performance evaluation are also presented. Written by four qualified authors in the field, sample topics covered in the book include: Secure state estimation that focuses on system robustness under cyber-attacks Multi-sensor fusion that helps improve system performance based on the complementary characteristics of different sensors A geometric pose estimation framework to incorporate measurements and constraints into a unified fusion scheme, which has been validated using public and self-collected data How to achieve real-time road-constrained and heading-assisted pose estimation This book will appeal to graduate-level students and professionals in the fields of ground vehicle pose estimation and perception who are looking for modern and updated insight into key concepts related to the field of robotic mobility platforms.
Author |
: Sampo Kuutti |
Publisher |
: Morgan & Claypool Publishers |
Total Pages |
: 82 |
Release |
: 2019-08-08 |
ISBN-10 |
: 9781681736082 |
ISBN-13 |
: 168173608X |
Rating |
: 4/5 (82 Downloads) |
Synopsis Deep Learning for Autonomous Vehicle Control by : Sampo Kuutti
The next generation of autonomous vehicles will provide major improvements in traffic flow, fuel efficiency, and vehicle safety. Several challenges currently prevent the deployment of autonomous vehicles, one aspect of which is robust and adaptable vehicle control. Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalizing previously learned rules to new scenarios. For these reasons, the use of deep neural networks for vehicle control has gained significant interest. In this book, we introduce relevant deep learning techniques, discuss recent algorithms applied to autonomous vehicle control, identify strengths and limitations of available methods, discuss research challenges in the field, and provide insights into the future trends in this rapidly evolving field.
Author |
: Yahya Massoud |
Publisher |
: |
Total Pages |
: |
Release |
: 2021 |
ISBN-10 |
: OCLC:1294013407 |
ISBN-13 |
: |
Rating |
: 4/5 (07 Downloads) |
Synopsis Sensor Fusion for 3D Object Detection for Autonomous Vehicles by : Yahya Massoud
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.
Author |
: Tim Fingscheidt |
Publisher |
: Springer Nature |
Total Pages |
: 435 |
Release |
: 2022-07-19 |
ISBN-10 |
: 9783031012334 |
ISBN-13 |
: 303101233X |
Rating |
: 4/5 (34 Downloads) |
Synopsis Deep Neural Networks and Data for Automated Driving by : Tim Fingscheidt
This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence. Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.
Author |
: Shaun Michael Howard |
Publisher |
: |
Total Pages |
: 171 |
Release |
: 2017 |
ISBN-10 |
: OCLC:1026417364 |
ISBN-13 |
: |
Rating |
: 4/5 (64 Downloads) |
Synopsis Deep Learning for Sensor Fusion by : Shaun Michael Howard
The use of multiple sensors in modern day vehicular applications is necessary to provide a complete outlook of surroundings for advanced driver assistance systems (ADAS) and automated driving. The fusion of these sensors provides increased certainty in the recognition, localization and prediction of surroundings. A deep learning-based sensor fusion system is proposed to fuse two independent, multi-modal sensor sources. This system is shown to successfully learn the complex capabilities of an existing state-of-the-art sensor fusion system and generalize well to new sensor fusion datasets. It has high precision and recall with minimal confusion after training on several million examples of labeled multi-modal sensor data. It is robust, has a sustainable training time, and has real-time response capabilities on a deep learning PC with a single NVIDIA GeForce GTX 980Ti graphical processing unit (GPU).
Author |
: Yasser Khalil |
Publisher |
: |
Total Pages |
: |
Release |
: 2022 |
ISBN-10 |
: OCLC:1332540823 |
ISBN-13 |
: |
Rating |
: 4/5 (23 Downloads) |
Synopsis Exploiting Multi-Modal Fusion for Urban Autonomous Driving Using Latent Deep Reinforcement Learning by : Yasser Khalil
Human driving decisions are the leading cause of road fatalities. Autonomous driving naturally eliminates such incompetent decisions and thus can improve traffic safety and efficiency. Deep reinforcement learning (DRL) has shown great potential in learning complex tasks. Recently, researchers investigated various DRL-based approaches for autonomous driving. However, exploiting multi-modal fusion to generate pixel-wise perception and motion prediction and then leveraging these predictions to train a latent DRL has not been targeted yet. Unlike other DRL algorithms, the latent DRL algorithm distinguishes representation learning from task learning, enhancing sampling efficiency for reinforcement learning. In addition, supplying the latent DRL algorithm with accurate perception and motion prediction simplifies the surrounding urban scenes, improving training and thus learning a better driving policy. To that end, this Ph.D. research initially develops LiCaNext, a novel real-time multi-modal fusion network to produce accurate joint perception and motion prediction at a pixel level. Our proposed approach relies merely on a LIDAR sensor, where its multi-modal input is composed of bird's-eye view (BEV), range view (RV), and range residual images. Further, this Ph.D. thesis proposes leveraging these predictions with another simple BEV image to train a sequential latent maximum entropy reinforcement learning (MaxEnt RL) algorithm. A sequential latent model is deployed to learn a more compact latent representation from high-dimensional inputs. Subsequently, the MaxEnt RL model trains on this latent space to learn a driving policy. The proposed LiCaNext is trained on the public nuScenes dataset. Results demonstrated that LiCaNext operates in real-time and performs better than the state-of-the-art in perception and motion prediction, especially for small and distant objects. Furthermore, simulation experiments are conducted on CARLA to evaluate the performance of our proposed approach that exploits LiCaNext predictions to train sequential latent MaxEnt RL algorithm. The simulated experiments manifest that our proposed approach learns a better driving policy outperforming other prevalent DRL-based algorithms. The learned driving policy achieves the objectives of safety, efficiency, and comfort. Experiments also reveal that the learned policy maintains its effectiveness under different environments and varying weather conditions.
Author |
: Stephen L. Johnston |
Publisher |
: |
Total Pages |
: 686 |
Release |
: 1980 |
ISBN-10 |
: STANFORD:36105030536945 |
ISBN-13 |
: |
Rating |
: 4/5 (45 Downloads) |
Synopsis Millimeter Wave Radar by : Stephen L. Johnston