Robustness Of Multimodal 3d Object Detection Using Deep Learning Approach For Autonomous Vehicles
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| Author | : Pooya Ramezani |
| Publisher | : |
| Total Pages | : 69 |
| Release | : 2021 |
| Genre | : |
| ISBN | : |
In this thesis, we study the robustness of a multimodal 3D object detection model in the context of autonomous vehicles. Self-driving cars need to accurately detect and localize pedestrians and other vehicles in their 3D surrounding environment to drive on the roads safely. Robustness is one of the most critical aspects of an algorithm in the self-driving car 3D perception problem. Therefore, in this work, we proposed a method to evaluate a 3D object detector’s robustness. To this end, we have trained a representative multimodal 3D object detector on three different datasets. Afterward, we evaluated the trained model on datasets that we have proposed and made to assess the robustness of the trained models in diverse weather and lighting conditions. Our method uses two different approaches for building the proposed datasets for evaluating the robustness. In one approach, we used artificially corrupted images, and in the other one, we used the real images captured in diverse weather and lighting conditions. To detect objects such as cars and pedestrians in the traffic scenes, the multimodal model relies on images and 3D point clouds. Multimodal approaches for 3D object detection exploit different sensors such as camera and range detectors for detecting the objects of interest in the surrounding environment. We leveraged three well-known datasets in the domain of autonomous driving consist of KITTI, nuScenes, and Waymo. We conducted extensive experiments to investigate the proposed method for evaluating the model’s robustness and provided quantitative and qualitative results. We observed that our proposed method can measure the robustness of the model effectively.
| Author | : Xinyu Zhang |
| Publisher | : Springer Nature |
| Total Pages | : 237 |
| Release | : |
| Genre | : |
| ISBN | : 9819932807 |
| Author | : Huihui Pan |
| Publisher | : Springer Nature |
| Total Pages | : 308 |
| Release | : 2023-11-25 |
| Genre | : Technology & Engineering |
| ISBN | : 9819977908 |
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.
| Author | : Yahya Massoud |
| Publisher | : |
| Total Pages | : |
| Release | : 2021 |
| Genre | : |
| ISBN | : |
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 | : Rui Fan |
| Publisher | : Springer Nature |
| Total Pages | : 391 |
| Release | : 2023-10-06 |
| Genre | : Technology & Engineering |
| ISBN | : 981994287X |
Discover the captivating world of computer vision and deep learning for autonomous driving with our comprehensive and in-depth guide. Immerse yourself in an in-depth exploration of cutting-edge topics, carefully crafted to engage tertiary students and ignite the curiosity of researchers and professionals in the field. From fundamental principles to practical applications, this comprehensive guide offers a gentle introduction, expert evaluations of state-of-the-art methods, and inspiring research directions. With a broad range of topics covered, it is also an invaluable resource for university programs offering computer vision and deep learning courses. This book provides clear and simplified algorithm descriptions, making it easy for beginners to understand the complex concepts. We also include carefully selected problems and examples to help reinforce your learning. Don't miss out on this essential guide to computer vision and deep learning for autonomous driving.
| Author | : Michael Yang |
| Publisher | : Academic Press |
| Total Pages | : 422 |
| Release | : 2019-07-16 |
| Genre | : Computers |
| ISBN | : 0128173599 |
Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms. Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful. Contains state-of-the-art developments on multi-modal computing Shines a focus on algorithms and applications Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning
| Author | : Venkateshwaran Balasubramanian |
| Publisher | : |
| Total Pages | : 72 |
| Release | : 2019 |
| Genre | : Automated guided vehicle systems |
| ISBN | : |
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.
| Author | : Martin Simon |
| Publisher | : BoD – Books on Demand |
| Total Pages | : 194 |
| Release | : 2023-01-01 |
| Genre | : Computers |
| ISBN | : 3863602722 |
Autonomous self-driving cars need a very precise perception system of their environment, working for every conceivable scenario. Therefore, different kinds of sensor types, such as lidar scanners, are in use. This thesis contributes highly efficient algorithms for 3D object recognition to the scientific community. It provides a Deep Neural Network with specific layers and a novel loss to safely localize and estimate the orientation of objects from point clouds originating from lidar sensors. First, a single-shot 3D object detector is developed that outputs dense predictions in only one forward pass. Next, this detector is refined by fusing complementary semantic features from cameras and joint probabilistic tracking to stabilize predictions and filter outliers. The last part presents an evaluation of data from automotive-grade lidar scanners. A Generative Adversarial Network is also being developed as an alternative for target-specific artificial data generation.
| Author | : Sampo Kuutti |
| Publisher | : Springer Nature |
| Total Pages | : 70 |
| Release | : 2022-06-01 |
| Genre | : Technology & Engineering |
| ISBN | : 3031015029 |
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 | : Melissa Mozifian |
| Publisher | : |
| Total Pages | : 72 |
| Release | : 2018 |
| Genre | : Artificial intelligence |
| ISBN | : |
This thesis focuses on advancing the state-of-the-art 3D object detection and localization in autonomous driving. An autonomous vehicle requires operating within a very unpredictable and dynamic environment. Hence a robust perception system is essential. This work proposes a novel architecture, AVOD, an \textbf{A}ggregate \textbf{V}iew \textbf{O}bject \textbf{D}etection architecture for autonomous driving capable of generating accurate 3D bounding boxes on road scenes. AVOD uses LIDAR point clouds and RGB images to generate features that are shared by two subnetworks: a region proposal network (RPN) and a second stage detector network. The proposed RPN uses a novel architecture capable of performing multimodal feature fusion on high resolution feature maps to generate reliable 3D object proposals for multiple object classes in road scenes. Using these proposals, the second stage detection network performs accurate oriented 3D bounding box regression and category classification to predict the extents, orientation, and classification of objects in 3D space. AVOD is differentiated from the state-of-the-art by using a high resolution feature extractor coupled with a multimodal fusion RPN architecture, and is therefore able to produce accurate region proposals for small classes in road scenes. AVOD also employs explicit orientation vector regression to resolve the ambiguous orientation estimate inferred from a bounding box. Experiments on the challenging KITTI dataset show the superiority of AVOD over the state-of-the-art detectors on the 3D localization, orientation estimation, and category classification tasks. Finally, AVOD is shown to run in real time and with a low memory overhead. The robustness of AVOD is also visually demonstrated when deployed on our autonomous vehicle operating under low lighting conditions such as night time as well as in snowy scenes. Furthermore, AVOD-SSD is proposed as a 3D Single Stage Detector. This work demonstrates how a single stage detector can achieve similar accuracy as that of a two-stage detector. An analysis of speed and accuracy trade-offs between AVOD and AVOD-SSD are presented.
