查看更多>>摘要：In the constantly changing realm of logistics and transportation management, the incorporation of Information and Communication Technology (ICT) has catalyzed transformative paradigm shifts in the approach and resolution of Vehicle Route Optimization (VRO). The current scientometric research paper embarks on a comprehensive exploration of the scholarly endeavors acquired from the Scopus database in the realm of ICT-assisted vehicle route optimization, spanning 2014–2023. The scientometric implications of the article encompass several pivotal dimensions, including publication patterns, (author, country, institution) co-authorship, geographical distribution, Document Co-citation network Analysis (DCA) and top articles based on betweenness centrality corresponding to each opted category of the current knowledge domain. A meticulous examination of the analyses revealed a significant research impact in the pervasive computing and communication technology categories. The co-authorship analysis presenting the interconnectedness of collaborative efforts across countries, authors, and institutions highlights the authors and universities of China and the United States as dominant players in the domain. The DCA elucidates research themes, including intelligent transportation systems, unmanned aerial vehicle-based wireless sensor networks, electric vehicle-based sustainable VRO, and vehicular ad-hoc networks. These themes underscore the current research trajectories within the field. Notably, quantum computing and blockchain emerged as prominent technologies. Overall, the study unveils the transformative impact of ICT on VRO, highlighting the key themes, future research directions and a collaborative research community poised for substantial innovation in this area of research.
查看更多>>摘要：Detecting small objects in optical images and videos is a significant challenge in numerous intelligent transportation and autonomous systems. State-of-the-art generic object detection methods fail to accurately localize and identify such small objects (e.g., pedestrians, small vehicles, obstacles). Because small objects occupy only a small area in the input image (e.g., $32 \times 32$ pixels or less), the information extracted from such a small area is not always rich enough to support decision-making. Multidisciplinary strategies are being developed by researchers working at the interface of deep learning and computer vision to enhance the performance of Small Object Detection (SOD). In this paper, we provide a comprehensive review of over 160 research papers published between 2017 and 2022 in order to survey this growing subject. This paper summarizes the existing literature and provides a taxonomy that illustrates the broad picture of current research. We further explore methods to boost the performance of small object detection in maritime settings, where enhanced performance is crucial for ensuring safety and managing traffic. Detecting small objects in the maritime environment requires additional considerations and the current survey aims to review the advanced techniques addressing those aspects. In addition, the popular SOD datasets for generic and maritime applications are discussed, and also well-known evaluation metrics for the state-of-the-art methods on some of the datasets are provided. The link to these datasets appears in https://github.com/arekavandi/Datasets_SOD.
查看更多>>摘要：In the past decade, deep learning has been widely applied across various fields. However, its applicability in open-world scenarios is often limited due to the lack of quantifying uncertainty in both data and models. In recent years, a multitude of uncertainty quantification (UQ) approaches for neural networks have emerged and found applications in safety-critical domains such as autonomous vehicles and medical analysis. This paper aims to review the latest advancements in UQ methods and investigate their application specifically in the field of computer vision and autonomous vehicles. Initially, we identify several key qualifications, namely practicability, robustness, accuracy, scalability, and efficiency (referred to as PRASE), and employ them as evaluation criteria throughout this study. By considering these criteria as uniform measurements, we meticulously evaluate and compare the performance of different types of UQ methods, including Bayesian methods, ensemble methods, and single deterministic methods. Furthermore, we delve into the discussion of their application in diverse tasks within the autonomous vehicle domain, such as semantic segmentation, object detection, depth estimation, and end-to-end control. Through comprehensive analysis and comparison, we identify a range of challenges and propose future research directions in this field. Our findings shed light on the importance of addressing uncertainty quantification in deep learning models and provide insights into enhancing the reliability and performance of autonomous vehicles in real-world scenarios.
查看更多>>摘要：Accurate and real-time acquisition of vehicular system dynamic states, road surface conditions, and motion states of surrounding participants is crucial for the safety, passenger comfort, and operational efficiency of autonomous vehicles (AVs) and connected automated vehicles (CAVs). In recent years, a significant amount of research has contributed to the field of state estimation for vehicles, roads, and pedestrians. From the systemwide perspective of intelligent transportation systems to a focused view on “vehicle-road-pedestrian”, this survey aims to provide a comprehensive review and summary of recent state estimation techniques for vehicle motion, road surface, and pedestrian motion. A thorough analysis of the reviewed literature, relevant datasets, evaluation metrics, and experimental platforms in this field is also conducted. Finally, existing challenges and future research directions about methods and performance evaluation are further discussed. This survey is expected to contribute to the advancement of research in dynamic state estimation of vehicle-road-pedestrian, thereby facilitating the development of efficient and safe intelligent transportation systems.
查看更多>>摘要：The advancements in Intelligent Transportation Systems have brought a heightened focus on safety, driven by innovative solutions like Vehicle-to-Everything (V2X) communication, Advanced Driver Assistance Systems(ADAS), and Cooperative Intelligent Transport Systems (C-ITS). Ensuring the safety of vulnerable road users (VRUs) remains a top priority in the transportation sector, and harnessing these cutting-edge technologies offers immense potential to address this concern effectively. This collaborative approach greatly enhances VRUs safety, reduces accidents, and promotes efficient and sustainable mobility. This paper reviews the latest developments in V2X technology, emphasizing its role in improving VRU safety. It explores current V2X standards, use cases on VRU safety, and the evolving research landscape, particularly in trajectory prediction models. These models are critical for foreseeing potential collisions and mitigating V2X-based data transmission delays. Trajectory prediction models can also offer a promising solution to ongoing challenges such as data association, scalability, and bandwidth requirements. By focusing on trajectory prediction, this paper highlights the vital role of predictive analytics in safeguarding vulnerable road users and advancing transportation safety.
查看更多>>摘要：The safety and efficiency of an Autonomous Vehicle (AV) manoeuvre planning heavily depend on the future trajectories of surrounding vehicles. If an AV can predict its surrounding vehicles’ future trajectories, it can make safe and efficient manoeuvre decisions. In this paper, we present a Deep Spatio-Temporal Context-Aware decision Network (DST-CAN) for predictive manoeuvre decisions for AVs on highways. DST-CAN has two main components, namely spatio-temporal context-aware map generator and predictive manoeuvre decisions engine. DST-CAN employ a memory neuron network to predict the future trajectories of its surrounding vehicles. Using look-ahead prediction and past actual trajectories, a spatio-temporal context-aware probability occupancy map is generated. These context-aware maps as input to a decision engine generate a safe and efficient manoeuvre decision. Here, CNN helps extract feature space, and two fully connected network generates longitudinal and lateral manoeuvre decisions. Performance evaluation of DST-CAN has been carried out using two publicly available NGSIM US-101 and I-80 highway datasets. A traffic rule is defined to generate ground truths for these datasets in addition to human decisions. Two DST-CAN models are trained using imitation learning with human driving decisions from actual traffic data and rule-based ground truth decisions. The performances of the DST-CAN models are compared with the state-of-the-art Convolutional Social-LSTM (CS-LSTM) models for manoeuvre prediction. The results clearly indicate that the context-aware maps help DST-CAN to predict the decision accurately over CS-LSTM. Further, an ablation study has been carried out to understand the effect of prediction horizons of performance and a robustness study to understand the near collision scenarios over actual traffic observations. The context-aware map with a 3 second prediction horizon is robust against near collision.
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NSTL
IEEE