Augmented Ship Tracking Under Occlusion Conditions From Maritime Surveillance Videos

Chen, Xinqiang; Xu, Xueqian; Yang, Yongsheng; Wu, Huafeng; Tang, Jinjun; Zhao, Jiansen

doi:10.1109/access.2020.2978054

Cited by 67 publications

(50 citation statements)

References 27 publications

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“…Future research could expand this study from several aspects. First, shipping is a sustainable transportation mode in terms of both energy saving and pollution prevention, following the existing literature [ 47 – 50 ], the problem in ship development with energy saving may be a potentially meaningful research direction. Second, this paper respectively considers the fairness concern and risk aversion, we in the future may formulate supply chain models considering the fairness concern and risk aversion simultaneously.…”

Section: Discussionmentioning

confidence: 99%

Energy-saving and pricing decisions in a sustainable supply chain considering behavioral concerns

et al. 2020

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With the rising environmental concerns among consumers all over the world, sustainability has received considerable attention, and numerous enterprises are adopting various practices such as investing in energy-saving to improve sustainability in supply chains. However, many previous researches always assume that decision makers are perfectly rational and neglect the behavioral concerns of decision makers. This paper considers a two-stage sustainable supply chain with behavioral concerns in order to develop more realistic models, and mainly focuses on the energy-saving and pricing decisions in the decentralized system, as well as how to improve energy-saving level and profits. We develop decentralized decision-making models under two types of behavioral concerns: fairness concern and risk aversion, and derive the optimal strategy for each member with a Stackelberg game in which the manufacturer acts as the leader. The effect of the behavioral concerns on the optimal decisions and corresponding profits is discussed in detail. Theoretical analysis verified by numerical experiments shows that the fairness behavior always causes a negative effect on the manufacturer, total supply chain, and energy conservation, while it could benefit the retailer in profits. The risk aversion behavior always benefits the manufacturer, total supply chain, and energy conservation, whereas it could make the retailer suffer. Note that both the optimal energy-saving level and corresponding profit of the total supply chain under two types of behavioral concerns are lower than that in the centralized system, thereby we propose a revenue-cost-sharing contract to coordinate the supply chain, under which both the manufacturer and the retailer can achieve a win-win outcome and the energy-saving level can be improved. In addition, some managerial implications through our analytical and numerical results are summarized in this paper.

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Section: Discussionmentioning

confidence: 99%

Energy-saving and pricing decisions in a sustainable supply chain considering behavioral concerns

et al. 2020

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“…For the tracking of the object, the prediction accuracy is deteriorated by noises, and the occlusion should be considered. A kernelized correlation filter was used to explore intrinsic features from background-interfered samples [ 18 ]. During the parking, simultaneous localization and mapping (SLAM) was developed to localize the vehicle at the centimeter level [ 3 ].…”

Section: Related Workmentioning

confidence: 99%

Data Efficient Reinforcement Learning for Integrated Lateral Planning and Control in Automated Parking System

Song

Chen

Sun

et al. 2020

Sensors

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Reinforcement learning (RL) is a promising direction in automated parking systems (APSs), as integrating planning and tracking control using RL can potentially maximize the overall performance. However, commonly used model-free RL requires many interactions to achieve acceptable performance, and model-based RL in APS cannot continuously learn. In this paper, a data-efficient RL method is constructed to learn from data by use of a model-based method. The proposed method uses a truncated Monte Carlo tree search to evaluate parking states and select moves. Two artificial neural networks are trained to provide the search probability of each tree branch and the final reward for each state using self-trained data. The data efficiency is enhanced by weighting exploration with parking trajectory returns, an adaptive exploration scheme, and experience augmentation with imaginary rollouts. Without human demonstrations, a novel training pipeline is also used to train the initial action guidance network and the state value network. Compared with path planning and path-following methods, the proposed integrated method can flexibly co-ordinate the longitudinal and lateral motion to park a smaller parking space in one maneuver. Its adaptability to changes in the vehicle model is verified by joint Carsim and MATLAB simulation, demonstrating that the algorithm converges within a few iterations. Finally, experiments using a real vehicle platform are used to further verify the effectiveness of the proposed method. Compared with obtaining rewards using simulation, the proposed method achieves a better final parking attitude and success rate.

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“…Note that we can obtain the friction coefficient value with two different working logics, which are empirical-like methods and the nonlinear relevant models. Previous studies suggested that solutions obtained by empirical relevant models may not be very robust, which may further degrade traffic modeling accuracy [28][29][30][31][32]. It is noted that the relationship between the friction coefficient and the typical traffic relevant factors (i.e., road conditions, tire structural parameters, and vehicle speed) is a type of nonlinear function.…”

Section: Calculation Of Road Surface Adhesion Coefficientmentioning

confidence: 99%

Research of Vehicle Rear-End Collision Model considering Multiple Factors

Luo

Zang

Yuan

et al. 2020

Mathematical Problems in Engineering

Self Cite

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e accuracy of the rear-end collision models is crucial for the early warning of potential traffic accident identification, and thus analyzes of the main factors influencing the rear-end collision relevant models is an active topic in the field. e previous studies have tried to determine the single factor influence on the rear-end collision model performance. Less attention was paid to exploit mutual influences on the model performance. To bridge the gap, we proposed an improved vehicle rear-end collision model by integrating varied factors which influence two parameters (i.e., response time and road adhesion coefficient). e two parameters were solved with the integrated weighting and neural network models, respectively. After that we analyzed the relationship between varied factors and the minimum car-following distance. e research findings support both the theoretical and practical guidance for transportation regulations to release more reasonable minimum headway distance to enhance the roadway traffic safety.On the basis of the traditional safety distance model, Kong et al. conducted information acquisition, distance judgment, braking, and other processes for active collision avoidance during vehicle travel. However, the judgment model used is relatively simple [5]. Jiang and Yu established a highway antitailing model based on radial basis function neural network by using traditional algorithms [6]. Nevertheless, the model is mainly applied to highways, and the influencing factors are considered to be single. Jing and Wang explored the influence of multiple vehicles on the driver and established a vehicle-following model considering the traffic state based on the full velocity difference (FVD) model [7]. Nonetheless, the model is too complicated to conduct to practical applications. Xia established a fuzzy inference system based on the characteristics (e.g., driving age, and gender) of the driver to determine the response time of different drivers and proposed an improved version of the safe model [8]. Peng and Sun derived the modified Korteweg-de Vries (mKdV) equation to describe the traffic

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Augmented Ship Tracking Under Occlusion Conditions From Maritime Surveillance Videos

Cited by 67 publications

References 27 publications

Energy-saving and pricing decisions in a sustainable supply chain considering behavioral concerns

Energy-saving and pricing decisions in a sustainable supply chain considering behavioral concerns

Data Efficient Reinforcement Learning for Integrated Lateral Planning and Control in Automated Parking System

Research of Vehicle Rear-End Collision Model considering Multiple Factors

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