Optimizing Coordinated Vehicle Platooning: An Analytical Approach Based on Stochastic Dynamic Programming

Platooning connected and autonomous vehicles (CAVs) can improve traffic and fuel efficiency. However, scalable platooning operations require junction-level coordination, which has not been well studied. In this paper, we study the coordination of vehicle platooning at highway junctions. We consider a setting where CAVs randomly arrive at a highway junction according to a general renewal process. When a CAV approaches the junction, a system operator determines whether the CAV will merge into the platoon ahead according to the positions and speeds of the CAV and the platoon. We formulate a Markov decision process to minimize the discounted cumulative travel cost, i.e. fuel consumption plus travel delay, over an infinite time horizon. We show that the optimal policy is threshold-based: the CAV will merge with the platoon if and only if the difference between the CAV's and the platoon's predicted times of arrival at the junction is less than a constant threshold. We also propose two ready-to-implement algorithms to derive the optimal policy. Comparison with the classical value iteration algorithm implies that our approach explicitly incorporating the characteristics of the optimal policy is significantly more efficient in terms of computation. Importantly, we show that the optimal policy under Poisson arrivals can be obtained by solving a system of integral equations. We also validate our results in simulation with Real-time Strategy (RTS) using real traffic data. The simulation results indicate that the proposed method yields better performance compared with the conventional method

From Discourse Structure To Text Specificity: Studies Of Coherence Preferences

To successfully communicate through text, a writer needs to organize information into an understandable and well-structured discourse for the targeted audience. This involves deciding when to convey general statements, when to elaborate on details, and gauging how much details to convey, i.e., the level of specificity. This thesis explores the automatic prediction of text specificity, and whether the perception of specificity varies across different audiences. We characterize text specificity from two aspects: the instantiation discourse relation, and the specificity of sentences and words. We identify characteristics of instantiation that signify a change of specificity between sentences. Features derived from these characteristics substantially improve the detection of the relation. Using instantiation sentences as the basis for training, we propose a semi-supervised system to predict sentence specificity with speed and accuracy. Furthermore, we present insights into the effect of underspecified words and phrases on the comprehension of text, and the prediction of such words. We show distinct preferences in specificity and discourse structure among different audiences. We investigate these distinctions in both cross-lingual and monolingual context. Cross-lingually, we identify discourse factors that significantly impact the quality of text translated from Chinese to English. Notably, a large portion of Chinese sentences are significantly more specific and need to be translated into multiple English sentences. We introduce a system using rich syntactic features to accurately detect such sentences. We also show that simplified text is more general, and that specific sentences are more likely to need simplification. Finally, we present evidence that the perception of sentence specificity differs among male and female readers

How to Train Your Dragon: Tamed Warping Network for Semantic Video Segmentation

Real-time semantic segmentation on high-resolution videos is challenging due to the strict requirements of speed. Recent approaches have utilized the inter-frame continuity to reduce redundant computation by warping the feature maps across adjacent frames, greatly speeding up the inference phase. However, their accuracy drops significantly owing to the imprecise motion estimation and error accumulation. In this paper, we propose to introduce a simple and effective correction stage right after the warping stage to form a framework named Tamed Warping Network (TWNet), aiming to improve the accuracy and robustness of warping-based models. The experimental results on the Cityscapes dataset show that with the correction, the accuracy (mIoU) significantly increases from 67.3% to 71.6%, and the speed edges down from 65.5 FPS to 61.8 FPS. For non-rigid categories such as "human" and "object", the improvements of IoU are even higher than 18 percentage points

Research on the communication of virtual anchors in E-commerce platforms from the perspective of Embodied Theory -- taking the live streaming sales of the virtual idol Luo Tianyi as an example

In recent years, with the rise of virtual anchors in live streaming, virtual characters represented by "virtual idols" have gradually entered people's life. This paper mainly analyzes the interactive ways and characteristics of virtual idol Luo Tianyi's practice of selling goods in live streaming. In this communication practice, Luo Tianyi has embodied phenomena on two levels "physical and emotional." In addition, this paper also analyzes the communication mode of virtual anchors in e-commerce platforms from the physical-emotional level. Our research believes that in e-commerce live streaming, virtual anchors can integrate technology with their bodies and combine audiences with shopping scenes, thus improving the audience's perception and experience and contributing to the development of the live streaming selling industry. However, the technical construction and practical application of virtual anchors still have some problems to be solved. With the progress and perfection of technology, virtual anchors will have more excellent application space in the future live-streaming industry. Keywords: Embodied theory, virtual anchor, virtual idol, e-commerce live streaming DOI: 10.7176/NMMC/104-05 Publication date: March 31st 202