1,720,963 research outputs found
Continual Learning for Motion Prediction Model via Meta-Representation Learning and Optimal Memory Buffer Retention Strategy
No full textEmbodied AI, such as autonomous vehicles, suffers from insufficient, long-tailed data because it must be obtained from the physical world. In fact, data must be continuously obtained in a series of small batches, and the model must also be continuously trained to achieve generalizability and scalability by improving the biased data distribution. This paper addresses the training cost and catastrophic forgetting problems when continuously updating models to adapt to incoming small batches from various environments for real-world motion prediction in autonomous driving. To this end, we propose a novel continual motion prediction (CMP) learning framework based on sparse meta-representation learning and an optimal memory buffer retention strategy. In meta-representation learning, a model explicitly learns a sparse representation of each driving environment, from road geometry to vehicle states, by training to reduce catastrophic forgetting based on an augmented modulation network with sparsity regularization. Also, in the adaptation phase, We develop an Optimal Memory Buffer Retention strategy that smartly preserves diverse samples by focusing on representation similarity. This approach handles the nuanced task distribution shifts characteristic of motion prediction datasets, ensuring our model stays responsive to evolving input variations without requiring extensive resources. The experiment results demonstrate that the proposed method shows superior adaptation performance to the conventional continual learning approach, which is developed using a synthetic dataset for the continual learning problem
Autonomous Driving Technology Trend and Future Outlook: Powered by Artificial Intelligence
No full textAutonomous driving is not a new concept, and relevant technology has been developed for a long time. However, in recent years, autonomous driving technology has been leaping forward, fueled by the advance of AI-based technologies. In particular, the essential components of autonomous driving, such as perception, prediction, and planning, deliver entirely different performances from those of the pre-AI era. In this study, the trends and development of autonomous driving technology will be analyzed by decomposing it into element technologies ranging from perception, prediction, and planning, focusing on AI-based research. For the perception part, LiDAR and camera-based research and sensor fusion technologies will be examined. For the prediction part, we will look into various prediction paradigms such as interaction-aware and map-based prediction. The planning part will cover maneuver decisions, motion planning, and reinforcement learning-based methods
RECUP Net: RECUrsive Prediction Network for Surrounding Vehicle Trajectory Prediction with Future Trajectory Feedback
No full textIn order to predict the behavior of human drivers accurately, the autonomous vehicle should he able to understand the reasoning and decision process of motion generation of human drivers. However, most of the conventional prediction methods overlook this and focus on improving the prediction results using the given data, the historical information. In contrast, human drivers not only depend on the historical motion but also consider future predictions when handling interactions with other vehicles. In this paper, we propose a novel recursive RNN encoder-decoder prediction model that takes the initial future prediction results as inputs of second prediction computation. This feedback mechanism can he interpreted as a network sharing, which allows the model to refine or correct the predicted results iteratively. We use two encoders to analyze both of the historical information and future information, and the attention mechanism is employed to interpret interaction. Our experimental results with the NGSIM dataset demonstrate the recursive structure enhances prediction results effectively compare to the baselines based on the ablation study and state-of-the-art methods. Furthermore, we observe that the results improve successively as the model iterates
Beyond the Data Imbalance: Employing the Heterogeneous Datasets for Vehicle Maneuver Prediction
Full text linkPredicting the maneuvers of surrounding vehicles is imperative for the safe navigation of autonomous vehicles. However, naturalistic driving datasets tend to be highly imbalanced, with a bias towards the “going straight” maneuver. Consequently, learning and accurately predicting turning maneuvers pose significant challenges. In this study, we propose a novel two-stage maneuver learning method that can overcome such strong biases by leveraging two heterogeneous datasets in a complementary manner. In the first training phase, we utilize an intersection-centric dataset characterized by balanced distribution of maneuver classes to learn the representations of each maneuver. Subsequently, in the second training phase, we incorporate an ego-centric driving dataset to account for various geometrical road shapes, by transferring the knowledge of geometric diversity to the maneuver prediction model. To facilitate this, we constructed an in-house intersection-centric trajectory dataset with a well-balanced maneuver distribution. By harnessing the power of heterogeneous datasets, our framework significantly improves maneuver prediction performance, particularly for minority maneuver classes such as turning maneuvers. The dataset is available at https://github.com/KAIST-VDCLab/VDC-Trajectory-Dataset
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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