The articles in this special section examine the market for autonomous and connected vehicles. The automotive industry is transitioning toward connected, autonomous, and electric vehicles; offering contextual, intelligent, and personal consumer experiences; and innovating disruptive ecosystems and business models such as car sharing and hailing services. All of these are primarily driven by fast-moving technology and innovations from 5G communication, cloud and edge computing platforms to artificial intelligence and big data. These changes are predicted to dominate our lives in about 10 years, leading to significant consumer and business values, including improved traffic and safety, smart journey management, cleaner environments, intelligent and personal mobility experiences, and competitive cost management. Similar to companies like Apple, Google, and Baidu heavily investing in connected and autonomous vehicles, car manufacturers are moving to be more like software companies in their use of big data, machine learning, and artificial intelligence to understand the driving context and consumer behavior. These days, a car is not just for going from point A to point B; rather, it delivers a holistic experience that revolves around our daily activities, with the future being the autonomous vehicle.
The information retrieval from relational database requires professionals who has an understanding of structural query language such as SQL. TEXT2SQL models apply natural language inference to enable user interacting the database via natural language utterance. Current TEXT2SQL models normally focus on generating complex SQL query in a precise and complete fashion while certain features of real-world application in the production environment is not fully addressed. This paper is aimed to develop a service-oriented Text-to-SQL parser that translates natural language utterance to structural and executable SQL query. We introduce a algorithmic framework named Semantic-Enriched SQL generator (SE-SQL) that enables flexibly access database than rigid API in the application while keeping the performance quality for the most commonly used cases. The qualitative result shows that the proposed model achieves 88.3% execution accuracy on WikiSQL task, outperforming baseline by 13% error reduction. Moreover, the framework considers several service-oriented needs including low-complexity inference, out-of-table rejection, and text normalization.
The problem of mobile context recognition targets the identification of semantic meaning of context in a mobile environment. This plays an important role in understanding mobile user behaviors and thus provides the opportunity for the development of better intelligent context-aware services. A key step of context recognition is to model the personalized contextual information of mobile users. Although many studies have been devoted to mobile context modeling, limited efforts have been made on the exploitation of the sequential and dependency characteristics of mobile contextual information. Also, the latent semantics behind mobile context are often ambiguous and poorly understood. Indeed, a promising direction is to incorporate some domain knowledge of common contexts, such as “waiting for a bus” or “having dinner,” by modeling both labeled and unlabeled context data from mobile users because there are often few labeled contexts available in practice. To this end, in this article, we propose a sequence-based semisupervised approach to modeling personalized context for mobile users. Specifically, we first exploit the Bayesian Hidden Markov Model (B-HMM) for modeling context in the form of probabilistic distributions and transitions of raw context data. Also, we propose a sequential model by extending B-HMM with the prior knowledge of contextual features to model context more accurately. Then, to efficiently learn the parameters and initial values of the proposed models, we develop a novel approach for parameter estimation by integrating the Dirichlet Process Mixture (DPM) model and the Mixture Unigram (MU) model. Furthermore, by incorporating both user-labeled and unlabeled data, we propose a semisupervised learning-based algorithm to identify and model the latent semantics of context. Finally, experimental results on real-world data clearly validate both the efficiency and effectiveness of the proposed approaches for recognizing personalized context of mobile users.
Platform-based large-scale journey planning of autonomous vehicles and context-sensitive route planning applications require new scalable approaches in order to work within an on-demand mobility service. In this work we present and test a machine learning-based approach for distance-based roundtrip planning in a Traveling Salesman Problem (TSP) setting. We introduce our applied Distance-Based Pointer Network (DBPN) algorithm which solves mini-batches of multiple symmetric and asymmetric 2D Euclidean TSPs. We provide our algorithm and test results for symmetric and asymmetric TSP distances, as present in real road and traffic networks. Subsequently, we compare our results with an industry standard routing solver OR-Tools. Here, we focus on solving comparably small TSP instances which commonly occur on our platform-based service. Our results show that compared to the State-of-the-Art methods such as the Coordinate-Based Pointer Network (CBPN) and OR-Tools, our approach solves asymmetric TSPs which cannot be solved by the CBPN approach. The results furthermore show that our approach achieves near-optimal results by a 5.9% mean absolute percentage error, compared to the OR-Tools solution. By solving 1000 TSPs, we show that our DBPN approach is approximately 27 times faster than the OR-Tools solver.
In conventional speech synthesis, large amounts of phonetically balanced speech data recorded in highly controlled recording studio environments are typically required to build a voice. Although using such data is a straightforward solution for high quality synthesis, the number of voices available will always be limited, because recording costs are high. On the other hand, our recent experiments with HMM-based speech synthesis systems have demonstrated that speaker-adaptive HMM-based speech synthesis (which uses an "average voice model" plus model adaptation) is robust to non-ideal speech data that are recorded under various conditions and with varying microphones, that are not perfectly clean, and/or that lack phonetic balance. This enables us to consider building high-quality voices on "non-TTS" corpora such as ASR corpora. Since ASR corpora generally include a large number of speakers, this leads to the possibility of producing an enormous number of voices automatically. In this paper, we demonstrate the thousands of voices for HMM-based speech synthesis that we have made from several popular ASR corpora such as the Wall Street Journal (WSJ0, WSJ1, and WSJCAM0), Resource Management, Globalphone, and SPEECON databases. We also present the results of associated analysis based on perceptual evaluation, and discuss remaining issues.
A vehicle's fuel consumption is strongly related to both its loading and the driver's driving behavior, such as aggressive/tender acceleration, improper/proper gear change or running/stopping the engine while waiting. This paper introduces EasyRoute, an economical route recommendation system for modern vehicles, implemented in smartphones, to improve fuel efficiency. EasyRoute senses the vehicle's fuel consumption through the On-Board Diagnostics (OBD) adapter and then models the driver's personal fuel consumption according to OBD data from two aspects: i) when the vehicle is moving and ii) when the vehicle is idling or waiting. Based on the crowdsourced traffic information, EasyRoute can near-correctly predict total fuel consumptions of different routes and recommend drivers with the greenest route. We describe the EasyRoute framework and evaluate it by collecting OBD and GPS data from 559 taxis in Beijing. Comparing with some commonly used baselines with error metrics, the experimental results show that using a small 10-minute dataset for training, the total fuel consumption estimated by EasyRoute has a relative error of at least 30% less than the baselines.
Connected vehicles and other relevant technologies have enabled smart or predictive personalization. However, much personalization is based on explicit driver profile where the driver enters her preferences in the vehicle such as seat heating, seat position or climate control. With the proliferation of big data, cloud computing and IoT, machine learning and AI can be used to learn user preferences implicitly without the need for explicitly setting them. However, the challenge is how to create a system for a personalized smart interior that can provide a proactive and comfortable user experience without inconveniencing the user. In this paper, we address this challenge by creating a machine learning framework for supporting smart interior, and use predictive preconditioning to illustrate this. We implemented this framework in our product BMW Connected and present preliminary results to show that the accuracy is 91%, precision is 76% and recall is 89%. Most active users when receiving the preconditioning notifications, on average, do execute the preconditioning at least twice a week, indicating its usefulness. This framework can be used for personalizing other interior features such as seat heating and seat positioning.
Service reuse aims at improving the efficiency of software development and providing common functionalities which are not linked to any particular business process. However, the existing service reuse methods are confined to the reuse of atomic services or processes encapsulated as stand-alone services. How to reuse arbitrary granularities of Service Process Fragment (SPF) is a challenging problem with great application value. This paper presents a novel Variable Granularities Index (VGI) based on SSM-Tree on service processes. VGI could realize the unified index on both atomic and composite services and maximize reuse of them. To verify the feasibility and effectiveness, we construct a sample dataset which contains 500 thousand processes and 127 million atomic services based on the Web Service Challenge Testset Generator (CTG). The experimental results show an effective and efficient approach for SPF query.
Finding and recommending suitable services for mobile users are increasingly important due to the popularity of mobile Internet. While recent research has attempted to use role-based approaches to recommend services, role discovery is still an ongoing research topic. In particular, numerous role mining approaches have been proposed in the context of RBAC (Role-Based Access Control) in which only two dimensions of parameters are considered (i.e., ); and the notion of context-awareness has been disregarded. This paper proposes a context-aware role mining method to automatically group users according to their interests and habits, such that popular mobile services can be recommended to other members in the same group in a context dependent manner. Experiments show that our approach is efficient and practical for mobile service recommendation.
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