High-level autonomous vehicles and domain-based electronic and electrical (E/E) architectures are important development directions of the intelligent automobile industry. The domain-centralized E/E architecture has become the potential upgrade to the autonomous vehicle benefitting from its powerful software updates, cabling reduction, and functional integration. Aiming at the efficient motion control of the autonomous vehicle equipped with domain-centralized E/E architecture, a novel control framework with algorithms improvement is proposed in this paper, which contains the multi-hops loop delay analysis to solve the control stability problem caused by the heterogeneous topology loop delay of domain-centralized E/E architecture. In this framework, the motion controller is generated through the combination of modified double reinforcement learning algorithm and multi-steps predictive control method, and the loop delay is integrated into the controller optimization. Through the virtual driving environment simulation and real world scenario, the results show that the proposed framework achieves better performance in terms of path tracking and obstacles avoidance, and the stability of control strategies to loop delay is also guaranteed.

In this paper, we present a purpose-built data management system, MLdp, for all machine learning (ML) datasets. ML applications pose some unique requirements different from common conventional data processing applications, including but not limited to: data lineage and provenance tracking, rich data semantics and formats, integration with diverse ML frameworks and access patterns, trial-and-error driven data exploration and evolution, rapid experimentation, reproducibility of the model training, strict compliance and privacy regulations, etc. Current ML systems/services, often named MLaaS, to-date focus on the ML algorithms, and offer no integrated data management system. Instead, they require users to bring their own data and to manage their own data on either blob storage or on file systems. The burdens of data management tasks, such as versioning and access control, fall onto the users, and not all compliance features, such as terms of use, privacy measures, and auditing, are available. MLdp offers a minimalist and flexible data model for all varieties of data, strong version management to guarantee re-producibility of ML experiments, and integration with major ML frameworks. MLdp also maintains the data provenance to help users track lineage and dependencies among data versions and models in their ML pipelines. In addition to table-stake features, such as security, availability and scalability, MLdp's internal design choices are strongly influenced by the goal to support rapid ML experiment iterations, which cycle through data discovery, data exploration, feature engineering, model training, model evaluation, and back to data discovery. The contributions of this paper are: 1) to recognize the needs and to call out the requirements of an ML data platform, 2) to share our experiences in building MLdp by adopting existing database technologies to the new problem as well as by devising new solutions, and 3) to call for actions from our communities on future challenges.

Abstract Linear models are commonly used to identify trends in data. While it is an easy task to build linear models using pre‐selected variables, it is challenging to select the best variables from a large number of alternatives. Most metrics for selecting variables are global in nature, and thus not useful for identifying local patterns. In this work, we present an integrated framework with visual representations that allows the user to incrementally build and verify models in three model spaces that support local pattern discovery and summarization: model complementarity, model diversity, and model representivity. Visual representations are designed and implemented for each of the model spaces. Our visualizations enable the discovery of complementary variables, i.e., those that perform well in modeling different subsets of data points. They also support the isolation of local models based on a diversity measure. Furthermore, the system integrates a hierarchical representation to identify the outlier local trends and the local trends that share similar directions in the model space. A case study on financial risk analysis is discussed, followed by a user study.

We will demonstrate the visual analytics system V istreamT, that supports interactive mining of complex patterns within and across live data streams and stream pattern archives. Our system is equipped with both computational pattern mining and visualization techniques, which allow it to not only efficiently discover and manage patterns but also effectively convey the mining results to human analysts through visual displays. In our demonstration, we will illustrate that with V istreamT, analysts can easily submit, monitor and interact with a broad range of query types for pattern mining. This includes novel strategies for extracting complex patterns from streams in real time, summarizing neighbour-based patterns using multi-resolution compression strategies, selectively pushing patterns into the stream archive, validating the popularity or rarity of stream patterns by stream archive matching, and pattern evolution tracking to link patterns across time.