Wheel-Individual Estimation of the Friction Potential for Split Friction and Changing Friction Conditions for the Application in an Automated Emergency Braking System

Including information of the current road surface conditions can significantly improve the effectiveness of an AEB （automated emergency braking） system to avoid accidents or reduce the injury severity in rear-end crashes. A method to estimate the friction potential based on on-board sensor information is shown in this work. This work expands the scope of existing investigations on whether the accuracy needed for the warning and intervention strategies of AEB can be reached with the proposed method. First, the bandwidth of surface conditions investigated is extended by including low friction surfaces comparable to ice. Second, situations of changing surface conditions and wheel-individual surface conditions were evaluated. Finally, estimation based on different sensor sets was conducted with regard to series application. The investigations are based on measurements performed on a proving ground. The main emphasis was placed on estimation during longitudinal driving conditions. The used sensors include advanced vehicle dynamics measurement equipment as well as standard on-board sensors of the vehicle.

Steering data quality with visual analytics:The complexity challenge

Data quality management,especially data cleansing,has been extensively studied for many years in the areas of data management and visual analytics.In the paper,we first review and explore the relevant work from the research areas of data management,visual analytics and human-computer interaction.Then for different types of data such as multimedia data,textual data,trajectory data,and graph data,we summarize the common methods for improving data quality by leveraging data cleansing techniques at different analysis stages.Based on a thorough analysis,we propose a general visual analytics framework for interactively cleansing data.Finally,the challenges and opportunities are analyzed and discussed in the context of data and humans.

A theoretical model for pattern discovery in visual analytics

The word‘pattern’frequently appears in the visualisation and visual analytics literature,but what do we mean when we talk about patterns?We propose a practicable definition of the concept of a pattern in a data distribution as a combination of multiple interrelated elements of two or more data components that can be represented and treated as a unified whole.Our theoretical model describes how patterns are made by relationships existing between data elements.Knowing the types of these relationships,it is possible to predict what kinds of patterns may exist.We demonstrate how our model underpins and refines the established fundamental principles of visualisation.The model also suggests a range of interactive analytical operations that can support visual analytics workflows where patterns,once discovered,are explicitly involved in further data analysis.

A learning-based approach for efficient visualization construction

We propose an approach to underpin interactive visual exploration of large data volumes by training Learned Visualization Index(LVI).Knowing in advance the data,the aggregation functions that are used for visualization,the visual encoding,and available interactive operations for data selection,LVI allows to avoid time-consuming data retrieval and processing of raw data in response to user’s interactions.Instead,LVI directly predicts aggregates of interest for the user’s data selection.We demonstrate the efficiency of the proposed approach in application to two use cases of spatio-temporal data at different scales.

Guidance in the human-machine analytics process

In this paper,we list the goals for and the pros and cons of guidance,and we discuss the role that it can play not only in key low-level visualization tasks but also the more sophisticated model-generation tasks of visual analytics.Recent advances in artificial intelligence,particularly in machine learning,have led to high hopes regarding the possibilities of using automatic techniques to perform some of the tasks that are currently done manually using visualization by data analysts.However,visual analytics remains a complex activity,combining many different subtasks.Some of these tasks are relatively low-level,and it is clear how automation could play a role—for example,classification and clustering of data.Other tasks are much more abstract and require significant human creativity,for example,linking insights gleaned from a variety of disparate and heterogeneous data artifacts to build support for decision making.In this paper,we outline the potential applications of guidance,as well as the inputs to guidance.We discuss challenges in implementing guidance,including the inputs to guidance systems and how to provide guidance to users.We propose potential methods for evaluating the quality of guidance at different phases in the analytic process and introduce the potential negative effects of guidance as a source of bias in analytic decision making.

Visual exploration of movement and event data with interactive time masks

We introduce the concept of time mask,which is a type of temporal filter suitable for selection of multiple disjoint time intervals in which some query conditions fulfil.Such a filter can be applied to time-referenced objects,such as events and trajectories,for selecting those objects or segments of trajectories that fit in one of the selected time intervals.The selected subsets of objects or segments are dynamically summarized in various ways,and the summaries are represented visually on maps and/or other displays to enable exploration.The time mask filtering can be especially helpful in analysis of disparate data(e.g.,event records,positions of moving objects,and time series of measurements),which may come from different sources.To detect relationships between such data,the analyst may set query conditions on the basis of one dataset and investigate the subsets of objects and values in the other datasets that co-occurred in time with these conditions.We describe the desired features of an interactive tool for time mask filtering and present a possible implementation of such a tool.By example of analysing two real world data collections related to aviation and maritime traffic,we show the way of using time masks in combination with other types of filters and demonstrate the utility of the time mask filtering.