23 research outputs found
Personalized driver workload inference by learning from vehicle related measurements
Adapting in-vehicle systems (e.g. Advanced Driver Assistance Systems, In-Vehicle Information Systems) to individual driversâ workload can enhance safety and convenience. To make this possible, it is a prerequisite to infer driver workload so that adaptive aiding can be provided to the driver at the right time and in a proper manner. Rather than developing an average model for all drivers, a Personalized Driver Workload Inference (PDWI)
system considering individual driversâ driving characteristics is developed using machine learning techniques via easily accessed
Vehicle Related Measurements (VRMs). The proposed PDWI system comprises two stages. In offline training, individual driversâ workload is first automatically splitted into different categories according to its inherent data characteristics using Fuzzy C means clustering. Then an implicit mapping between VRMs
and different levels of workload is constructed via classification algorithms. In online implementation, VRMs samples are classified
into different clusters, consequently driver workload can be successfully inferred. A recently collected dataset from real-world
naturalistic driving experiments is drawn to validate the proposed PDWI system. Comparative experimental results indicate that the proposed framework integrating Fuzzy C-means clustering and Support Vector Machine classifier provides a promising workload recognition performance in terms of accuracy, precision, recall, F1-score and prediction time. The inter-individual differences in term of workload are also identified and can be accommodated by the proposed framework due to its adaptiveness
The Role and Potentials of Field User Interaction Data in the Automotive UX Development Lifecycle: An Industry Perspective
We are interested in the role of field user interaction data in the
development of IVIS, the potentials practitioners see in analyzing this data,
the concerns they share, and how this compares to companies with digital
products. We conducted interviews with 14 UX professionals, 8 from automotive
and 6 from digital companies, and analyzed the results by emergent thematic
coding. Our key findings indicate that implicit feedback through field user
interaction data is currently not evident in the automotive UX development
process. Most decisions regarding the design of IVIS are made based on personal
preferences and the intuitions of stakeholders. However, the interviewees also
indicated that user interaction data has the potential to lower the influence
of guesswork and assumptions in the UX design process and can help to make the
UX development lifecycle more evidence-based and user-centered
Multimodal Brain-Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines
Through this paper, we introduce a novel driver cognitive load assessment
dataset, CL-Drive, which contains Electroencephalogram (EEG) signals along with
other physiological signals such as Electrocardiography (ECG) and Electrodermal
Activity (EDA) as well as eye tracking data. The data was collected from 21
subjects while driving in an immersive vehicle simulator, in various driving
conditions, to induce different levels of cognitive load in the subjects. The
tasks consisted of 9 complexity levels for 3 minutes each. Each driver reported
their subjective cognitive load every 10 seconds throughout the experiment. The
dataset contains the subjective cognitive load recorded as ground truth. In
this paper, we also provide benchmark classification results for different
machine learning and deep learning models for both binary and ternary label
distributions. We followed 2 evaluation criteria namely 10-fold and
leave-one-subject-out (LOSO). We have trained our models on both hand-crafted
features as well as on raw data.Comment: 13 pages, 8 figures, 11 tables. This work has been submitted to the
IEEE for possible publication. Copyright may be transferred without notic
Personalized Driver Workload Inference by Learning From Vehicle Related Measurements
Adapting in-vehicle systems (e.g., advanced driver assistance systems and in-vehicle information systems) to individual drivers' workload can enhance both safety and convenience. To make this possible, it is a prerequisite to infer driver workload so that adaptive aiding can be provided to the driver at the right time and in an appropriate manner. Rather than developing an average model for all drivers, a personalized driver workload inference (PDWI) system considering individual drivers driving characteristics is developed using machine learning techniques via easily accessed vehicle related measurements (VRMs). The proposed PDWI system comprises two stages. In offline training, individual drivers workload is first automatically splitted into different categories according to its inherent data characteristics using fuzzy C-means (FCM) clustering. Then an implicit mapping between VRMs and different levels of workload is constructed via classification algorithms. In online implementation, VRMs samples are classified into different clusters, consequently driver workload type can be successfully inferred. A recently collected dataset from real-world naturalistic driving experiments is drawn to validate the proposed PDWI system. Comparative experimental results indicate that the proposed framework integrating FCM clustering and support vector machine classifier provides a promising workload recognition performance in terms of accuracy, precision, recall, F 1 -score, and prediction time. The interindividual differences in term of workload are also identified and can be accommodated by the proposed framework due to its adaptiveness
From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI
This paper gives an overview of the ten-year devel- opment of the papers presented at the International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI) from 2009 to 2018. We categorize the topics into two main groups, namely, manual driving-related research and automated driving-related re- search. Within manual driving, we mainly focus on studies on user interfaces (UIs), driver states, augmented reality and head-up displays, and methodology; Within automated driv- ing, we discuss topics, such as takeover, acceptance and trust, interacting with road users, UIs, and methodology. We also discuss the main challenges and future directions for AutoUI and offer a roadmap for the research in this area.https://deepblue.lib.umich.edu/bitstream/2027.42/153959/1/From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI.pdfDescription of From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI.pdf : Main articl
Implicit personalization in driving assistance: State-of-the-art and open issues
In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With the consideration of driving preferences and driver characteristics, these systems become more acceptable and trustworthy. This article presents a survey on recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, benefits of personalization, application prospects, and future focal points. Both relevant driving datasets and open issues about personalized driving assistance are discussed to facilitate future research. By creating an organized categorization of the field, we hope that this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the application of these techniques within the driving automation community</h2