Explanatory Rule Generation for Advanced Driver Assistant Systems

Although urban rail transit provides significant daily assistance to users, traffic risk remains. Turn-back faults are a common cause of traffic accidents. To address turn-back faults, machines are able to learn the complicated and detailed rules of the train’s internal communication codes, and engineers must understand simple external features for quick judgment. Focusing on turn-back faults in urban rail, in this study we took advantage of related accumulated data to improve algorithmic and human diagnosis of this kind of fault. In detail, we first designed a novel framework combining rules and algorithms to help humans and machines understand the fault characteristics and collaborate in fault diagnosis, including determining the category to which the turn-back fault belongs, and identifying the simple and complicated judgment rules involved. Then, we established a dataset including tabular and text data for real application scenarios and carried out corresponding analysis of fault rule generation, diagnostic classification, and topic modeling. Finally, we present the fault characteristics under the proposed framework. Qualitative and quantitative experiments were performed to evaluate the proposed method, and the experimental results show that (1) the framework is helpful in understanding the faults of trains that occur in three types of turn-back: automatic turn-back (ATB), automatic end change (AEC), and point mode end change (PEC); (2) our proposed framework can assist in diagnosing turn-back faults.

Download Full-text

Advanced Driver Assistant Systems Focused on Pedestrians’ Safety: A User Experience Approach

Sustainability ◽

10.3390/su13084264 ◽

2021 ◽

Vol 13 (8) ◽

pp. 4264

Author(s):

Matúš Šucha ◽

Ralf Risser ◽

Kristýna Honzíčková

Keyword(s):

Autonomous Vehicles ◽

Qualitative Data ◽

Expert Interviews ◽

Driver Assistance ◽

Driver Assistance Systems ◽

Assistant Systems ◽

Vehicle Technology ◽

Assistance Systems ◽

Technical Solutions ◽

Near Future

Globally, pedestrians represent 23% of all road deaths. Many solutions to protect pedestrians are proposed; in this paper, we focus on technical solutions of the ADAS–Advanced Driver Assistance Systems–type. Concerning the interaction between drivers and pedestrians, we want to have a closer look at two aspects: how to protect pedestrians with the help of vehicle technology, and how pedestrians–but also car drivers–perceive and accept such technology. The aim of the present study was to analyze and describe the experiences, needs, and preferences of pedestrians–and drivers–in connection with ADAS, or in other words, how ADAS should work in such a way that it would protect pedestrians and make walking more relaxed. Moreover, we interviewed experts in the field in order to check if, in the near future, the needs and preferences of pedestrians and drivers can be met by new generations of ADAS. A combination of different methods, specifically, an original questionnaire, on-the-spot interviewing, and expert interviews, was used to collect data. The qualitative data was analyzed using qualitative text analysis (clustering and categorization). The questionnaire for drivers was answered by a total of 70 respondents, while a total of 60 pedestrians agreed to complete questionnaires concerning pedestrian safety. Expert interviews (five interviews) were conducted by means of personal interviews, approximately one hour in duration. We conclude that systems to protect pedestrians–to avoid collisions of cars with pedestrians–are considered useful by all groups, though with somewhat different implications. With respect to the features of such systems, the considerations are very heterogeneous, and experimentation is needed in order to develop optimal systems, but a decisive argument put forward by some of the experts is that autonomous vehicles will have to be programmed extremely defensively. Given this argument, we conclude that we will need more discussion concerning typical interaction situations in order to find solutions that allow traffic to work both smoothly and safely.

Download Full-text

Supporting User Onboarding in Automated Vehicles through Multimodal Augmented Reality Tutorials

Multimodal Technologies and Interaction ◽

10.3390/mti5050022 ◽

2021 ◽

Vol 5 (5) ◽

pp. 22

Author(s):

Henrik Detjen ◽

Robert Niklas Degenhart ◽

Stefan Schneegass ◽

Stefan Geisler

Keyword(s):

Augmented Reality ◽

Task Performance ◽

Online Survey ◽

Smartphone Application ◽

Assistant Systems ◽

Vehicle Automation ◽

Hedonic Quality ◽

First Time ◽

Reducing Potential ◽

And Task

Misconceptions of vehicle automation functionalities lead to either non-use or dangerous misuse of assistant systems, harming the users’ experience by reducing potential comfort or compromise safety. Thus, users must understand how and when to use an assistant system. In a preliminary online survey, we examined the use, trust, and the perceived understanding of modern vehicle assistant systems. Despite remaining incomprehensibility (36–64%), experienced misunderstandings (up to 9%), and the need for training (around 30%), users reported high trust in the systems. In the following study with first-time users, we examine the effect of different User Onboarding approaches for an automated parking assistant system in a Tesla and compare the traditional text-based manual with a multimodal augmented reality (AR) smartphone application in means of user acceptance, UX, trust, understanding, and task performance. While the User Onboarding experience for both approaches shows high pragmatic quality, the hedonic quality was perceived significantly higher in AR. For the automated parking process, reported hedonic and pragmatic user experience, trust, automation understanding, and acceptance do not differ, yet the observed task performance was higher in the AR condition. Overall, AR might help motivate proper User Onboarding and better communicate how to operate the system for inexperienced users.

Download Full-text

A neuro-fuzzy system modeling with self-constructing rule generation and hybrid SVD-based learning

IEEE Transactions on Fuzzy Systems ◽

10.1109/tfuzz.2003.812693 ◽

2003 ◽

Vol 11 (3) ◽

pp. 341-353 ◽

Cited By ~ 103

Author(s):

Shie-Jue Lee ◽

Chen-Sen Ouyang

Keyword(s):

Fuzzy System ◽

System Modeling ◽

Rule Generation ◽

Neuro Fuzzy

Download Full-text

GRG: KNOWLEDGE DISCOVERY USING INFORMATION GENERALIZATION, INFORMATION REDUCTION, AND RULE GENERATION

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213096000079 ◽

1996 ◽

Vol 05 (01n02) ◽

pp. 99-112 ◽

Cited By ~ 4

Author(s):

NING SHAN ◽

HOWARD J. HAMILTON ◽

NICK CERCONE

Keyword(s):

Information System ◽

Knowledge Discovery ◽

Relational Databases ◽

Decision Rules ◽

Reduction Technique ◽

Second Step ◽

Rule Generation ◽

Information Reduction ◽

General Rules ◽

Minimal Subset

We present the three-step GRG approach for learning decision rules from large relational databases. In the first step, an attribute-oriented concept tree ascen sion technique is applied to generalize an information system. This step loses some information but substantially improves the efficiency of the following steps. In the second step, a reduction technique is applied to generate a minimalized information system called a reduct which contains a minimal subset of the generalized attributes and the smallest number of distinct tuples for those attributes. Finally, a set of maximally general rules are derived directly from the reduct. These rules can be used to interpret and understand the active mechanisms underlying the database.

Download Full-text