The Neglected Passenger—How Collaboration in the Car Fosters Driving Experience and Safety

2017 ◽  
pp. 187-213 ◽  
Author(s):  
Alexander Meschtscherjakov ◽  
Nicole Perterer ◽  
Sandra Trösterer ◽  
Alina Krischkowsky ◽  
Manfred Tscheligi
Keyword(s):  
Driving Experience

scholarly journals Concept of an Ontology for Automated Vehicle Behavior in the Context of Human-Centered Research on Automated Driving Styles

Information ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 21
Author(s):  
Johannes Ossig ◽  
Stephanie Cramer ◽  
Klaus Bengler
Keyword(s):  
Driving Behavior ◽  
Automation System ◽  
Automated Driving ◽  
Driving Style ◽  
Driving Experience ◽  
Automated Vehicle ◽  
Future Design ◽  
Vehicle System ◽  
The Future ◽  
Conceptual Distinction

In the human-centered research on automated driving, it is common practice to describe the vehicle behavior by means of terms and definitions related to non-automated driving. However, some of these definitions are not suitable for this purpose. This paper presents an ontology for automated vehicle behavior which takes into account a large number of existing definitions and previous studies. This ontology is characterized by an applicability for various levels of automated driving and a clear conceptual distinction between characteristics of vehicle occupants, the automation system, and the conventional characteristics of a vehicle. In this context, the terms ‘driveability’, ‘driving behavior’, ‘driving experience’, and especially ‘driving style’, which are commonly associated with non-automated driving, play an important role. In order to clarify the relationships between these terms, the ontology is integrated into a driver-vehicle system. Finally, the ontology developed here is used to derive recommendations for the future design of automated driving styles and in general for further human-centered research on automated driving.

Level-wise driving knowledge induction for embedded automatic train driving system

2021 ◽  
pp. 107754632110033
Author(s):  
Gang Xiao ◽  
Qinwen Yang ◽  
Fan Yang ◽  
Tao Liu ◽  
Tao Li ◽  
...  
Keyword(s):  
Embedded System ◽  
Fuzzy Rule ◽  
Operating Efficiency ◽  
Driving Experience ◽  
Driving System ◽  
Automatic Driving ◽  
Energy Consumption Reduction ◽  
Experience Levels ◽  
Consumption Reduction ◽  
Regression Learning

Automatic driving of trains can significantly reduce the energy cost and enhance the operating efficiency and safety. The automatic train driving system has to be an embedded system that can run onboard with low power, which necessitates an efficient inference model. In this article, a level-wise driving knowledge induction approach is proposed for embedded automatic train driving systems. The coincident driving patterns in the records of drivers with different experience levels suggest the suitability of a driving experience knowledge rule induction approach. We design a two-level learning approach to obtain both the driving experience pattern in fuzzy rule-based knowledge form and the detailed parameters of velocity and gear by regression learning methods. With 8.93% energy consumption reduction compared with average human drivers, the experiments indicate the effectiveness of our approach.

scholarly journals Review of Studies on Older Drivers’ Behavior and Stress—Methods, Results, and Outlook

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3503
Author(s):  
Yanning Zhao ◽  
Toshiyuki Yamamoto
Keyword(s):  
Age Groups ◽  
Older Drivers ◽  
Driving Simulators ◽  
Driving Experience ◽  
Older Driver ◽  
Vehicle Data ◽  
Vehicle Systems ◽  
Questionnaire Surveys ◽  
Definition Of ◽  
Short Time

This paper presents a review on relevant studies and reports related to older drivers’ behavior and stress. Questionnaires, simulators, and on-road/in-vehicle systems are used to collect driving data in most studies. In addition, research either directly compares older drivers and the other drivers or considers participants according to various age groups. Nevertheless, the definition of ‘older driver’ varies not only across studies but also across different government reports. Although questionnaire surveys are widely used to affordably obtain massive data in a short time, they lack objectivity. In contrast, biomedical information can increase the reliability of a driving stress assessment when collected in environments such as driving simulators and on-road experiments. Various studies determined that driving behavior and stress remain stable regardless of age, whereas others reported degradation of driving abilities and increased driving stress among older drivers. Instead of age, many researchers recommended considering other influencing factors, such as gender, living area, and driving experience. To mitigate bias in findings, this literature review suggests a hybrid method by applying surveys and collecting on-road/in-vehicle data.

scholarly journals Hazard Perception in City and Highway Environments

2019 ◽  
Vol 63 (1) ◽  
pp. 1404-1408
Author(s):  
John Paul Plummer ◽  
Anastasia Diamond ◽  
Alex Chaparro ◽  
Rui Ni
Keyword(s):  
Signal Detection ◽  
Response Bias ◽  
Signal Detection Theory ◽  
Response Criterion ◽  
Crash Risk ◽  
Hazard Perception ◽  
Driving Experience ◽  
City Environment ◽  
Video Clips ◽  
The City

Hazard perception (HP) is an important aspect of driving performance and is associated with crash risk. In the current study, we investigate the effect of roadway environment (city vs. highway) and expertise on HP. HP was measured using HP clips that evaluated response lag (defined as the time from the participant’s response to the end of the clip) and fuzzy signal detection theory metrics of response criterion and sensitivity. Forty videos were used: 20 from highway environments and 20 from city environments. Forty-eight participants with a range of driving experience as assessed by the years since obtaining a license (less than 1 year to 24 years) completed the study. There were differences between city and highway environments in response lag and response bias; participants responded earlier to the hazards in the highway environment and exhibited a more liberal response bias. Driving experience was significantly correlated to response lag. When the video clips were categorized by environment, driving experience was only significantly correlated with performance for the city environment.

scholarly journals Noise, vibration and harshness during dry clutch engagement oscillations

2020 ◽  
Vol 234 (23) ◽  
pp. 4572-4588
Author(s):  
I Minas ◽  
N Morris ◽  
S Theodossiades ◽  
M O’Mahony
Keyword(s):  
Coefficient Of Friction ◽  
Numerical Models ◽  
Dynamic Coefficient ◽  
Driving Experience ◽  
Clutch Engagement ◽  
Pin On Disc ◽  
Dry Clutch ◽  
Interacting Surfaces ◽  
Noise Vibration ◽  
Dynamic Coefficient Of Friction

Determining the root causes of various noise, vibration and harshness phenomena in modern automotive drivetrains is a critical task for industry, since noise, vibration and harshness issues often result in worsened driving experience. The aim of the current research is to investigate the dynamics during dry clutch engagement and the associated – often problematic – oscillations. This paper reports the development and partial validation of numerical models to study dry clutch behaviour. The models are used to investigate the influence of clutch and throttle actuation on the occurrence of unwanted clutch oscillations. The dynamic coefficient of friction between the clutch interacting surfaces was measured using a pin-on-disc rig under different slip speeds and contact pressure conditions, which are representative of a typical clutch engagement manoeuvre. The paper highlights the occurrence of instability issues in clutch dynamics (disc radial mode) as potential generators of aggressive noise, vibration and harshness, particularly during two different clutch pedal actuations. Such analysis has not hitherto been reported in the open literature.

scholarly journals Toward a Comfortable Driving Experience for a Self-Driving Shuttle Bus

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 943 ◽  
Author(s):  
Il Bae ◽  
Jaeyoung Moon ◽  
Jeongseok Seo
Keyword(s):  
Real Time ◽  
High Performance ◽  
Autonomous Driving ◽  
Planning Method ◽  
Dynamics Simulation ◽  
Driving Experience ◽  
Optimal Velocity ◽  
Planning Approach ◽  
Velocity Planning ◽  
Time Optimal

The convergence of mechanical, electrical, and advanced ICT technologies, driven by artificial intelligence and 5G vehicle-to-everything (5G-V2X) connectivity, will help to develop high-performance autonomous driving vehicles and services that are usable and convenient for self-driving passengers. Despite widespread research on self-driving, user acceptance remains an essential part of successful market penetration; this forms the motivation behind studies on human factors associated with autonomous shuttle services. We address this by providing a comfortable driving experience while not compromising safety. We focus on the accelerations and jerks of vehicles to reduce the risk of motion sickness and to improve the driving experience for passengers. Furthermore, this study proposes a time-optimal velocity planning method for guaranteeing comfort criteria when an explicit reference path is given. The overall controller and planning method were verified using real-time, software-in-the-loop (SIL) environments for a real-time vehicle dynamics simulation; the performance was then compared with a typical planning approach. The proposed optimized planning shows a relatively better performance and enables a comfortable passenger experience in a self-driving shuttle bus according to the recommended criteria.

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