Automated Driving: Liability of the Software Producer and the Producer of the Automated Vehicle

2019 ◽  
Author(s):  
Nynke Vellinga
Keyword(s):  
Automated Driving ◽  
Automated Vehicle ◽  
Software Producer

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.

scholarly journals Effects of User Interfaces on Take-Over Performance: A Review of the Empirical Evidence

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 162
Author(s):  
Soyeon Kim ◽  
René van Egmond ◽  
Riender Happee
Keyword(s):  
User Interface ◽  
Situation Awareness ◽  
User Interfaces ◽  
Situational Awareness ◽  
Selection Criterion ◽  
Auditory Modality ◽  
Automated Driving ◽  
Automated Vehicle ◽  
Positive Effects ◽  
High Urgency

In automated driving, the user interface plays an essential role in guiding transitions between automated and manual driving. This literature review identified 25 studies that explicitly studied the effectiveness of user interfaces in automated driving. Our main selection criterion was how the user interface (UI) affected take-over performance in higher automation levels allowing drivers to take their eyes off the road (SAE3 and SAE4). We categorized user interface (UI) factors from an automated vehicle-related information perspective. Short take-over times are consistently associated with take-over requests (TORs) initiated by the auditory modality with high urgency levels. On the other hand, take-over requests directly displayed on non-driving-related task devices and augmented reality do not affect take-over time. Additional explanations of take-over situation, surrounding and vehicle information while driving, and take-over guiding information were found to improve situational awareness. Hence, we conclude that advanced user interfaces can enhance the safety and acceptance of automated driving. Most studies showed positive effects of advanced UI, but a number of studies showed no significant benefits, and a few studies showed negative effects of advanced UI, which may be associated with information overload. The occurrence of positive and negative results of similar UI concepts in different studies highlights the need for systematic UI testing across driving conditions and driver characteristics. Our findings propose future UI studies of automated vehicle focusing on trust calibration and enhancing situation awareness in various scenarios.

scholarly journals An adaptive model predictive approach for automated vehicle control in fallback procedure based on virtual vehicle scheme

2019 ◽  
Vol 2 (2) ◽  
pp. 67-77
Author(s):  
Wei Xue ◽  
Rencheng Zheng ◽  
Bo Yang ◽  
Zheng Wang ◽  
Tsutomu Kaizuka ◽  
...  
Keyword(s):  
Path Planning ◽  
Human Error ◽  
Driving Safety ◽  
Planning Problem ◽  
Adaptive Model ◽  
Sensor Failure ◽  
Automated Driving ◽  
Content Type ◽  
Automated Vehicle ◽  
Virtual Vehicle

Purpose Automated driving systems (ADSs) are being developed to avoid human error and improve driving safety. However, limited focus has been given to the fallback behavior of automated vehicles, which act as a fail-safe mechanism to deal with safety issues resulting from sensor failure. Therefore, this study aims to establish a fallback control approach aimed at driving an automated vehicle to a safe parking lane under perceptive sensor malfunction. Design/methodology/approach Owing to an undetected area resulting from a front sensor malfunction, the proposed ADS first creates virtual vehicles to replace existing vehicles in the undetected area. Afterward, the virtual vehicles are assumed to perform the most hazardous driving behavior toward the host vehicle; an adaptive model predictive control algorithm is then presented to optimize the control task during the fallback procedure, avoiding potential collisions with surrounding vehicles. This fallback approach was tested in typical cases related to car-following and lane changes. Findings It is confirmed that the host vehicle avoid collision with the surrounding vehicles during the fallback procedure, revealing that the proposed method is effective for the test scenarios. Originality/value This study presents a model for the path-planning problem regarding an automated vehicle under perceptive sensor failure, and it proposes an original path-planning approach based on virtual vehicle scheme to improve the safety of an automated vehicle during a fallback procedure. This proposal gives a different view on the fallback safety problem from the normal strategy, in which the mode is switched to manual if a driver is available or the vehicle is instantly stopped.

Necessity of Automated Vehicle Control Customization: Experimental Results during Lane Changing

2018 ◽  
Vol 2672 (22) ◽  
pp. 1-9
Author(s):  
Naohisa Hashimoto ◽  
Simon Thompson ◽  
Shin Kato ◽  
Ali Boyali ◽  
Sadayuki Tsugawa
Keyword(s):  
Vehicle Control ◽  
Experimental Results ◽  
Single Type ◽  
Automated Driving ◽  
Lane Changing ◽  
Automated Vehicle ◽  
Vehicle Trajectories ◽  
Significant Difference ◽  
The Subject ◽  
Automated Vehicle Control

This study investigated the necessity of automated vehicle control customization for individual drivers via a lane-changing experiment involving 35 subjects and an automated minivan. The experiment consisted of two automated driving conditions: one in which the subject was unable to override vehicle controls, the other with the option to override when the subject felt it was necessary. The automated vehicle drove at a speed of 40 km/h along three kinds of planned paths for lane changing, generated by Bezier curves; the distance required for lane changing was varied to obtain the preferred path of each subject. Various data obtained during driving, including vehicle trajectories and steering angles produced by subjects were logged. After automated driving, a questionnaire was administered to each subject. The experimental data showed that there was a statistically significant difference between comfort when the vehicle drove along the subject’s preferred path, and when it drove along other paths. The results of the questionnaire indicated that 46% of the subjects preferred the planned path that most closely resembled their own. In addition, quantitative analysis of driving data found that approximately 69% of the subjects preferred an automated driving control that resembled their own. However, it was also observed that certain subjects were open to multiple types of automated vehicle control. The experimental results indicate that drivers will not necessarily accept a single type of automated vehicle control, therefore customization will be necessary to improve acceptance of automated driving.

scholarly journals Investigating the Impacts of Road Traffic Conditions and Driver’s Characteristics on Automated Vehicle Takeover Time and Quality Using a Driving Simulator

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jaehyun Jason So ◽  
Sungho Park ◽  
Jonghwa Kim ◽  
Jejin Park ◽  
Ilsoo Yun
Keyword(s):  
Driving Simulator ◽  
Road Traffic ◽  
Vehicle Control ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
The Road ◽  
Traffic Conditions ◽  
Vehicle System ◽  
Driver’S Characteristics

This study investigates the impacts of road traffic conditions and driver’s characteristics on the takeover time in automated vehicles using a driving simulator. Automated vehicles are barely expected to maintain their fully automated driving capability at all times based on the current technologies, and the automated vehicle system transfers the vehicle control to a driver when the system can no longer be automatically operated. The takeover time is the duration from when the driver requested the vehicle control transition from the automated vehicle system to when the driver takes full control of the vehicle. This study assumes that the takeover time can vary according to the driver’s characteristics and the road traffic conditions; the assessment is undertaken with various participants having different characteristics in various traffic volume conditions and road geometry conditions. To this end, 25 km of the northbound road section between Osan Interchange and Dongtan Junction on Gyeongbu Expressway in Korea is modeled in the driving simulator; the experiment participants are asked to drive the vehicle and take a response following a certain triggering event in the virtual driving environment. The results showed that the level of service and road curvature do not affect the takeover time itself, but they significantly affect the stabilization time, that is, a duration for a driver to become stable and recover to a normal state. Furthermore, age affected the takeover time, indicating that aged drivers are likely to slowly respond to a certain takeover situation, compared to the younger drivers. With these findings, this study emphasizes the importance of having effective countermeasures and driver interface to monitor drivers in the automated vehicle system; therefore, an early and effective alarm system to alert drivers for the vehicle takeover can secure enough time for stable recovery to manual driving and ultimately to achieve safety during the takeover.

scholarly journals REQUIREMENT ANALYSIS OF 3D ROAD SPACE MODELS FOR AUTOMATED DRIVING

2019 ◽  
Vol IV-4/W8 ◽  
pp. 99-106
Author(s):  
B. Schwab ◽  
T. H. Kolbe
Keyword(s):  
Urban Development ◽  
System Development ◽  
Level Of Detail ◽  
Requirement Analysis ◽  
Automated Driving ◽  
Public Attention ◽  
Automated Vehicle ◽  
Road Space ◽  
High Degree ◽  
Current Standards

<p><strong>Abstract.</strong> Automated driving has received a high degree of public attention in recent years as it will lead to profound changes in mobility, society and urban development. Despite several product announcements from automobile manufacturers and mobility providers, many questions have not yet been answered completely. The need of lane-level HD maps was widely discussed and has been the reason for company acquisitions. HD maps are tailored towards supporting the operation of an automated vehicle. However, the development of this technology also requires road space models, but with a completely different focus and level of detail. Therefore, this article investigates the system development and testing challenges of automated driving. Based on this, requirements of road space models for developing automated driving are derived and gaps to current standards are indicated.</p>

scholarly journals Calibration of Trust in Automated Driving: A Matter of Initial Level of Trust and Automation Driving Style?

2021 ◽  
Author(s):  
J. B. Manchon ◽  
Mercedes Bueno ◽  
Jordan Navarro
Keyword(s):  
Initial Level ◽  
Automated Driving ◽  
Human Machine Interaction ◽  
Driving Style ◽  
Automated Vehicle ◽  
Visual Behaviors ◽  
Trust In Automation ◽  
Machine Interaction ◽  
Over Time ◽  
Highly Automated Driving

Automated driving is becoming a reality, such technology raises new concerns about human-machine interaction on-road. Sixty-one drivers participated in an experiment aiming to better understand the influence of initial level of trust (Trustful vs Distrustful) on drivers’ behaviors and trust calibration during simulated Highly Automated Driving (HAD). The automated driving style was manipulated as positive (smooth) or negative (abrupt) to investigate human-machine early interactions. Trust was assessed over time through questionnaires. Drivers’ visual behaviors and take-over performances during an unplanned take-over request were also investigated. Results showed an increase of trust in automation over time, for both Trustful and Distrustful drivers regardless the automated driving style. Trust was also found to fluctuate over time depending on the specific events handled by the automated vehicle. Take-over performances were not influenced by the initial level of trust nor automated driving style.

scholarly journals Modeling Cross-National Differences in Automated Vehicle Acceptance

2020 ◽  
Vol 12 (22) ◽  
pp. 9765
Author(s):  
Shelly Etzioni ◽  
Jamil Hamadneh ◽  
Arnór B. Elvarsson ◽  
Domokos Esztergár-Kiss ◽  
Milena Djukanovic ◽  
...  
Keyword(s):  
Mode Choice ◽  
Continuous Distribution ◽  
Preference Heterogeneity ◽  
Type Model ◽  
Model Parameters ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
National Differences ◽  
Private Car

The technology that allows fully automated driving already exists and it may gradually enter the market over the forthcoming decades. Technology assimilation and automated vehicle acceptance in different countries is of high interest to many scholars, manufacturers, and policymakers worldwide. We model the mode choice between automated vehicles and conventional cars using a mixed multinomial logit heteroskedastic error component type model. Specifically, we capture preference heterogeneity assuming a continuous distribution across individuals. Different choice scenarios, based on respondents’ reported trip, were presented to respondents from six European countries: Cyprus, Hungary, Iceland, Montenegro, Slovenia, and the UK. We found that large reservations towards automated vehicles exist in all countries with 70% conventional private car choices, and 30% automated vehicles choices. We found that men, under the age of 60, with a high income who currently use private car, are more likely to be early adopters of automated vehicles. We found significant differences in automated vehicles acceptance in different countries. Individuals from Slovenia and Cyprus show higher automated vehicles acceptance while individuals from wealthier countries, UK, and Iceland, show more reservations towards them. Nontrading mode choice behaviors, value of travel time, and differences in model parameters among the different countries are discussed.

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