Who is the driver in an automated vehicle? - The Performance of dynamic driving task by automated driving system (ADS) and the ADS Entity’s responsibility for it

2019 ◽  
Vol 20 (3) ◽  
pp. 343-370
Author(s):  
Choong-Kee Lee ◽  
changgeun Hwang
Keyword(s):  
Automated Driving ◽  
Driving System ◽  
Automated Vehicle ◽  
Driving Task

scholarly journals Driver intervention performance assessment as a key aspect of L3–L4 automated vehicles deployment

2021 ◽  
Vol 11 (1) ◽  
pp. 845-852
Author(s):  
Aleksandra Rodak ◽  
Paweł Budziszewski ◽  
Małgorzata Pędzierska ◽  
Mikołaj Kruszewski
Keyword(s):  
Performance Assessment ◽  
Minimal Risk ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Driving System ◽  
Driving Task ◽  
Driving Conditions

Abstract In L3–L4 vehicles, driving task is performed primarily by automated driving system (ADS). Automation mode permits to engage in non-driving-related tasks; however, it necessitates continuous vigilance and attention. Although the driver may be distracted, a request to intervene may suddenly occur, requiring immediate and appropriate response to driving conditions. To increase safety, automated vehicles should be equipped with a Driver Intervention Performance Assessment module (DIPA), ensuring that the driver is able to take the control of the vehicle and maintain it safely. Otherwise, ADS should regain control from the driver and perform a minimal risk manoeuvre. The paper explains the essence of DIPA, indicates possible measures, and describes a concept of DIPA framework being developed in the project.

A Systematic Review and Meta-Analysis of Takeover Performance During Conditionally Automated Driving

2020 ◽  
pp. 001872082097647
Author(s):  
Bradley W. Weaver ◽  
Patricia R. DeLucia
Keyword(s):  
Time Budget ◽  
Meta Analysis ◽  
Task Engagement ◽  
Information Support ◽  
Future Research ◽  
Automated Driving ◽  
Driving System ◽  
Weak Evidence ◽  
Dependent Variables ◽  
Driving Task

Objective The aim of this paper was to synthesize the experimental research on factors that affect takeover performance during conditionally automated driving. Background For conditionally automated driving, the automated driving system (ADS) can handle the entire dynamic driving task but only for limited domains. When the system reaches a limit, the driver is responsible for taking over vehicle control, which may be affected by how much time they are provided to take over, what they were doing prior to the takeover, or the type of information provided to them during the takeover. Method Out of 8446 articles identified by a systematic literature search, 48 articles containing 51 experiments were included in the meta-analysis. Coded independent variables were time budget, non-driving related task engagement and resource demands, and information support during the takeover. Coded dependent variables were takeover timing and quality measures. Results Engaging in non-driving related tasks results in degraded takeover performance, particularly if it has overlapping resource demands with the driving task. Weak evidence suggests takeover performance is impaired with shorter time budgets. Current implementations of information support did not affect takeover performance. Conclusion Future research and implementation should focus on providing the driver more time to take over while automation is active and should further explore information support. Application The results of the current paper indicate the need for the development and deployment of vehicle-to-everything (V2X) services and driver monitoring.

scholarly journals Potential Crash Rate Benchmarks for Automated Vehicles

2021 ◽  
Author(s):  
Noah J. Goodall
Keyword(s):  
Stated Preference ◽  
Average Rate ◽  
Crash Risk ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
Crash Rates ◽  
Potential Safety ◽  
Transportation Modes ◽  
Driving Task

Most automobile manufacturers and several technology companies are testing automated vehicles on public roads. While automation of the driving task is expected to reduce crashes, there is no consensus regarding how safe an automated vehicle must be before it can be deployed. An automated vehicle should be at least as safe as the average driver, but national crash rates include drunk and distracted driving, meaning that an automated vehicle that crashes at the average rate is somewhere between drunk and sober. In this paper, automated vehicle safety benchmarks are explored from three perspectives. First, crash rates from naturalistic driving studies are used to determine the crash risk of the model (i.e., sober, rested, attentive, cautious) driver. Second, stated preference surveys in the literature are reviewed to estimate the public’s acceptable automated vehicle risk. Third, crash, injury, and fatality rates from other transportation modes are compared as baseline safety levels. A range of potential safety targets is presented as a guide for policymakers, regulators, and automated vehicle developers to assist in evaluating the safety of automated driving technologies for public use.

scholarly journals A Methodology for Normalizing Safety Statistics of Partially Automated Vehicles

2021 ◽  
Author(s):  
Noah J. Goodall
Keyword(s):  
Reporting Standards ◽  
Driver Assistance ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Driver Assistance Systems ◽  
Driving System ◽  
Automated Vehicle ◽  
Crash Rates ◽  
Road Crash ◽  
Assistance Systems

The safety of increasingly automated vehicles is of great concern to regulators, yet crash rates are generally reported by manufacturers with proprietary metrics. Without consistent definitions of crashes and exposure, comparing automated vehicle crash rates with baseline datasets becomes challenging. This study investigates the reported on-road crash rates of one manufacturer’s partially automated driving system. Their reported crash rates are adjusted based on roadway classification and driver demographics to allow for direct comparison with the manufacturer’s own advanced driver assistance systems. Recommendations for uniform crash reporting standards are provided.

scholarly journals The Decline of User Experience in Transition from Automated Driving to Manual Driving

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 126
Author(s):  
Mikael Johansson ◽  
Mattias Mullaart Söderholm ◽  
Fjollë Novakazi ◽  
Annie Rydström
Keyword(s):  
San Francisco ◽  
User Experience ◽  
San Francisco Bay ◽  
San Francisco Bay Area ◽  
Transition Process ◽  
Automated Driving ◽  
Driving System ◽  
Congested Traffic ◽  
Bay Area ◽  
Driving Task

Automated driving technologies are rapidly being developed. However, until vehicles are fully automated, the control of the dynamic driving task will be shifted between the driver and automated driving system. This paper aims to explore how transitions from automated driving to manual driving affect user experience and how that experience correlates to take-over performance. In the study 20 participants experienced using an automated driving system during rush-hour traffic in the San Francisco Bay Area, CA, USA. The automated driving system was available in congested traffic situations and when active, the participants could engage in non-driving related activities. The participants were interviewed afterwards regarding their experience of the transitions. The findings show that most of the participants experienced the transition from automated driving to manual driving as negative. Their user experience seems to be shaped by several reasons that differ in temporality and are derived from different phases during the transition process. The results regarding correlation between participants’ experience and take-over performance are inconclusive, but some trends were identified. The study highlights the need for new design solutions that do not only improve drivers’ take-over performance, but also enhance user experience during take-over requests from automated to manual driving.

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.

Safety Enhancement by Automated Driving: What are the Relevant Scenarios?

2020 ◽  
Vol 64 (1) ◽  
pp. 1686-1690
Author(s):  
Niklas Grabbe ◽  
Michael Höcher ◽  
Alexander Thanos ◽  
Klaus Bengler
Keyword(s):  
Traffic Safety ◽  
Road Traffic ◽  
Test Cases ◽  
Automated Driving ◽  
Road Traffic Safety ◽  
Human Driver ◽  
Driving Task ◽  
Promising Solution ◽  
Vehicle Automation ◽  
Safety Enhancement

Automated driving offers great possibilities in traffic safety advancement. However, evidence of safety cannot be provided by current validation methods. One promising solution to overcome the approval trap (Winner, 2015) could be the scenario-based approach. Unfortunately, this approach still results in a huge number of test cases. One possible way out is to show the current, incorrect path in the argumentation and strategy of vehicle automation, and focus on the systemic mechanisms of road traffic safety. This paper therefore argues the case for defining relevant scenarios and analysing them systemically in order to ultimately reduce the test cases. The relevant scenarios are based on the strengths and weaknesses, in terms of the driving task, for both the human driver and automation. Finally, scenarios as criteria for exclusion are being proposed in order to systemically assess the contribution of the human driver and automation to road safety.

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