scholarly journals Dynamic driving task fallback for an automated driving system whose ability to monitor the driving environment has been compromised

2017 ◽  
Author(s):  
Yrvann Emzivat ◽  
Javier Ibanez-Guzman ◽  
Philippe Martinet ◽  
Olivier H. Roux
Keyword(s):  
Automated Driving ◽  
Driving System ◽  
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 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.

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.

When the automated driving system fails: Dynamics of public responses to automated vehicles

2021 ◽  
Vol 129 ◽  
pp. 103271
Author(s):  
Zhigang Xu ◽  
Zijun Jiang ◽  
Guanqun Wang ◽  
Runmin Wang ◽  
Tingting Li ◽  
...  
Keyword(s):  
Automated Vehicles ◽  
Automated Driving ◽  
Driving System

In Pursuit of Emergency Procedures for Automated Driving System-Involved Scenarios

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Travis Terry ◽  
Tammy E. Trimble ◽  
Mindy Buchanan-King ◽  
Myra Blanco ◽  
Vikki L. Fitchett ◽  
...  
Keyword(s):  
Automated Driving ◽  
Driving System ◽  
Emergency Procedures

Dynamics of Trust in Automation and Interactive Decision Making during Driving Simulation Tasks

2021 ◽  
Vol 65 (1) ◽  
pp. 786-790
Author(s):  
Lucero Rodriguez Rodriguez ◽  
Carlos Bustamante Orellana ◽  
Jayci Landfair ◽  
Corey Magaldino ◽  
Mustafa Demir ◽  
...  
Keyword(s):  
Decision Making ◽  
Automated System ◽  
Automated Driving ◽  
Safety And Efficacy ◽  
High Performing ◽  
Interactive Decision Making ◽  
Driving Task ◽  
Preliminary Study ◽  
Self Driving Cars ◽  
The Impact

As technological advancements and lowered costs make self-driving cars available to more people, it becomes important to understand the dynamics of human-automation interactions for safety and efficacy. We used a dynamical approach to examine data from a previous study on simulated driving with an automated driving assistant. To maximize effect size in this preliminary study, we focused the current analysis on the two lowest and two highest-performing participants. Our visual comparisons were the utilization of the automated system and the impact of perturbations. Low-performing participants toggled and maintained reliance either on automation or themselves for longer periods of time. Decision making of high-performing participants was using the automation briefly and consistently throughout the driving task. Participants who displayed an early understanding of automation capabilities opted for tactical use. Further exploration of individual differences and automation usage styles will help to understand the optimal human-automation-team dynamic and increase safety and efficacy.

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