Safety Compensation for Improving Driver Takeover Performance in Conditionally Automated Driving

2020 ◽  
Vol 32 (3) ◽  
pp. 530-536
Author(s):  
Hua Yao ◽  
Suyang An ◽  
Huiping Zhou ◽  
Makoto Itoh ◽  
◽  
...  
Keyword(s):  
Time Budget ◽  
Vehicle Control ◽  
Driving Performance ◽  
Lateral Acceleration ◽  
Time To Event ◽  
Automated Driving ◽  
Functional Limit ◽  
Level 3 ◽  
Transition Scheme

The topic of transition from automated driving to manual maneuver in conditionally automated driving (SAE level-3) has acquired increasing interest. In such conditionally automated driving, drivers are expected to take over the vehicle control if the situation goes beyond the system’s functional limit of operation. However, it is challenging for drivers to resume control timely and perform well after being engaged in non-driving related tasks. Facing this challenge, this paper investigated a safety compensation in which the system conducts automatic deceleration to prolong the time budget for drivers to response. The purpose of the paper is to evaluate the effect of safety compensation on takeover performance in different takeover scenarios such as fog, route choosing, and lane closing. In the experiment, 16 participants were recruited. Results showed no significant effect of safety compensation on the takeover time, but a significant effect on the longitudinal driving performance (viz. driver brake input and the time to event). Moreover, it indicated a significant effect of safety compensation on the lateral acceleration in the lane closing scenario. This finding is useful for the automotive manufacturers to supply users a safer transition scheme from automated driving to manual maneuver.

scholarly journals Toward Computational Simulations of Behavior During Automated Driving Takeovers: A Review of the Empirical and Modeling Literatures

2019 ◽  
Vol 61 (4) ◽  
pp. 642-688 ◽  
Author(s):  
Anthony D. McDonald ◽  
Hananeh Alambeigi ◽  
Johan Engström ◽  
Gustav Markkula ◽  
Tobias Vogelpohl ◽  
...  
Keyword(s):  
Time Budget ◽  
Empirical Studies ◽  
Vehicle Control ◽  
Influential Factors ◽  
Automated Driving ◽  
Secondary Tasks ◽  
Safety Issues ◽  
Automated Vehicle ◽  
Automated Vehicle Control ◽  
The Impact

Objective: This article provides a review of empirical studies of automated vehicle takeovers and driver modeling to identify influential factors and their impacts on takeover performance and suggest driver models that can capture them. Background: Significant safety issues remain in automated-to-manual transitions of vehicle control. Developing models and computer simulations of automated vehicle control transitions may help designers mitigate these issues, but only if accurate models are used. Selecting accurate models requires estimating the impact of factors that influence takeovers. Method: Articles describing automated vehicle takeovers or driver modeling research were identified through a systematic approach. Inclusion criteria were used to identify relevant studies and models of braking, steering, and the complete takeover process for further review. Results: The reviewed studies on automated vehicle takeovers identified several factors that significantly influence takeover time and post-takeover control. Drivers were found to respond similarly between manual emergencies and automated takeovers, albeit with a delay. The findings suggest that existing braking and steering models for manual driving may be applicable to modeling automated vehicle takeovers. Conclusion: Time budget, repeated exposure to takeovers, silent failures, and handheld secondary tasks significantly influence takeover time. These factors in addition to takeover request modality, driving environment, non-handheld secondary tasks, level of automation, trust, fatigue, and alcohol significantly impact post-takeover control. Models that capture these effects through evidence accumulation were identified as promising directions for future work. Application: Stakeholders interested in driver behavior during automated vehicle takeovers may use this article to identify starting points for their work.

scholarly journals Glaucomatous visual fields and neurocognitive function are independently associated with poor lane maintenance during driving simulation

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
David E. Anderson ◽  
John P. Bader ◽  
Emily A. Boes ◽  
Meghal Gagrani ◽  
Lynette M. Smith ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Vehicle Control ◽  
Driving Behavior ◽  
Driving Performance ◽  
Driving Simulation ◽  
Sensor Data ◽  
Lateral Acceleration ◽  
Steering Wheel ◽  
Neurocognitive Performance ◽  
Loss Severity

Abstract Background Driving simulators are a safe alternative to on-road vehicles for studying driving behavior in glaucoma drivers. Visual field (VF) loss severity is associated with higher driving simulator crash risk, though mechanisms explaining this relationship remain unknown. Furthermore, associations between driving behavior and neurocognitive performance in glaucoma are unexplored. Here, we evaluated the hypothesis that VF loss severity and neurocognitive performance interact to influence simulated vehicle control in glaucoma drivers. Methods Glaucoma patients (n = 25) and suspects (n = 18) were recruited into the study. All had > 20/40 corrected visual acuity in each eye and were experienced field takers with at least three stable (reliability > 20%) fields over the last 2 years. Diagnosis of neurological disorder or cognitive impairment were exclusion criteria. Binocular VFs were derived from monocular Humphrey VFs to estimate a binocular VF index (OU-VFI). Montreal Cognitive Assessment (MoCA) was administered to assess global and sub-domain neurocognitive performance. National Eye Institute Visual Function Questionnaire (NEI-VFQ) was administered to assess peripheral vision and driving difficulties sub-scores. Driving performance was evaluated using a driving simulator with a 290° panoramic field of view constructed around a full-sized automotive cab. Vehicle control metrics, such as lateral acceleration variability and steering wheel variability, were calculated from vehicle sensor data while patients drove on a straight two-lane rural road. Linear mixed models were constructed to evaluate associations between driving performance and clinical characteristics. Results Patients were 9.5 years older than suspects (p = 0.015). OU-VFI in the glaucoma group ranged from 24 to 98% (85.6 ± 18.3; M ± SD). OU-VFI (p = .0066) was associated with MoCA total (p = .0066) and visuo-spatial and executive function sub-domain scores (p = .012). During driving simulation, patients showed greater steering wheel variability (p = 0.0001) and lateral acceleration variability (p < .0001) relative to suspects. Greater steering wheel variability was independently associated with OU-VFI (p = .0069), MoCA total scores (p = 0.028), and VFQ driving sub-scores (p = 0.0087), but not age (p = 0.61). Conclusions Poor vehicle control was independently associated with greater VF loss and worse neurocognitive performance, suggesting both factors contribute to information processing models of driving performance in glaucoma. Future research must demonstrate the external validity of current findings to on-road performance in glaucoma.

scholarly journals Takeover Quality: Assessing the Effects of Time Budget and Traffic Density with the Help of a Trajectory-Planning Method

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fabian Doubek ◽  
Erik Loosveld ◽  
Riender Happee ◽  
Joost de Winter
Keyword(s):  
Driving Simulator ◽  
Time Budget ◽  
Lateral Acceleration ◽  
Traffic Density ◽  
Density Level ◽  
Lane Change ◽  
Automated Driving ◽  
Human In The Loop ◽  
Short Time ◽  
Left Lane

In highly automated driving, the driver can engage in a nondriving task but sometimes has to take over control. We argue that current takeover quality measures, such as the maximum longitudinal acceleration, are insufficient because they ignore the criticality of the scenario. This paper proposes a novel method of quantifying how well the driver executed an automation-to-manual takeover by comparing human behaviour to optimised behaviour as computed using a trajectory planner. A human-in-the-loop study was carried out in a high-fidelity 6-DOF driving simulator with 25 participants. The takeover required a lane change to avoid roadworks on the ego-lane while taking other traffic into consideration. Each participant encountered six different takeover scenarios, with a different time budget (5 s, 7 s, or 20 s) and traffic density level (low or medium). Results showed that drivers exhibited a considerably higher longitudinal and lateral acceleration than the optimised behaviour, especially in the short time budget scenarios. In scenarios of medium traffic density, the trajectory planner showed a moderate deceleration to let a vehicle in the left lane pass; many participants, on the other hand, did not decelerate before making a lane change, resulting in a dangerous emergency brake of the left-lane vehicle. In conclusion, our results illustrate the value of assessing human takeover behaviour relative to optimised behaviour. Using the trajectory planner, we showed that human drivers are unable to behave optimally in urgent scenarios and that, in some conditions, a medium deceleration, as opposed to a maximal or minimal deceleration, is optimal.

scholarly journals Effects of non-driving related tasks on mental workload and take-over times during conditional automated driving

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Andreas Lars Müller ◽  
Natacha Fernandes-Estrela ◽  
Ruben Hetfleisch ◽  
Lukas Zecha ◽  
Bettina Abendroth
Keyword(s):  
Driving Simulator ◽  
Mental Workload ◽  
Reaction Times ◽  
Vehicle Control ◽  
Automated Driving ◽  
Level Of Automation ◽  
Spare Time ◽  
High Workload ◽  
Level 3 ◽  
Perceived Workload

Abstract Background Automated driving will be of high value in the future. While in partial-automated driving the driver must always monitor the traffic situation, a paradigm shift is taking place in the case of conditional automated driving (Level 3 according to SAE). From this level of automation onwards, the vehicle user is released from permanent vehicle control and environmental monitoring and is allowed to engage in Non-Driving Related Tasks (NDRT) in his or her newly gained spare time. These tasks can be performed until a take-over request informs the user to resume vehicle control. As the driver is still considered to be the fall-back level, this aspect of taking over control is considered especially critical. Methods While previous research projects have focused their studies on the factors influencing the take-over request, this paper focuses on the effects of NDRT on the user of the vehicle during conditional automated driving, especially on the human workload. NDRT (such as Reading, Listening, Watching a movie, Texting and Monitoring ride) were examined within a static driving simulator at the Institute of Ergonomics & Human Factors with 56 participants in an urban environment. These NDRT were tested for mental workload and the ability to take over in a critical situation. To determine the perceived workload, the subjective workload, psychophysiological activity as well as performance-based parameters of a secondary competing task performed by a were used. Results This study revealed that the selected NDRT vary significantly in their mental workload and that the workload correlates with the length of the time needed for take over control. NDRT which are associated with a high workload (such as Reading or Texting) also lead to longer reaction times.

scholarly journals Behavioral Changes to Repeated Takeovers in Highly Automated Driving: Effects of the Takeover-Request Design and the Nondriving-Related Task Modality

2018 ◽  
Vol 61 (5) ◽  
pp. 839-849 ◽  
Author(s):  
Fabienne Roche ◽  
Anna Somieski ◽  
Stefan Brandenburg
Keyword(s):  
Subjective Experience ◽  
Vehicle Control ◽  
Behavioral Changes ◽  
Behavior Changes ◽  
Automated Driving ◽  
The Road ◽  
Gaze Patterns ◽  
On The Road ◽  
Task Modality ◽  
Highly Automated Driving

Objective: We investigated drivers’ behavior and subjective experience when repeatedly taking over their vehicles’ control depending on the design of the takeover request (TOR) and the modality of the nondriving-related task (NDRT). Background: Previous research has shown that taking over vehicle control after highly automated driving provides several problems for drivers. There is evidence that the TOR design and the NDRT modality may influence takeover behavior and that driver behavior changes with more experience. Method: Forty participants were requested to resume control of their simulated vehicle six times. The TOR design (auditory or visual-auditory) and the NDRT modality (auditory or visual) were varied. Drivers’ takeover behavior, gaze patterns, and subjective workload were recorded and analyzed. Results: Results suggest that drivers change their behavior to the repeated experience of takeover situations. An auditory TOR leads to safer takeover behavior than a visual-auditory TOR. And with an auditory TOR, the takeover behavior improves with experience. Engaging in the visually demanding NDRT leads to fewer gazes on the road than the auditory NDRT. Participants’ fixation duration on the road decreased over the three takeovers with the visually demanding NDRT. Conclusions: The results imply that (a) drivers change their behavior to repeated takeovers, (b) auditory TOR designs might be preferable over visual-auditory TOR designs, and (c) auditory demanding NDRTs allow drivers to focus more on the driving scene. Application: The results of the present study can be used to design TORs and determine allowed NDRTs in highly automated driving.

scholarly journals The L3Pilot Data Management Toolchain for a Level 3 Vehicle Automation Pilot

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 809 ◽  
Author(s):  
Johannes Hiller ◽  
Sami Koskinen ◽  
Riccardo Berta ◽  
Nisrine Osman ◽  
Ben Nagy ◽  
...  
Keyword(s):  
Data Management ◽  
Reference Architecture ◽  
Automated Driving ◽  
Iterative Processing ◽  
Vehicle Owner ◽  
Level 3 ◽  
System Functioning ◽  
Comprehensive Test ◽  
Vehicle Automation ◽  
Set Up

As industrial research in automated driving is rapidly advancing, it is of paramount importance to analyze field data from extensive road tests. This paper investigates the design and development of a toolchain to process and manage experimental data to answer a set of research questions about the evaluation of automated driving functions at various levels, from technical system functioning to overall impact assessment. We have faced this challenge in L3Pilot, the first comprehensive test of automated driving functions (ADFs) on public roads in Europe. L3Pilot is testing ADFs in vehicles made by 13 companies. The tested functions are mainly of Society of Automotive Engineers (SAE) automation level 3, some of them of level 4. In this context, the presented toolchain supports various confidentiality levels, and allows cross-vehicle owner seamless data management, with the efficient storage of data and their iterative processing with a variety of analysis and evaluation tools. Most of the toolchain modules have been developed to a prototype version in a desktop/cloud environment, exploiting state-of-the-art technology. This has allowed us to efficiently set up what could become a comprehensive edge-to-cloud reference architecture for managing data in automated vehicle tests. The project has been released as open source, the data format into which all vehicular signals, recorded in proprietary formats, were converted, in order to support efficient processing through multiple tools, scalability and data quality checking. We expect that this format should enhance research on automated driving testing, as it provides a shared framework for dealing with data from collection to analysis. We are confident that this format, and the information provided in this article, can represent a reference for the design of future architectures to implement in vehicles.

scholarly journals Mode Awareness and Automated Driving—What Is It and How Can It Be Measured?

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 277 ◽  
Author(s):  
Christina Kurpiers ◽  
Bianca Biebl ◽  
Julia Mejia Hernandez ◽  
Florian Raisch
Keyword(s):  
Gold Standard ◽  
Automated Driving ◽  
Active System ◽  
Operational Design ◽  
Automation Systems ◽  
Study Results ◽  
Level 3 ◽  
No Gold Standard ◽  
Level 2 ◽  
Initial Instruction

In SAE (Society of Automotive Engineers) Level 2, the driver has to monitor the traffic situation and system performance at all times, whereas the system assumes responsibility within a certain operational design domain in SAE Level 3. The different responsibility allocation in these automation modes requires the driver to always be aware of the currently active system and its limits to ensure a safe drive. For that reason, current research focuses on identifying factors that might promote mode awareness. There is, however, no gold standard for measuring mode awareness and different approaches are used to assess this highly complex construct. This circumstance complicates the comparability and validity of study results. We thus propose a measurement method that combines the knowledge and the behavior pillar of mode awareness. The latter is represented by the relational attention ratio in manual, Level 2 and Level 3 driving as well as the controllability of a system limit in Level 2. The knowledge aspect of mode awareness is operationalized by a questionnaire on the mental model for the automation systems after an initial instruction as well as an extensive enquiry following the driving sequence. Further assessments of system trust, engagement in non-driving related tasks and subjective mode awareness are proposed.

scholarly journals Sleep inertia in automated driving: Post-sleep take-over and driving performance

2021 ◽  
Vol 150 ◽  
pp. 105918
Author(s):  
Johanna Wörle ◽  
Barbara Metz ◽  
Martin Baumann
Keyword(s):  
Driving Performance ◽  
Automated Driving ◽  
Sleep Inertia
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