Takeover Request Design in Automated Driving: A Systematic Review

2021 ◽  
Vol 65 (1) ◽  
pp. 868-872
Author(s):  
Kevin Joel Salubre ◽  
Dan Nathan-Roberts
Keyword(s):  
Systematic Review ◽  
Current Literature ◽  
Autonomous Vehicles ◽  
Manual Control ◽  
Automated Driving ◽  
Lateral Control ◽  
Vehicle System ◽  
Level 3

Autonomous vehicles (AV) with “level 3” automation and above are expected to take full longitudinal and lateral control, which relinquishes the driver from manual control and allows for engagement with non-driving-related tasks. Despite the advance nature of a level 3 vehicle, system limitations can occur, and the driver is expected to re-engage in manual driving at a moment’s notice. Current literature has been focused on takeover performance during a takeover request (TOR) and the effects of multimodal warnings, but there is little consensus on how modality stimulus is presented. This systematic review summarizes the current designs and implementations of TORs of level 3 AVs and above. Identified themes in the review were categorized into three sections: non-driving-related tasks, driving scenarios, and takeover modality. A summary of how researchers utilized these themes in the current literature are discussed as well as implications and further research.

The Impact of Mental States on Semi-autonomous Driving Takeover Performance: A Systematic Review

2020 ◽  
Vol 64 (1) ◽  
pp. 1372-1376
Author(s):  
Gaojian Huang ◽  
Christine Petersen ◽  
Brandon J. Pitts
Keyword(s):  
Systematic Review ◽  
Autonomous Vehicles ◽  
Response Times ◽  
Autonomous Driving ◽  
Mental States ◽  
Manual Control ◽  
Monitoring Systems ◽  
Wide Range ◽  
Different Types ◽  
The Impact

Semi-autonomous vehicles still require drivers to occasionally resume manual control. However, drivers of these vehicles may have different mental states. For example, drivers may be engaged in non-driving related tasks or may exhibit mind wandering behavior. Also, monitoring monotonous driving environments can result in passive fatigue. Given the potential for different types of mental states to negatively affect takeover performance, it will be critical to highlight how mental states affect semi-autonomous takeover. A systematic review was conducted to synthesize the literature on mental states (such as distraction, fatigue, emotion) and takeover performance. This review focuses specifically on five fatigue studies. Overall, studies were too few to observe consistent findings, but some suggest that response times to takeover alerts and post-takeover performance may be affected by fatigue. Ultimately, this review may help researchers improve and develop real-time mental states monitoring systems for a wide range of application domains.

scholarly journals Towards standardized metrics for measuring takeover performance in conditionally automated driving: A systematic review

2021 ◽  
Vol 65 (1) ◽  
pp. 1065-1069
Author(s):  
Yining Cao ◽  
Feng Zhou ◽  
Elizabeth M. Pulver ◽  
Lisa J. Molnar ◽  
Lionel P. Robert ◽  
...  
Keyword(s):  
Systematic Review ◽  
Performance Metrics ◽  
Empirical Literature ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
Wide Range ◽  
Level 3

A particular concern with SAE Level 3 automated vehicles is the takeover transition from the automated vehicle to the driver. Prior research has employed a wide range of metrics for measuring takeover performance. However, the lack of a set of standard metrics for measuring takeover performance makes it difficult to consolidate findings and summarize the influence of different factors. This article presents a review of the metrics employed in empirical literature examining takeover transitions in Level 3 automated driving and proposes a framework for standardizing the objective takeover performance metrics.

scholarly journals Tradeoffs between safe/comfortable headways versus mobility-enhancing headways in an automated driving environment: preliminary insights using a driving simulator experiment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yujie Li ◽  
Tiantian Chen ◽  
Sikai Chen ◽  
Samuel Labi
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Research Question ◽  
Probit Analysis ◽  
Automated Driving ◽  
Content Type ◽  
Driving Mode ◽  
Level 3 ◽  
Autonomous Mobility ◽  
User Friendly

PurposeThe anticipated benefits of connected and autonomous vehicles (CAVs) include safety and mobility enhancement. Small headways between successive vehicles, on one hand, can cause increased capacity and throughput and thereby improve overall mobility. On the other hand, small headways can cause vehicle occupant discomfort and unsafety. Therefore, in a CAV environment, it is important to determine appropriate headways that offer a good balance between mobility and user safety/comfort.Design/methodology/approachIn addressing this research question, this study carried out a pilot experiment using a driving simulator equipped with a Level-3 automated driving system, to measure the threshold headways. The Method of Constant Stimuli (MCS) procedure was modified to enable the estimation of two comfort thresholds. The participants (drivers) were placed in three categories (“Cautious,” “Neutral” and “Confident”) and 250 driving tests were carried out for each category. Probit analysis was then used to estimate the threshold headways that differentiate drivers' discomfort and their intention to re-engage the driving tasks.FindingsThe results indicate that “Cautious” drivers tend to be more sensitive to the decrease in headways, and therefore exhibit greater propensity to deactivate the automated driving mode under a longer headway relative to other driver groups. Also, there seems to exist no driver discomfort when the CAV maintains headway up to 5%–9% shorter than the headways they typically adopt. Further reduction in headways tends to cause discomfort to drivers and trigger take over control maneuver.Research limitations/implicationsIn future studies, the number of observations could be increased further.Practical implicationsThe study findings can help guide specification of user-friendly headways specified in the algorithms used for CAV control, by vehicle manufacturers and technology companies. By measuring and learning from a human driver's perception, AV manufacturers can produce personalized AVs to suit the user's preferences regarding headway. Also, the identified headway thresholds could be applied by practitioners and researchers to update highway lane capacities and passenger-car-equivalents in the autonomous mobility era.Originality/valueThe study represents a pioneering effort and preliminary pilot driving simulator experiment to assess the tradeoffs between comfortable headways versus mobility-enhancing headways in an automated driving environment.

Unsettled Topics Concerning Coating Detection by LiDAR in Autonomous Vehicles

2021 ◽  
Author(s):  
Cristina Porcel Magnusson ◽  
Keyword(s):  
Autonomous Vehicles ◽  
System Development ◽  
Light Detection And Ranging ◽  
Automated Driving ◽  
Radar Sensors ◽  
Object Distance ◽  
Vehicle System ◽  
Automotive Paint ◽  
Multiple Devices ◽  
New Applications

Autonomous vehicles (AVs) utilize multiple devices, like high-resolution cameras and radar sensors, to interpret the driving environment and achieve full autonomy. One of these instruments—the light detection and ranging (LiDAR) sensor—utilizes pulsed infrared (IR) light, typically at wavelengths of 905 nm or 1,550 nm, to calculate object distance and position. Exterior automotive paint covers an area larger than any other exterior material. Therefore, understanding how LiDAR wavelengths interact with vehicle coatings is extremely important for the safety of future automated driving technologies. Sensing technologies and materials are two different industries that have not directly interacted in the perception and system sense. With the new applications in the AV industry, multidisciplinary approaches need to be taken to ensure reliability and safety in the future. Unsettled Topics Concerning Coating Detection by LiDAR in Autonomous Vehicles provides a transversal view of different industry segments, from pigment and coating manufacturers to LiDAR components and vehicle system development and integration. The report includes a structured decomposition of the different variables and technologies involved.

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 Electrical Stimulation of Injected Muscles to Boost Botulinum Toxin Effect on Spasticity: Rationale, Systematic Review and State of the Art

Toxins ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 303
Author(s):  
Alessandro Picelli ◽  
Mirko Filippetti ◽  
Giorgio Sandrini ◽  
Cristina Tassorelli ◽  
Roberto De Icco ◽  
...  
Keyword(s):  
Systematic Review ◽  
Electrical Stimulation ◽  
Botulinum Toxin ◽  
Current Literature ◽  
Electric Stimulation ◽  
Botulinum Toxin Type A ◽  
Type A ◽  
Botulinum Toxin Type ◽  
Botulinum Toxins ◽  
Stimulation Of

Botulinum toxin type A (BoNT-A) represents a first-line treatment for spasticity, a common disabling consequence of many neurological diseases. Electrical stimulation of motor nerve endings has been reported to boost the effect of BoNT-A. To date, a wide range of stimulation protocols has been proposed in the literature. We conducted a systematic review of current literature on the protocols of electrical stimulation to boost the effect of BoNT-A injection in patients with spasticity. A systematic search using the MeSH terms “electric stimulation”, “muscle spasticity” and “botulinum toxins” and strings “electric stimulation [mh] OR electrical stimulation AND muscle spasticity [mh] OR spasticity AND botulinum toxins [mh] OR botulinum toxin type A” was conducted on PubMed, Scopus, PEDro and Cochrane library electronic databases. Full-text articles written in English and published from database inception to March 2021 were included. Data on patient characteristics, electrical stimulation protocols and outcome measures were collected. This systematic review provides a complete overview of current literature on the role of electrical stimulation to boost the effect of BoNT-A injection for spasticity, together with a critical discussion on its rationale based on the neurobiology of BoNT-A uptake.

scholarly journals Driver seat comfort for level 3-4 autonomous vehicles

Work ◽  
2021 ◽  
Vol 68 (s1) ◽  
pp. S111-S118
Author(s):  
Neil J. Mansfield ◽  
Kartikeya Walia ◽  
Aditya Singh
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Case Methods ◽  
Driving Mode ◽  
Full Control ◽  
Seat Comfort ◽  
Autonomous Mode ◽  
Different Types ◽  
Level 3 ◽  
Level 2

BACKGROUND: Autonomous vehicles can be classified on a scale of automation from 0 to 5, where level 0 corresponds to vehicles that have no automation to level 5 where the vehicle is fully autonomous and it is not possible for the human occupant to take control. At level 2, the driver needs to retain attention as they are in control of at least some systems. Level 3-4 vehicles are capable of full control but the human occupant might be required to, or desire to, intervene in some circumstances. This means that there could be extended periods of time where the driver is relaxed, but other periods of time when they need to drive. OBJECTIVE: The seat must therefore be designed to be comfortable in at least two different types of use case. METHODS: This driving simulator study compares the comfort experienced in a seat from a production hybrid vehicle whilst being used in a manual driving mode and in autonomous mode for a range of postures. RESULTS: It highlights how discomfort is worse for cases where the posture is non-optimal for the task. It also investigates the design of head and neckrests to mitigate neck discomfort, and shows that a well-designed neckrest is beneficial for drivers in autonomous mode.

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