Do autonomous vehicles drive like humans? A Turing approach and an application to SAE automation Level 2 cars

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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Sight distance analyses for autonomous vehicles in Civil 3D

Pollack Periodica ◽

10.1556/606.2021.00380 ◽

2021 ◽

Author(s):

Khalil Khaska ◽

Dániel Miletics

Keyword(s):

Reaction Time ◽

Human Factors ◽

Surface Properties ◽

Autonomous Vehicles ◽

Road Surface ◽

Eye Position ◽

Road Design ◽

Sight Distance ◽

Automation Level ◽

Self Driving Cars

AbstractNowadays, self-driving cars have a wide reputation among people that is constantly increasing, many manufacturers are developing their own autonomous vehicles. These vehicles are equipped with various sensors that are placed at several points in the car. These sensors provide information to control the vehicle (partially or completely, depending on the automation level). Sight distances on roads are defined according to various traffic situations (stopping, overtaking, crossing, etc.). Safety reasons require these sight distances, which are calculated from human factors (e.g., reaction time), vehicle characteristics (e.g., eye position, brakes), road surface properties, and other factors. Autodesk Civil 3D is a widely used tool in the field of road design, the software however was developed based on the characteristics of the human drivers and conventional vehicles.

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YouTube as a Source of Information in Understanding Autonomous Vehicle Consumers: Natural Language Processing Study

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119842110 ◽

2019 ◽

Vol 2673 (8) ◽

pp. 242-253 ◽

Cited By ~ 5

Author(s):

Subasish Das ◽

Anandi Dutta ◽

Tomas Lindheimer ◽

Mohammad Jalayer ◽

Zachary Elgart

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Automotive Industry ◽

Autonomous Vehicles ◽

Large Scale ◽

Keyword Search ◽

Autonomous Vehicle ◽

Perception Of Safety ◽

Automation Level

The automotive industry is currently experiencing a revolution with the advent and deployment of autonomous vehicles. Several countries are conducting large-scale testing of autonomous vehicles on private and even public roads. It is important to examine the attitudes and potential concerns of end users towards autonomous cars before mass deployment. To facilitate the transition to autonomous vehicles, the automotive industry produces many videos on its products and technologies. The largest video sharing website, YouTube.com, hosts many videos on autonomous vehicle technology. Content analysis and text mining of the comments related to the videos with large numbers of views can provide insight about potential end-user feedback. This study examines two questions: first, how do people view autonomous vehicles? Second, what polarities exist regarding (a) content and (b) automation level? The researchers found 107 videos on YouTube using a related keyword search and examined comments on the 15 most-viewed videos, which had a total of 60.9 million views and around 25,000 comments. The videos were manually clustered based on their content and automation level. This study used two natural language processing (NLP) tools to perform knowledge discovery from a bag of approximately seven million words. The key issues in the comment threads were mostly associated with efficiency, performance, trust, comfort, and safety. The perception of safety and risk increased in the textual contents when videos presented full automation level. Sentiment analysis shows mixed sentiments towards autonomous vehicle technologies, however, the positive sentiments were higher than the negative.

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The iconography of vehicle automation—a focus group study

Human-Intelligent Systems Integration ◽

10.1007/s42454-021-00034-2 ◽

2021 ◽

Author(s):

Joy Richardson ◽

Kirsten M. A. Revell ◽

Jisun Kim ◽

Neville A. Stanton

Keyword(s):

Focus Group ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

International Markets ◽

Cultural Barriers ◽

Focus Group Study ◽

Complex Information ◽

Vehicle Automation ◽

The Impact ◽

Level 2

AbstractSAE level 2 and 3 semi-autonomous vehicles are widely available but, due to the nature of automation, their in-vehicle displays are required to communicate more complex information to the driver. Examination of interfaces from a variety of manufacturers revealed a clear lack of consistency in the way key information is displayed. Different manufacturers have adopted icons varying in shape and colour to convey the same message. When driving a semi-autonomous vehicle, mode awareness is critical for trust, performance and safety. Standardisation of icons has been shown to have many benefits including opening products up to wider international markets by helping overcome language and cultural barriers, by providing a method of communication which can surpass them. However, the current lack of standardisation in icon design could cause mode confusion and has little cross-vehicle compatibility. To understand the impact of mode confusion on users, a focus group was held in which participants were asked to interpret the meaning of icons from a variety of different driver interfaces. Ambiguity in user interpretations makes the case for the introduction of new ISO standard icons to better support drivers in SAE level 2 and 3 automated vehicles.

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The use of scanning electron microscopy in interpreting chenopodium remains from three archaeological sites in northwestern Iowa

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100130195 ◽

1992 ◽

Vol 50 (2) ◽

pp. 1112-1113

Author(s):

Douglas William Jones

Keyword(s):

Electron Microscopy ◽

Scanning Electron Microscopy ◽

Archaeological Sites ◽

Late Prehistoric ◽

Eastern United States ◽

Chenopodium Berlandieri ◽

Seed Identification ◽

Level 2 ◽

Scanning Electron

Within the past 20 years, archaeobotanical research in the Eastern United States has documented an early agricultural complex before the dominance of the Mesoamerican domesticates (corn, beans, and squash) in late prehistoric and historic agricultural systems. This early agricultural complex consisted of domesticated plants such as Iva annua var.macrocarpa (Sumpweed or Marshelder), Hellanthus annuus (Sunflower) and Chenopodium berlandieri, (Goosefoot or Lasbsquarters), and heavily utilized plants such as Polygonum erectum (Erect Knotweed), Phalaris caroliniana (May grass), and Hordeum pusillum (Little Barley).Recent research involving the use of Scanning Electron Microscopy (SEM) specifically on Chenopodium has established diagnostic traits of wild and domesticated species seeds. This is important because carbonized or uncarbonized seeds are the most commonly recovered Chenopodium material from archaeological sites. The diagnostic seed traits assist archaeobotanists in identification of Chenopodium remains and provide a basis for evaluation of Chenopodium utilization in a culture's subsistence patterns. With the aid of SEM, an analysis of Chenopodium remains from three Late Prehistoric sites in Northwest Iowa (Blood Run [Oneota culture], Brewster [Mill Creek culture], and Chan-Ya-Ta [Mill Creek culture]) has been conducted to: 1) attempt seed identification to a species level, 2) evaluate the traits of the seeds for classification as either wild or domesticated, and 3) evaluate the role of Chenopodium utilization in both the Oneota and Mill Creek cultures.

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