A Study of Interpretation of Private liability for Accidents led by Level 3 Autonomous Vehicles

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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Computational Solutions Based on Bayesian Networks to Hierarchize and to Predict Factors Influencing Gender Fairness in the Transport System: Four Use Cases

Sustainability ◽

10.3390/su132011372 ◽

2021 ◽

Vol 13 (20) ◽

pp. 11372

Author(s):

Gemma Dolores Molero ◽

Sara Poveda-Reyes ◽

Ashwani Kumar Malviya ◽

Elena García-Jiménez ◽

Maria Chiara Leva ◽

...

Keyword(s):

Bayesian Networks ◽

Transport System ◽

Autonomous Vehicles ◽

Personal Space ◽

Use Cases ◽

Railway Transport ◽

Analytic Hierarchy ◽

Perception Of Safety ◽

Level 3 ◽

The Difference

Previous studies have highlighted inequalities and gender differences in the transport system. Some factors or fairness characteristics (FCs) strongly influence gender fairness in the transport system. The difference with previous studies, which focus on general concepts, is the incorporation of level 3 FCs, which are more detailed aspects or measures that can be implemented by companies or infrastructure managers and operators in order to increase fairness and inclusion in each use case. The aim of this paper is to find computational solutions, Bayesian networks, and analytic hierarchy processes capable of hierarchizing level 3 FCs and to predict by simulation their values in the case of applying some improvements. This methodology was applied to data from women in four use cases: railway transport, autonomous vehicles, bicycle sharing stations, and transport employment. The results showed that fairer railway transport requires increased personal space, hospitality rooms, help points, and helpline numbers. For autonomous vehicles, the perception of safety, security, and sustainability should be increased. The priorities for bicycle sharing stations are safer cycling paths avoiding hilly terrains and introducing electric bicycles, child seats, or trailers to carry cargo. In transport employment, the priorities are fair recruitment and promotion processes and the development of family-friendly policies.

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Multimodal Cue Combinations: A Possible Approach to Designing In-Vehicle Takeover Requests for Semi-autonomous Driving

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181319631053 ◽

2019 ◽

Vol 63 (1) ◽

pp. 1739-1743

Author(s):

Gaojian Huang ◽

Clayton Steele ◽

Xinrui Zhang ◽

Brandon J. Pitts

Keyword(s):

Autonomous Vehicles ◽

Response Times ◽

Autonomous Driving ◽

Multimodal Signals ◽

Road Sign ◽

Sign Detection ◽

Level 3 ◽

Perceived Workload ◽

Tactile Cues ◽

Vehicle Automation

The rapid growth of autonomous vehicles is expected to improve roadway safety. However, certain levels of vehicle automation will still require drivers to ‘takeover’ during abnormal situations, which may lead to breakdowns in driver-vehicle interactions. To date, there is no agreement on how to best support drivers in accomplishing a takeover task. Therefore, the goal of this study was to investigate the effectiveness of multimodal alerts as a feasible approach. In particular, we examined the effects of uni-, bi-, and trimodal combinations of visual, auditory, and tactile cues on response times to takeover alerts. Sixteen participants were asked to detect 7 multimodal signals (i.e., visual, auditory, tactile, visual-auditory, visual-tactile, auditory-tactile, and visual-auditory-tactile) while driving under two conditions: with SAE Level 3 automation only or with SAE Level 3 automation in addition to performing a road sign detection task. Performance on the signal and road sign detection tasks, pupil size, and perceived workload were measured. Findings indicate that trimodal combinations result in the shortest response time. Also, response times were longer and perceived workload was higher when participants were engaged in a secondary task. Findings may contribute to the development of theory regarding the design of takeover request alert systems within (semi) autonomous vehicles.

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Development of Quantitative Methods for Evaluating Failure Safety of Level 3 Autonomous Vehicles

The Journal of The Korea Institute of Intelligent Transport Systems ◽

10.12815/kits.2019.18.1.91 ◽

2019 ◽

Vol 18 (1) ◽

pp. 91-102

Author(s):

Dooyong Kim ◽

◽

Sangyeop Lee ◽

Hyuckkee Lee ◽

Inseong Choi ◽

...

Keyword(s):

Autonomous Vehicles ◽

Quantitative Methods ◽

Level 3

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Development of a Situational Awareness Estimation Model Considering Traffic Environment for Unscheduled Takeover Situations

International Journal of Intelligent Transportation Systems Research ◽

10.1007/s13177-020-00231-4 ◽

2020 ◽

Author(s):

Hiroaki Hayashi ◽

Naoki Oka ◽

Mitsuhiro Kamezaki ◽

Shigeki Sugano

Keyword(s):

Autonomous Vehicles ◽

Driving Simulator ◽

Situational Awareness ◽

Support Vector ◽

Steering Control ◽

Estimation Model ◽

Level 3 ◽

Estimation System ◽

Leave One Out ◽

The Relationship

Abstract In semi-autonomous vehicles (SAE level 3) that requires drivers to takeover (TO) the control in critical situations, a system needs to judge if the driver have enough situational awareness (SA) for manual driving. We previously developed a SA estimation system that only used driver’s glance data. For deeper understanding of driver’s SA, the system needs to evaluate the relevancy between driver’s glance and surrounding vehicle and obstacles. In this study, we thus developed a new SA estimation model considering driving-relevant objects and investigated the relationship between parameters. We performed TO experiments in a driving simulator to observe driver’s behavior in different position of surrounding vehicles and TO performance such as the smoothness of steering control. We adopted support vector machine to classify obtained dataset into safe and dangerous TO, and the result showed 83% accuracy in leave-one-out cross validation. We found that unscheduled TO led to maneuver error and glance behavior differed from individuals.

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Takeover Request Design in Automated Driving: A Systematic Review

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181321651296 ◽

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.

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

Frontiers in Engineering and Built Environment ◽

10.1108/febe-05-2021-0025 ◽

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.

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