Keeping the Driver in the Loop: The ‘Other’ Ethics of Automation

Banks, Victoria A.; Shaw, Emily; Large, David R.

doi:10.1007/978-3-319-96074-6_8

Cited by 5 publications

(3 citation statements)

References 53 publications

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“…34 Once these phases have been accomplished, the ADAS scans once more the surrounding to monitor its evolution and adapt consequently, considering also the eventual actions performed by the drivers of both vehicles (driver-in-the-loop). 35 If the time step between two scans is sufficiently low (e.g. 0.1 s), 36 changes to the surrounding are already comprehensive of drivers’ intervention: 37 in this case, it is not necessary to foresee the scenario evolution making use of complex models for driver behaviour.…”

Section: Methodsmentioning

confidence: 99%

Adaptive intervention logic for automated driving systems based on injury risk minimization

Vangi

Virga

Gulino

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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Performance improvement of advanced driver assistance systems yields two major benefits: increasingly rapid progress towards autonomous driving and a simultaneous advance in vehicle safety. Integration of multiple advanced driver assistance systems leads to the so-called automated driving system, which can intervene jointly on braking and steering to avert impending crashes. Nevertheless, obstacles such as stationary vehicles and buildings can interpose between the opponent vehicles and the working field of advanced driver assistance systems’ sensors, potentially resulting in an inevitable collision state. Currently available devices cannot properly handle an inevitable collision state, because its occurrence is not subject to evaluations by the system. In the present work, criteria for intervention on braking and steering are introduced, based on the vehicle occupants’ injury risk. The system must monitor the surrounding and act on the degrees of freedom adapting to the evolution of the scenario, following an adaptive logic. The model-in-the-loop, software-in-the-loop and hardware-in-the-loop for such adaptive intervention are first introduced. To highlight the potential benefits offered by the adaptive advanced driver assistance systems, simulation software has been developed. The adaptive logic has been tested in correspondence of three inevitable collision state conditions between two motor vehicles: at each instant, the adaptive logic attitude of creating impact configurations associated with minimum injury risk is ultimately demonstrated.

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Section: Methodsmentioning

confidence: 99%

Adaptive intervention logic for automated driving systems based on injury risk minimization

Vangi

Virga

Gulino

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…Using an appropriate driver model [44,45], it is possible to evaluate impact eccentricity at the instant of collision, based on the driver's reaction time and according to the time-to-collision at which the audible warning is activated. The visualization on the CMI-V r plane hence directly identifies, both a priori and a posteriori, the variation in the efficiency of a specific ADAS as the driver's behaviour changes (while maintaining the driverin-the-loop [46]). This possibility is extremely promising for the near future, where it is expected that ADAS systems capable of also intervening on the steering degree will be subjected to EuroNCAP tests starting from 2022; given the coexistence of two diverse systems (man and 2, moving from the crash in the Reference Scenario to that in the Modified Scenario Fig.…”

Section: Future Applicationsmentioning

confidence: 99%

Prospective and retrospective performance assessment of Advanced Driver Assistance Systems in imminent collision scenarios: the CMI-Vr approach

Gulino

Fiorentino²,

Vangi

2022

Eur. Transp. Res. Rev.

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Structured abstract Introduction Prospective and retrospective performance assessment of Advanced Driver Assistance Systems (ADASs) is fundamental to pilot future enhancements for active safety devices. In critical road scenarios between two vehicles where ADAS activation enables collision mitigation only, currently available assessment methodologies rely on the reconstruction of the impact phase consequent to the specific intervention on braking and steering: the velocity change sustained by the vehicle in the collision ($$\Delta V$$ Δ V ) is retrieved, so that IR decrease for the vehicle occupants can be obtained by appropriate Injury Risk (IR) models. However, information regarding the ADAS performance is available only after the impact phase reconstruction and not just as when the criticality occurs in the pre-impact phase: the best braking and steering alternative cannot be immediately envisaged, since a direct correlation lacks between the braking/steering intervention and IR. Method This work highlights an ADAS performance assessment method based on the disaggregation of $$\Delta V$$ Δ V in the two pre-impact parameters closing velocity at collision ($$V_r$$ V r ) and impact eccentricity, represented by the Crash Momentum Index (CMI). Such a disaggregation leads to the determination of IR based solely on impact configuration between the vehicles, without directly considering the impact phase. The performance of diverse ADASs in terms of intervention logic are directly comparable based on the resulting impact configuration, associated with a single coordinate in the CMI-$$V_r$$ V r plane and a sole IR value as a consequence. Results The CMI-$$V_r$$ V r approach is employable for both purposes of prospective and retrospective performance assessment of ADAS devices. To illustrate the advantages of the methodology, a solution for prospective assessment based on the CMI-$$V_r$$ V r plane is initially proposed and applied to case studies: this provides direct suggestions regarding the most appropriate interventions on braking and steering for IR minimization, fundamental in the tuning or development phase of an ADAS. A method for retrospective assessment is ultimately contextualized in the EuroNCAP “Car-to-Car Rear moving” test for an Inter-Urban Autonomous Emergency Braking system, a device implemented on a significant portion of the circulating fleet. Conclusions Based on the evidenced highlights, it is demonstrated that the approach provides complementary information compared to well-established performance assessment methodologies in all stages of an ADAS life cycle, by suggesting a direct physical connection in the pre-impact phase between the possible ADAS interventions and the foreseeable injury outcomes.

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“…The arrival of autonomous vehicle technologies is expected to revolutionize human daily transportation and promises to enhance road safety, comfort, and mobility. This rapidly growing field has established a thriving industry in just a matter of a decade [8,78]. "Safety Driver" is born of this trend.…”

Section: Introductionmentioning

confidence: 99%

Work with AI and Work for AI: Autonomous Vehicle Safety Drivers’ Lived Experiences

Chu

Zong

Shu

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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The development of Autonomous Vehicle (AV) has created a novel job, the safety driver, recruited from experienced drivers to supervise and operate AV in numerous driving missions. Safety drivers usually work with non-perfect AV in high-risk real-world traffic environments for road testing tasks. However, this group of workers is under-explored in the HCI community. To fill this gap, we conducted semi-structured interviews with 26 safety drivers. Our results present how safety drivers cope with defective algorithms and shape and calibrate their perceptions while working with AV. We found that, as front-line workers, safety drivers are forced to take risks accumulated from the AV industry upstream and are also confronting restricted self-development in working for AV development. We contribute the first empirical evidence of the lived experience of safety drivers, the first passengers in the development of AV, and also the grassroots workers for AV, which can shed light on future human-AI interaction research.

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Keeping the Driver in the Loop: The ‘Other’ Ethics of Automation

Cited by 5 publications

References 53 publications

Adaptive intervention logic for automated driving systems based on injury risk minimization

Adaptive intervention logic for automated driving systems based on injury risk minimization

Prospective and retrospective performance assessment of Advanced Driver Assistance Systems in imminent collision scenarios: the CMI-Vr approach

Work with AI and Work for AI: Autonomous Vehicle Safety Drivers’ Lived Experiences

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