Keeping the driver in the loop through semi-automated or manual lane changes in conditionally automated driving

Dillmann, Jeremy; Hartigh, Ruud den; Kurpiers, Christina; Pelzer, Julia; Raisch, Florian; Cox, Ralf F. A.; Waard, Dick de

doi:10.1016/j.aap.2021.106397

Cited by 14 publications

(7 citation statements)

References 51 publications

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“…Based on these differences and considering that these works do not share similar key performance indicators, a numerical comparison is not possible. The same is true for other related works that implemented shared control in overtaking maneuvers but did not consider oncoming traffic [ 18 ], or focused on the blind spot scenario instead [ 16 ]. Therefore, the comparison with similar works is presented in a qualitative manner rather than a quantitative one.…”

Section: Discussionmentioning

confidence: 57%

“…Nishimura [ 17 ] evaluates the situation of overtaking, but with a focus only on the control transitions from automated to manual and vice versa, but not on steering correction. In another work, Dillman [ 18 ] presents a comparison of overtaking modes, including a semi-automated one based on shared control, where the automation performs an overtake as soon as the driver puts the hands on the steering wheel and confirms the intention to overtake. However, to the knowledge of the authors, the current literature does not present a system based on shared control that handles control transitions and avoids dangerous overtaking maneuvers on roads with oncoming traffic.…”

Section: Introductionmentioning

confidence: 99%

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Can Shared Control Improve Overtaking Performance? Combining Human and Automation Strengths for a Safer Maneuver

Marcano

Tango

Sarabia

et al. 2022

Sensors

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The Shared Control (SC) cooperation scheme, where the driver and automated driving system control the vehicle together, has been gaining attention through the years as a promising option to improve road safety. As a result, advanced interaction methods can be investigated to enhance user experience, acceptance, and trust. Under this perspective, not only the development of algorithms and system applications are needed, but it is also essential to evaluate the system with real drivers, assess its impact on road safety, and understand how drivers accept and are willing to use this technology. In this sense, the contribution of this work is to conduct an experimental study to evaluate if a previously developed shared control system can improve overtaking performance on roads with oncoming traffic. The evaluation is performed in a Driver-in-the-Loop (DiL) simulator with 13 real drivers. The system based on SC is compared against a vehicle with conventional SAE-L2 functionalities. The evaluation includes both objective and subjective assessments. Results show that SC proved to be the best solution for assisting the driver during overtaking in terms of safety and acceptance. The SC’s longer and smoother control transitions provide benefits to cooperative driving. The System Usability Scale (SUS) and the System Acceptance Scale (SAS) questionnaire show that the SC system was perceived as better in terms of usability, usefulness, and satisfaction.

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Section: Discussionmentioning

confidence: 57%

Section: Introductionmentioning

confidence: 99%

Can Shared Control Improve Overtaking Performance? Combining Human and Automation Strengths for a Safer Maneuver

Marcano

Tango

Sarabia

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…There has been more and more research about automated vehicles in recent years, however much of them has focused on the driver's responses to critical conditions & Dillmann et al, 2021. Little is known about driver behavior in non-emergency driving situations, such as driverinitiated overtaking maneuvers which is one main reason of crashes.…”

Section: Vehicle Typementioning

confidence: 99%

A Preliminary Comparison of Drivers’ Overtaking behavior between Partially Automated Vehicles and Conventional Vehicles

Liu

Zhao

2022

Proceedings of the Human Factors and Ergonomics Society Annual

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The study investigated drivers' overtaking behavior in partially automated vehicles and conventional vehicles under different traffic density conditions. Twelve participants "drove" a simulated vehicle on a 10-mile straight, two-way rural interstate highway in 4 scenarios (2 (traffic density: Low, High) x 2 (vehicle type: Partially automated vehicle, Conventional vehicle)) in random orders. Participants' driving performance, eye movement, brain activity, and workload were collected and analyzed. Results indicated that vehicle type significantly affects speed standard deviation, pupil dilation and workload. Results also showed that traffic density significantly affects average speed and workload. The findings should help improve the design of automated vehicles and thus improve traffic efficiency.

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“…Taking back control from automation can be adversely influenced if the driver is out of the loop (Merat et al, 2018). Recently, research has investigated being out of the loop from a perception-action theory perspective (Dillmann, Den Hartigh, Kurpiers, Pelzer, et al, 2021;Mole et al, 2019). From this perspective, drivers' being in the loop is seen as being in an active cycle of perception and action (Fajen & Devaney, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…If drivers leave the loop during automated driving, they may need time to reattune to relevant visual information and re-calibrate their actions (Brand & de Oliveira, 2017;Mole et al, 2019;Russell et al, 2016). Indeed, leaving the loop during automated driving has been associated with reduced glances at relevant sources of perceptual information (Dillmann, Den Hartigh, Kurpiers, Pelzer, et al, 2021;Schnebelen et al, 2020), and deteriorating motor-perceptual calibration in driving actions when taking back control .…”

Section: Introductionmentioning

confidence: 99%

Keeping the Driver in the Loop While Driving With Conditional Automation

Dillmann¹

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The automation of vehicles can be divided into several steps (SAE International, 2018). Level 0 contains no automation at all. Level 1 contains driving assistance that is either longitudinal or lateral. For instance, cruise control or adaptive cruise control would assist longitudinal vehicle control by holding or adapting velocity. At partial automation (Level 2) both lateral and longitudinal driving assistance systems are combined to support the driver. An example is the combination of adaptive cruise control and lane centering functions. In combination these two assistance functions support the driver in staying in the lane and maintaining a certain speed. However, at this level of automation, the driver remains the labels entail (Flach, 1995;Woods, 1993) and shifting scientific endeavors away from observable human performance (Dekker & Hollnagel, 2004). The Loop Defined From Perception-Action Theory PerspectiveGenerally speaking, perception-action theory originates from James Gibson's seminal work on understanding drivers' and pilots' capacity to control movement adequately (Gibson et al., 1955;Gibson & Crooks, 1938). Building on Gibson's later work (Gibson, 1986) the perception-action theory approach investigates how drivers locomote by interacting with the environment through the process of perception and action loops (Bootsma, 1998;Harrison et al., 2016;Mathieu et al., 2017a). For example, the optical expansion of a stop sign in the retina of drivers guides their braking actions towards it (DeLucia et al., 2016;Lee, 1976;Morando et al., 2016). In turn, the action of braking changes the optical expansion of the stop sign relative to the driver, thereby closing the perception-action loop. Iterations of these perception-action loops allow driving behavior to emerge (Harrison et al., 2016;Mathieu et al., 2017a).In order to understand how drivers can be kept in the loop, it is necessary to understand two key processes underlying the perception-action loop: perceptual attunement and perceptual-motor calibration. Perceptual attunement entails that a driver is exposed to -and able to detect and track -the specifying (i.e. task-relevant) visual information (Bootsma, 1998). In the vehicle braking example above, drivers are perceptually attuned to visual variables such as looming, specifying the relative distance to other vehicles (Fajen & Devaney, 2006). In lane keeping tasks, drivers have been shown to perceptually attune to visual variables such as optical flow, bearing-and slay angles (Li & Chen, 2010), or the inner point of approaching curves (Land & Lee, 1994).To close the perception-action loop, drivers must be proficient in scaling their motor responses adequately to this information, a process termed perceptualmotor calibration (Brand & de Oliveira, 2017;Van Andel et al., 2017). Perceptualmotor calibration is not static but dynamically dependent on the situation. Drivers have been shown to need a period of perceptual-motor calibration when tailwind is introduced in a vehicle braking task (Fajen, 2005b) or wh...

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Keeping the driver in the loop through semi-automated or manual lane changes in conditionally automated driving

Cited by 14 publications

References 51 publications

Can Shared Control Improve Overtaking Performance? Combining Human and Automation Strengths for a Safer Maneuver

Can Shared Control Improve Overtaking Performance? Combining Human and Automation Strengths for a Safer Maneuver

A Preliminary Comparison of Drivers’ Overtaking behavior between Partially Automated Vehicles and Conventional Vehicles

Keeping the Driver in the Loop While Driving With Conditional Automation

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