User-centred design evaluation of symbols for adaptive cruise control (ACC) and lane-keeping assistance (LKA)

Perrier, Mickaël J. R.; Louw, Tyron; Carsten, Oliver

doi:10.1007/s10111-021-00673-0

Cited by 12 publications

(6 citation statements)

References 34 publications

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“…In cases where the driver is inattentive to the LDW and drifts out of the lane, the steering system will intervene to guide the vehicle back into the lane. The study in [28] observed that "drivers must familiarise themselves with various symbols to correctly identify and activate the system they wish to be using," as OEMs often replace standard graphical symbols with their own preferences. Therefore, it is crucial to consider the learning, trust, and acceptance of AV systems for the continuous development and evaluation of UX and BA over time.…”

Section: E) A-esc-based Bamentioning

confidence: 99%

User Experience and Behavioural Adaptation Based on Repeated Usage of Vehicle Automation: Online Survey

Mbelekani,

Bengler

2024

IJACSA

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For years, Level 2 vehicle automation systems (VAS) have been commercially available, yet the extent to which users comprehend their capabilities and limitations remains largely unclear. This study aimed to evaluate user knowledge regarding Level 2 VAS and explore the correlation between user experiences (UX), behavioural adaptations, trust, and acceptance. By using an online survey, we sought to deepen understanding of how UX, trust, and acceptance of Level 2 automated vehicles (AVs) evolve with prolonged use in urban traffic. The survey, comprising demographic data and knowledge inquiries (automated driving experience and timeframes, vehicle operation competency, driving skills over long-term use of automation, the learning process, automation-induced effects, trust in automation, and ADS researchers and manufacturers), was completed by various drivers (N=16). This investigation focused on users' long-term experiences with automation in urban traffic. Consequently, we offer user-centric transformative insights into users' experiences with driving automation in urban traffic settings. Results revealed that users' knowledge of automation exhibits their learning patterns, trust and acceptance. Moreover, users' attitudes trust, and acceptance varies across different user profiles. What we have also learned about UX and the changing nature of user behaviours towards automation is that, automated driving changes influence the safety and risk conditions in which users and AVs interact. These findings can inform the development of interaction design strategies and policy aimed at enhancing UX of AV users.

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Section: E) A-esc-based Bamentioning

confidence: 99%

User Experience and Behavioural Adaptation Based on Repeated Usage of Vehicle Automation: Online Survey

Mbelekani,

Bengler

2024

IJACSA

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“…Lane Keeping Assist (LKA): this system helps the driver keep the car in its lane, which can be especially important when overtaking cyclists. If the vehicle comes dangerously close to a cyclist or deviates from its lane, the system can intervene in the steering to correct the trajectory ( 36 ). LKA technology uses advanced sensors to monitor the vehicle’s position within the lane.…”

Section: Introductionmentioning

confidence: 99%

“Cyclist at 12 o’clock!”: a systematic review of in-vehicle advanced driver assistance systems (ADAS) for preventing car-rider crashes

Useche,

Faus,

Alonso

2024

Front. Public Health

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IntroductionWhile Advanced Driver Assistance Systems (ADAS) have become a prominent topic in road safety research, there has been relatively little discussion about their effectiveness in preventing car collisions involving specific vulnerable road users, such as cyclists. Therefore, the primary objective of this systematic literature review is to analyze the available evidence regarding the effectiveness of in-vehicle ADAS in preventing vehicle collisions with cyclists.MethodsTo achieve this goal, this systematic review analyzed a selection of original research papers that examined the effectiveness of ADAS systems in preventing car-cyclist collisions. The review followed the PRISMA protocol, which led to the extraction of 21 eligible studies from an initial pool of 289 sources indexed in the primary scientific literature databases. Additionally, word community-based content analyses were used to examine the research topics and their links within the current scientific literature on the matter.ResultsAlthough the current number of studies available is still scarce (most sources focus on car-motorcyclist or car-pedestrian crashes), the overall quality of the available studies has been reasonably good, as determined by the selected evaluation methods. In terms of studies’ outcomes, the literature supports the value of in-vehicle ADAS for preventing car-cyclist crashes. However, threatful side effects such as unrealistic expectations of these systems and users’ overconfidence or desensitization are also highlighted, as well as the need to increase driver training and road user awareness.ConclusionThe results of this study suggest that Advanced Driver Assistance Systems have significant potential to contribute to the prevention of driving crashes involving cyclists. However, the literature emphasizes the importance of concurrently enhancing user-related skills in both ADAS use and road-user interaction through educational and training initiatives. Future research should also address emerging issues, such as ADAS-related behavioral ergonomics, and conduct long-term effectiveness assessments of ADAS in preventing car-cycling crashes and their subsequent injuries.Systematic review registrationPROSPERO, unique identifier CRD42024505492, https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=505492.

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“…Deep learning is used extensively in autonomous driving and augmented reality. Only a few of the complex and specialized functions needed for autonomous driving are automatic, for example, lane-keeping assistance, emergency braking (AEB) [1], active cruise control [2], forward collision warning (FCW) [3] and crash avoidance [4]. FCW and AEB are two of the first accident-avoidance features to be tested.…”

Section: Introductionmentioning

confidence: 99%

Navigating an Automated Driving Vehicle via the Early Fusion of Multi-Modality

Haris

Głowacz

2022

Sensors

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The ability of artificial intelligence to drive toward an intended destination is a key component of an autonomous vehicle. Different paradigms are now being employed to address artificial intelligence advancement. On the one hand, modular pipelines break down the driving model into submodels, such as perception, maneuver planning and control. On the other hand, we used the end-to-end driving method to assign raw sensor data directly to vehicle control signals. The latter is less well-studied but is becoming more popular since it is easier to use. This article focuses on end-to-end autonomous driving, using RGB pictures as the primary sensor input data. The autonomous vehicle is equipped with a camera and active sensors, such as LiDAR and Radar, for safe navigation. Active sensors (e.g., LiDAR) provide more accurate depth information than passive sensors. As a result, this paper examines whether combining the RGB from the camera and active depth information from LiDAR has better results in end-to-end artificial driving than using only a single modality. This paper focuses on the early fusion of multi-modality and demonstrates how it outperforms a single modality using the CARLA simulator.

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User-centred design evaluation of symbols for adaptive cruise control (ACC) and lane-keeping assistance (LKA)

Cited by 12 publications

References 34 publications

User Experience and Behavioural Adaptation Based on Repeated Usage of Vehicle Automation: Online Survey

User Experience and Behavioural Adaptation Based on Repeated Usage of Vehicle Automation: Online Survey

“Cyclist at 12 o’clock!”: a systematic review of in-vehicle advanced driver assistance systems (ADAS) for preventing car-rider crashes

Navigating an Automated Driving Vehicle via the Early Fusion of Multi-Modality

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