The Challenge of Advanced Driver Assistance Systems Assessment: A Scale for the Assessment of the Human–Machine Interface of Advanced Driver Assistance Technology

Biondi, Francesco; Getty, Douglas; McCarty, Madeleine M.; Goethe, Rachel M.; Cooper, Joel M.; Strayer, David L.

doi:10.1177/0361198118773569

Cited by 18 publications

(6 citation statements)

References 20 publications

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“…Uchida et al designed a discrete mathematics-based trunk line coordination control model, divided the delay calculation of trunk lines into two parts, external import lanes and internal import lanes, proposed a multiobjective optimal signal timing model for trunk line cooperative control, and used a genetic algorithm to solve it [ 16 ]. Biondi et al designed a parameter dynamic adjustment strategy to better optimize the coordinated control of two-way traffic signals on trunk lines, and this model is based on the standard difference (P-ADE) algorithm [ 17 ]. The balance between global search and local search is ensured, and the results show that the advantages of this method in terms of algorithm speed, accuracy, and robustness are obvious.…”

Section: Current Status Of Researchmentioning

confidence: 99%

Vehicle Safety-Assisted Driving Technology Based on Computer Artificial Intelligence Environment

Yan

2022

Computational Intelligence and Neuroscience

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In this paper, we propose an assisted driving system implemented with a Jetson nano-high-performance embedded platform by using machine vision and deep learning technologies. The vehicle dynamics model is established under multiconditional assumptions, the path planner and path tracking controller are designed based on the model predictive control algorithm, and the local desired path is reasonably planned in combination with the behavioral decision system. The behavioral decision algorithm based on finite state machine reasonably transforms the driving state according to the environmental changes, realizes the following of the target vehicle speed, and can take effective emergency braking in time when there is a collision danger. The system can complete the motion planning by the model predictive control algorithm and control the autonomous vehicle to smoothly track the replanned local desired path to complete the lane change overtaking action, which can meet the demand of ADAS. The path planner is designed based on the MPC algorithm, solving the objective function with obstacle avoidance function, planning the optimal path that can avoid a collision, and using 5th order polynomial to fit the output local desired path points. In 5∼8 s time, the target vehicle decelerates to 48 km/h; the autonomous vehicle immediately makes a deceleration action and gradually reduces the speed difference between the two vehicles until it reaches the target speed, at which time the distance between the two vehicles is close to the safe distance, obtained by the simulation test results. The system can still accurately track the target when the vehicle is driving on a curve and timely control the desired speed change of the vehicle, and the target vehicle always maintains a safe distance. The system can be used within 50 meters.

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Section: Current Status Of Researchmentioning

confidence: 99%

Vehicle Safety-Assisted Driving Technology Based on Computer Artificial Intelligence Environment

Yan

2022

Computational Intelligence and Neuroscience

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“…Therefore, different methodological approaches such as heuristic expert assessments are necessary in these instances. Such heuristic evaluations should be based on the guidelines that are available for the topic of interest [17,18]. Regarding observations of uncontrollable events in a user study, the respective size of the sample for a verification of a certain population also increases.…”

Section: Discussionmentioning

confidence: 99%

How Many Participants Are Required for Validation of Automated Vehicle Interfaces in User Studies?

Forster¹,

Naujoks²,

Keinath³

2021

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Empirical validation and verification procedures require the sophisticated development of research methodology. Therefore, researchers and practitioners in human–machine interaction and the automotive domain have developed standardized test protocols for user studies. These protocols are used to evaluate human–machine interfaces (HMI) for driver distraction or automated driving. A system or HMI is validated in regard to certain criteria that it can either pass or fail. One important aspect is the number of participants to include in the study and the respective number of potential failures concerning the pass/fail criteria of the test protocol. By applying binomial tests, the present work provides recommendations on how many participants should be included in a user study. It sheds light on the degree to which inferences from a sample with specific pass/fail ratios to a population is permitted. The calculations take into account different sample sizes and different numbers of observations within a sample that fail the criterion of interest. The analyses show that required sample sizes increase to high numbers with a rising degree of controllability that is assumed for a population. The required sample sizes for a specific controllability verification (e.g., 85%) also increase if there are observed cases of fails in regard to the safety criteria. In conclusion, the present work outlines potential sample sizes and valid inferences about populations and the number of observed failures in a user study.

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“…Results indicate that the presence of warnings increased mean time-headway by 0.28 s, and drivers' response time to the forward collisions was 15% faster than the baseline condition (i.e., no in-vehicle crash warning system). Biondi et al (2018) developed a rating tool for assessing HMIs of various ADASs. Based on a field-experiment testing, the authors point out issues that are related to visual, auditory, and haptic warnings; for example, auditory warnings used by the rear parking sensor were not indicative of the distance of the vehicle to obstacles, visual warnings adopted by a blindspot monitor were located in unconventional locations, and accelerations operated by the lane keep assist system were in some cases uncomfortable and jolty.…”

Section: Assessment Of Hmimentioning

confidence: 99%

Assessment of Drivers’ Perceptions of Connected Vehicle–Human Machine Interface for Driving Under Adverse Weather Conditions: Preliminary Findings From Wyoming

2020

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Connected vehicle (CV) technology aims to improve drivers' situational awareness through audible and visual warnings displayed on a human-machine interface (HMI), thus reducing crashes caused by human error. This paper developed a driving simulator test bed to assess the readability and usefulness of the Wyoming CV applications. A total number of 26 professional drivers were recruited to participate in a driving-simulator study. Prior to driving the simulator, the participants were trained on both the concept of CV technology and the developed CV applications as well as the operation of the driving simulator. Three driving simulation scenarios were designed. For each scenario, participants drove two times: one with the HMI turned on and another one with the HMI turned off. After driving the simulator, a comprehensive revealed-preference survey was employed to collect the participants' perceptions of CV technology and Wyoming CV applications. Results show that the Wyoming CV applications were most favored under poor-visibility driving conditions. Among the Wyoming CV applications, forward collision warning and rerouting applications were experienced as the most useful. Approximately 89% of the participants stated that the Wyoming CV applications provided them with improved road condition information and increased their experienced safety while driving; 65% of the participants stated the CV applications and the HMI did not introduce distraction from the primary task of driving. Finally, this paper concludes that the design of CV HMI needs to balance a trade-off between the readability of the warnings and drivers' capability to safely recognize and timely respond to the received warnings.

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The Challenge of Advanced Driver Assistance Systems Assessment: A Scale for the Assessment of the Human–Machine Interface of Advanced Driver Assistance Technology

Cited by 18 publications

References 20 publications

Vehicle Safety-Assisted Driving Technology Based on Computer Artificial Intelligence Environment

Vehicle Safety-Assisted Driving Technology Based on Computer Artificial Intelligence Environment

How Many Participants Are Required for Validation of Automated Vehicle Interfaces in User Studies?

Assessment of Drivers’ Perceptions of Connected Vehicle–Human Machine Interface for Driving Under Adverse Weather Conditions: Preliminary Findings From Wyoming

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