Why do drivers and automation disengage the automation? Results from a study among Tesla users

A better understanding of automation disengagements can impact the safety and efficiency of automated systems. This study investigates the factors contributing to driver- and system-initiated disengagements by analyzing semi-structured interviews with 103 users of Tesla's Autopilot and FSD Beta. Through an examination of the data, main categories and sub-categories of disengagements were identified, which led to the development of a triadic model of automation disengagements. The model treats automation and human operators as equivalent agents. It suggests that human operators disengage automation when they anticipate failure, observe unnatural or unwanted automation behavior (e.g., erratic steering, running red lights), or believe the automation is not suited for certain environments (e.g., inclement weather, non-standard roads). Human operators' negative experiences, such as frustration, feelings of unsafety, and distrust, are also incorporated into the model, as these emotions can be triggered by (anticipated) automation behaviors. The automation, in turn, monitors human operators and may disengage itself if it detects insufficient vigilance or traffic rule violations. Moreover, human operators can be influenced by the reactions of passengers and other road users, leading them to disengage automation if they sense discomfort, anger, or embarrassment due to the system's actions. This research offers insights into the factors contributing to automation disengagements, highlighting not only the concerns of human operators but also the social aspects of the phenomenon. Furthermore, the findings provide information on potential edge cases of automated vehicle technology, which may help to enhance the safety and efficiency of such systems.Comment: 51 pages, 1 figur

What impressions do users have after a ride in an automated shuttle? An interview study

In the future, automated shuttles may provide on-demand transport and serve as feeders to public transport systems. However, automated shuttles will only become widely used if they are accepted by the public. This paper presents results of an interview study with 30 users of an automated shuttle on the EUREF (Europäisches Energieforum) campus in Berlin-Schöneberg to obtain in-depth understanding of the acceptance of automated shuttles as feeders to public transport systems. From the interviews, we identified 340 quotes, which were classified into six categories: (1) expectations about the capabilities of the automated shuttle (10% of quotes), (2) evaluation of the shuttle performance (10%), (3) service quality (34%), (4) risk and benefit perception (15%), (5) travel purpose (25%), and (6) trust (6%). The quotes indicated that respondents had idealized expectations about the technological capabilities of the automated shuttle, which may have been fostered by the media. Respondents were positive about the idea of using automated shuttles as feeders to public transport systems but did not believe that the shuttle will allow them to engage in cognitively demanding activities such as working. Furthermore, 20% of respondents indicated to prefer supervision of shuttles via an external control room or steward on board over unsupervised automation. In conclusion, even though the current automated shuttle did not live up to the respondents’ expectations, respondents still perceived automated shuttles as a viable option for feeders to public transport systems.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport and PlanningHuman-Robot InteractionIntelligent VehiclesTransport and Plannin

Cyclist support systems for future automated traffic: A review

Interpreting the subtleness and complexity of vulnerable road user (VRU) behaviour is still a significant challenge for automated vehicles (AVs). Solutions for facilitating safe and acceptable interactions in future automated traffic include equipping AVs and VRUs with human-machine interfaces (HMl.s), such as awareness and notification systems, and connecting road users to a network of A Vs and infrastructure. The research on these solutions, however, primarily focuses on pedestrians. There is no overview ofthe type of systems or solutions supporting cyclists in future automated traffic. The objective ofthe present study is to synthesise current literature and provide an overview ofthe state-ofthe-art support systems available to cyclists. The aim is to identify, classify, and count the types of communicative technologies, systems, and devices capable of supporting the safety of cyclists in automated traffic. The overall goal is to understand A V-cyclist interaction better, pinpoint knowledge gaps in current literature, and develop strategies for optimising safe and pleasant cycling in future traffic environments with AVs

Effects of Concurrent Continuous Visual Feedback on Learning the Lane Keeping Task

This study investigated the training effectiveness of continuous visual feedback in a simulator-based lane keeping task. Two groups of student drivers (total of 30 participants) were instructed to drive as accurately as possible in the center of the right lane in a self-paced driving task during five 8-min sessions. One group received visual feedback using a horizontal compensatory display positioned on the dashboard, which provided an indication of the momentary distance to the lane center during the three training sessions. During two retention sessions (immediate and one day delayed) both groups drove without the augmented feedback. The augmented feedback resulted in improved performance on a measure lane keeping accuracy, but this effect disappeared during retention. Furthermore, the augmented feedback resulted in increased steering wheel activity during all sessions, and increased driver workload in the delayed retention session. These results provide support for the guidance hypothesis and have possible implications for the use of continuous concurrent feedback in simulatorbased driver training

Driving examiners’ views on data-driven assessment of test candidates:An interview study

Vehicles are increasingly equipped with sensors that capture the state of the driver, the vehicle, and the environment. These developments are relevant to formal driver testing, but little is known about the extent to which driving examiners would support the use of sensor data in their job. This semi-structured interview study examined the opinions of 37 driving examiners about datadriven assessment of test candidates. The results showed that the examiners were supportive of using data to explain their pass/fail verdict to the candidate. According to the examiners, data in an easily accessible form such as graphs of eye movements, headway, speed, or braking behaviour, and colour-coded scores, supplemented with camera images, would allow them to eliminate doubt or help them convince disagreeing test-takers. The examiners were sceptical about higher levels of decision support, noting that forming an overall picture of the candidate’s abilities requires integrating multiple context-dependent sources of information. The interviews yielded other possible applications of data collection and sharing, such as selecting optimal routes, improving standardization, and training and pre-selecting candidates before they are allowed to take the driving test. Finally, the interviews focused on an increasingly viable form of data collection: simulator-based driver testing. This yielded a divided picture, with about half of the examiners being positive and half negative about using simulators in driver testing. In conclusion, this study has provided important insights regarding the use of data as an explanation aid for examiners. Future research should consider the views of test candidates and experimentally evaluate different forms of data-driven support in the driving test

Using Eye-tracking Data to Predict Situation Awareness in Real Time during Takeover Transitions in Conditionally Automated Driving

Situation awareness (SA) is critical to improving takeover performance during the transition period from automated driving to manual driving. Although many studies measured SA during or after the driving task, few studies have attempted to predict SA in real time in automated driving. In this work, we propose to predict SA during the takeover transition period in conditionally automated driving using eye-tracking and self-reported data. First, a tree ensemble machine learning model, named LightGBM (Light Gradient Boosting Machine), was used to predict SA. Second, in order to understand what factors influenced SA and how, SHAP (SHapley Additive exPlanations) values of individual predictor variables in the LightGBM model were calculated. These SHAP values explained the prediction model by identifying the most important factors and their effects on SA, which further improved the model performance of LightGBM through feature selection. We standardized SA between 0 and 1 by aggregating three performance measures (i.e., placement, distance, and speed estimation of vehicles with regard to the ego-vehicle) of SA in recreating simulated driving scenarios, after 33 participants viewed 32 videos with six lengths between 1 and 20 s. Using only eye-tracking data, our proposed model outperformed other selected machine learning models, having a root-mean-squared error (RMSE) of 0.121, a mean absolute error (MAE) of 0.096, and a 0.719 correlation coefficient between the predicted SA and the ground truth. The code is available at https://github.com/refengchou/Situation-awareness-prediction. Our proposed model provided important implications on how to monitor and predict SA in real time in automated driving using eye-tracking data.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167003/1/hkwggmgngbsqcmmqkbffywbrjtcmhhxx.pdfDescription of hkwggmgngbsqcmmqkbffywbrjtcmhhxx.pdf : Mian articleSEL

Objective classification of residents based on their psychomotor laparoscopic skills

Background - From the clinical point of view, it is important to recognize residents’ level of expertise with regard to basic psychomotor skills. For that reason, surgeons and surgical organizations (e.g., Acreditation Council for Graduate Medical Education, ACGME) are calling for assessment tools that credential residents as technically competent. Currently, no method is universally accepted or recommended for classifying residents as ‘‘experienced,’’ ‘‘intermediates,’’ or ‘‘novices’’ according to their technical abilities. This study introduces a classification method for recognizing residents’ level of experience in laparoscopic surgery based on psychomotor laparoscopic skills alone. Methods - For this study, 10 experienced residents (>100 laparoscopic procedures performed), 10 intermediates (10– 100 procedures performed), and 11 novices (no experience) performed four tasks in a box trainer. The movements of the laparoscopic instruments were recorded with the TrEndo tracking system and analyzed using six motion analysis parameters (MAPs). The MAPs of all participants were submitted to principal component analysis (PCA), a data reduction technique. The scores of the first principal components were used to perform linear discriminant analysis (LDA), a classification method. Performance of the LDA was examined using a leave-one-out crossvalidation. Results - Of 31 participants, 23 were classified correctly with the proposed method, with 7 categorized as experienced, 7 as intermediates, and 9 as novices. Conclusions - The proposed method provides a means to classify residents objectively as experienced, intermediate, or novice surgeons according to their basic laparoscopic skills. Due to the simplicity and generalizability of the introduced classification method, it is easy to implement in existing trainers.Biomechanical EngineeringMechanical, Maritime and Materials Engineerin

How to keep drivers engaged while supervising driving automation? A literature survey and categorization of six solution areas

This work aimed to organise recommendations for keeping people engaged during human supervision of driving automation, encouraging a safe and acceptable introduction of automated driving systems. First, heuristic knowledge of human factors, ergonomics, and psychological theory was used to propose solution areas to human supervisory control problems of sustained attention. Driving and non-driving research examples were drawn to substantiate the solution areas. Automotive manufacturers might (1) avoid this supervisory role altogether, (2) reduce it in objective ways or (3) alter its subjective experiences, (4) utilize conditioning learning principles such as with gamification and/or selection/training techniques, (5) support internal driver cognitive processes and mental models and/or (6) leverage externally situated information regarding relations between the driver, the driving task, and the driving environment. Second, a cross-domain literature survey of influential human-automation interaction research was conducted for how to keep engagement/attention in supervisory control. The solution areas (via numeric theme codes) were found to be reliably applied from independent rater categorisations of research recommendations. Areas (5) and (6) were addressed by around 70% or more of the studies, areas (2) and (4) in around 50% of the studies, and areas (3) and (1) in less than around 20% and 5%, respectively. The present contribution offers a guiding organisational framework towards improving human attention while supervising driving automation.submittedVersio

Cerebellar plasticity and associative memories are controlled by perineuronal nets.

Perineuronal nets (PNNs) are assemblies of extracellular matrix molecules, which surround the cell body and dendrites of many types of neuron and regulate neural plasticity. PNNs are prominently expressed around neurons of the deep cerebellar nuclei (DCN), but their role in adult cerebellar plasticity and behavior is far from clear. Here we show that PNNs in the mouse DCN are diminished during eyeblink conditioning (EBC), a form of associative motor learning that depends on DCN plasticity. When memories are fully acquired, PNNs are restored. Enzymatic digestion of PNNs in the DCN improves EBC learning, but intact PNNs are necessary for memory retention. At the structural level, PNN removal induces significant synaptic rearrangements in vivo, resulting in increased inhibition of DCN baseline activity in awake behaving mice. Together, these results demonstrate that PNNs are critical players in the regulation of cerebellar circuitry and function