Conference Paper

The role of perceptual failure and degrading processes in urban traffic accidents: a stochastic computational model for virtual experiments

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... actions based on cognitive processing (see [21]). These include the processes of perception, memory, decision making, and action implementation. ...
... The problem with this model type is that the opening angle and gaze behavior (where the visual funnel is directed) are difficult to parameterize. To parametrize these top-down and bottomup processes, Denk et al. [21] and Horrey et al. [168] suggest using the SEEV model (salience, effort, expectancy, value). The idea behind the SEEV model is to predict the probability of attention (eye fixation) based on the perceived parameters of saliency, effort, expectancy, and value [98]. ...
... Wickens and McCarley [98] theorize that attention can be modelled by the perceived parameters of saliency, effort, expectancy, and value of the fixation target. Denk et al. [21] and Horrey et al. [168] suggest the usage of the SEEV model [98] of Wickens and McCarley for the modelling of bottom-up and top-down perception processes in driver models. Next to this, Wang et al. [116] developed an approach to simulate the visual perception process based on the so called FLMP [193]. ...
Article
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Will Advanced Driving Assistance Systems (ADAS) and Highly Automated Driving (HAD) perform in the expected manner? Will they actually make road traffic safer, or will they potentially introduce new critical situations or road accidents? It is almost impossible to address these questions solely through real-world tests. A promising tool to provide appropriate answers in a time-and cost-efficient way without exposing subjects to risk are virtual assessment methods. Reliable safety assessments are only possible, if the traffic simulations provide realistic traffic, including critical situations and road accidents. This paper provides a review of how human error contributes to critical situations and accidents in road traffic. The focus is on the causes and mechanisms of human error, which driver behavior models must address in order to simulate realistic traffic. For this purpose, Rasmussen’s error taxonomies are applied to the traffic context and extended with further research. The paper shows the causes of those human errors and that the underlying mechanisms thereof should be taken into account in order to obtain more transparent and realistic driver behavior models. It is shown, which concepts for modelling realistic traffic exist and how virtual safety assessment could benefit from this development. In addition, the driver behavior model DReaM (Driver Reaction Model) is presented to address the issues resulting from existing cognitive driver models.
... IV. STOCHASTIC MODELS OF SUB-PROCESSES As described in [11], traffic flow, viewed as a complex process, exhibits high reliability in spite of relatively frequent partial failures. This failure tolerance can be attributed to multiple redundancies. ...
... Trajectory combinations sampled with the hot deck paradigm in which the cyclist has once been in front of the car are regarded as conflict-free since it is assumed that the driver likely has perceived the cyclist in this case. For modeling the reliability of drivers to perform a shoulder glance, a Markov State Model as proposed in [11] was employed. Drivers are assumed to initially start in a state where they observe the roadway in front of them. ...
... Considering a constant likelihood to perform a shoulder glance within this time frame results in the exponential survival function of not performing a shoulder glance depicted in Fig. 5. In each simulation run, random survival times are generated using the procedure for sampling from a Bernoulli process as summarized in [11]. ...
Conference Paper
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Safely traveling through urban traffic, for exampleperforming a right turn at an intersection, is a complextask for human drivers. Multiple stimuli such as leadingvehicles, cyclists or spontaneously crossing pedestrians competefor attention. Limited field of view and sampling capabilitiesof humans make a parsimonious and sometime error-proneselection of most relevant information necessary, thus contribut-ing to the occurrence of collisions. Especially with regard tovulnerable road users, such incidents still pose a high risk tolife and health. A potential solution to compensate for humanlimitations are intelligent driver assistance functions up toconnected and automated driving, which enable a continuoussampling of the surrounding environment. However, given thecharacteristics of current urban infrastructure, these systemswill be confronted with frequent physical obstructions, e.g. dueto construction, delivery vehicles, parked cars, vegetation orbus stops. These sensor occlusions likely lead to ambiguoussituations for decision algorithms because of an only partiallyobservable traffic environment. In this regard, external sensorsdelivering information from a different point of view viaV2X could support the system’s decision-making confidence.Since the implementation of such technology can consumeconsiderable resources, especially if installed at every urbanintersection, an estimation of the specific benefit (given thecharacteristics of intersections) is reasonable to support decisionmakers. The present article demonstrates a methodologicalapproach to quantitative assessment of safety efficacy of newtechnologies by the technique of virtual randomized controlledtrials. Thereby, the main focus in the scope of this documentis to discuss requirements and the general process of thistechnique. It will be discussed how the statistical power ismaximized and which sub-processes need to be modeled inorder to come up with valid predictions. First simulation resultswill be presented and discussed with respect to their validitybased on the implemented sub-process models.
... Cognitive models (Newell, 1994;Laird, 2012;Anderson et al., 1998;Liu et al., 2006;Tattegrain-Veste et al., 1996;Krajzewicz, 2005;Cacciabue et Cognitive models (Newell, 1994;Laird, 2012;Anderson et al., 1998;Liu et al., 2006;Tattegrain-Veste et al., 1996;Krajzewicz, 2005;Cacciabue et al., 2010b;Witt et al., 2019) Input information processing Sensor models (Witt et al., 2019;Wang et al., 2020a;Bellet et al., 2018;Massaro, 1979); Top-down/bottomup simulation (Denk et al., 2020;Horrey et al., 2006;Wickens et al., 2008) Recall Limited storage capacity; Selection of actions among alternatives Inference Cognitive models (Newell, 1994;Laird, 2012;Anderson et al., 1998;Liu et al., 2006;Tattegrain-Veste et al., 1996;Krajzewicz, 2005;Cacciabue et al., 2010b;Witt et al., 2019) Physical coordination Noise models ...
... To expand the application of the gaze model to additional scenarios, it is necessary to ensure that all gaze fixations originate from cognitive processes, without the need for pre-conducted gaze studies. One solution could be using the SEEV model, according to Wickens et al. (2008) and suggested by Denk et al. (2020) and Horrey et al. (2006). This way, the gaze fixation is calculated based on the perceived parameters of saliency, effort, expectancy, and value (SEEV) of the potential target. ...
Thesis
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In recent years, the automotive sector has seen a steady increase in the introduction of new Advanced Driving Assistance Systems (ADAS). This trend toward more complex systems will become even more pronounced with regard to Highly Automated Driving (HAD). In addition to the expected benefits of ADAS and HAD (increased comfort, efficiency, and safety), it is important to eliminate risks as much as possible to ensure that the system does not introduce new critical situations or road traffic accidents. Due to the increasing interaction of systems with the driver and their environment, it is no longer sufficient to investigate the system in isolation. There is also a need to investigate how the driver and the environment interact with the new system. Furthermore, the functional scope of the systems is expanding to cover entire application domains, such as highways and in the future rural and urban areas. This results in a significant increase in the number of parameters and scenarios that require testing for approval of these new technologies. This means that the scenario space to be analyzed is constantly expanding, which poses increasing problems for safety assessments. The expected number of test kilometers required to validate HAD is too large to be cost- and time-effective through real-world testing. This is why virtual safety assessments are necessary. In this context, the present thesis investigates whether virtual safety assessments can be efficiently performed today through Monte Carlo simulations using cognitive driver behavior models. The body of the thesis consists of four articles that consider different aspects of the safety assessment. Article 1 derives the cognitive core functions that driver behavior models must implement to display the causes and mechanisms of human error. This way, driver behavior models are able to map all hazard levels of realistic traffic, including normal traffic, critical situations, and road traffic accidents. By mapping the interactions of road users, cognitive models thus form the basis for the virtual safety assessment of ADAS and HAD systems. Due to the lack of existing cognitive driver behavior models that implement these cognitive core functions, the Driver Reaction Model (DReaM), a new driver behavior model, was developed and continuously improved as part of this work. Article 2 outlines a calibration and validation strategy, using DReaM as an example, to investigate whether driver behavior models are suitable for safety assessments, mapping all levels of realistic traffic. Subsequently, Article 3 estimates the time required to perform Monte Carlo studies for safety assessments, again using DReaM as an example. Therefore, an optimistic and pessimistic estimation is generated based on the minimum number of runs (MNR) required to simulate an exemplary traffic scenario. In summary, Articles 1–3 examine the quality of driver behavior models and the time required to perform safety-related studies. This lays the foundation for determining whether efficient safety assessments are feasible. Finally, Article 4 exemplarily assesses an urban Automatic Emergency Braking (AEB) system using DReaM to outline the overall virtual assessment methodology. Based on Article 4 and the findings of Article 1–3, minimal requirements are defined for improving and standardizing the virtual safety assessment process. These requirements aim to improve the reliability of safety assessments and enhance the comparability of results across various studies and models
... As indicated in [9], failures in the interactions between motorists and VRUs may occur at several stages within the human information processing chain: Errors might occur in the detectability of the VRU, the correct detection of VRU, the correct prediction of the behavior of the VRU, and within acting out formed decisions. Requirements for specific research designs depend heavily on the process under investigation. ...
Conference Paper
Crashes involving vulnerable road users (VRUs) account for nearly 70% of all traffic fatalities in urban areas. To evaluate the effectiveness of measures to improve VRU safety, the causes of accidents attributable to human behavior require investigation, which remains a challenge to date. Experiments for this purpose are often conducted in a controlled environment, such as on test tracks or in driving simulators. In this work, we compared gaze behavior and subjective realism between a test track and two different driving simulators, one using a head-mounted display (HMD-Sim) and one using an LED wall and a full vehicle mockup (LED-Sim). Both on the test track and in the simulator a right turn maneuver was studied in which a motorist turns right and a cyclist goes straight ahead through an intersection. The relevant areas of interest (gaze data) and subjective questionnaires were matched. Results show that shoulder glances were observed significantly more often in the test track experiment, whereas mirror glances occurred more often in the simulators. No difference was observed with respect to the perceived responsibility for safeguarding behavior across the different environments. The simulator study was subjectively perceived more similar to real traffic than the test track based on questionnaire data. The findings presented may facilitate the selection of an appropriate test environment for particular research questions. However, given the differences in the two study designs, further research is needed to make more informed conclusions about the applicability of test track and simulator experiments.
... As data in Hurwitz [11] and Kolrep [4] show, the most dangerous situations occur when cyclists are located behind a motorist. Following the categorization of failure mechanisms in [12], this suggests that failures in the right-turning situation are primarily perceptual in origin. Therefore, we will focus on studying perceptual failure processes as a subset of cognitive failures in general. ...
Conference Paper
Full-text available
Background: Driving in urban traffic requires ad-vanced cognitive skills: perceiving all relevant traffic participants,anticipating their likely trajectories, deciding which action totake, and controlling the vehicle. The underlying perceptual andcognitive processes are subject to occasional failures, which candepend in a complex way on learned heuristics and the cognitiveload. Collisions between motor vehicles and vulnerable roadusers (VRU) in urban traffic remain frequent and have severeconsequences. In this article, we study the behavior of drivers ofmotor vehicles turning right who are required to yield to cyclistsriding straight through an intersection. A key potential errorprocess is failure to perceive the cyclist.Methods: We conducted a trial with n = 35 subjects on ourclosed test track including observations of perceptual actions andgaze control, subject to variations in cognitive load and otherfactors. The artificial environment of a closed test track and theconstraints due to ethical requirements pose challenges to theinterpretation of any empirical trial. The current paper focuseson the trial design and on quantification of measurement validity.Results: Summary statistics involving trial features were assessed.Most participants reported that they performed the visual taskof checking for cyclists in a manner similar to their behavior inreal traffic (whether or not cyclist interactions were expected).The spatial distributions of driver glances to perceive cyclistswere evaluated.Conclusion: The realism in this trial despite laboratory conditionsmay be attributable to ingrained skills and habits of participants.Laboratory trials can help to identify root causes of cognitiveerrors and ultimately guide efficient and effective deployment ofbicycle safety countermeasures.
... They are applied in a wide range of applications in transportation planning and traffic engineering, in particular in testing new vehicle systems for automated cars [2]. A key issue related to automated driving is the communication between automated vehicles (AVs) and vulnerable road users (VRUs), because, as reviewed in [3], VRU accidents are often associated with possible occlusion, unexpected trajectories or difficulties of the VRU in threat perception. Communication as an enhancement to human perception in road traffic offers a possibility to improve safety in the VRU-to-AV interaction. ...
... As Lint and Calvert (2018) described, incorporating the perceptual and cognitive processes of humans into driver models is beneficial in terms of 1) more accurate prediction of traffic flow problems, 2) modeling the interaction and possible crashes of human-operated vehicles with automated vehicles, and 3) the prediction of accident metrics and surrogate safety measures. Therefore, adequate modeling of the visual perception process, as the most important information source for human drivers, is fundamental for the quality of new and advanced driving behavior models (Denk, Huber, Brunner, & Kates, 2020;Witt, Ring, Wang, Kompaß, & Prokop, 2018). For stochastic distribution of driver gaze, these models should distinguish between different intersection scenarios of having and giving right of way and should consider the influences of variations in traffic volume, driving maneuver, and driver state. ...
Article
Urban intersections are hotspots for crashes because they provide a location for several traffic streams and types of road users to cross. A main cause of crashes is the misinformation of drivers as they fail to sense relevant visual information. We aimed to analyze the gaze behavior of car drivers in a variety of intersection scenarios, bringing together the partial findings of previous research, and examine the interdependencies of the contributing factors to provide a database for driver modeling. In a driving simulator study with 59 participants, we varied intersection scenarios regarding drivers’ right of way (yield sign, green traffic light), intersection type (T junction, X intersection), surrounding traffic (none, irrelevant, relevant), and intended driving maneuver (left turn, right turn, going straight). A total of 25 intersection scenarios were presented in a within-subjects design to a control group and a group with a cognitive load task (counting back in numbers of two). Fixations were coded regarding defined areas of interest in the field of view and separated according to three segments of the intersection approach: 75–50 m, 50–25 m, and 25–0 m before entering the intersection. The results show that the effect of surrounding traffic, secondary task engagement, and the intended driving maneuver changed dramatically depending on the right of way of the driver. Surrounding traffic primarily affected gaze behavior in scenarios of ceding the right of way close to the intersection entry. The cognitive load task increased fixations on the road center especially in situations where the driver had the right of way, but less in situations of ceding the right of way. Interactions with the type of intersection were only apparent for different driving maneuvers. This study provides a detailed and comprehensive picture of drivers’ attentional processes when approaching intersections which is relevant for understanding and modeling of driver behavior in urban traffic.
... They are applied in a wide range of applications in transportation planning and traffic engineering, in particular in testing new vehicle systems for automated cars [2]. A key issue related to automated driving is the communication between automated vehicles (AVs) and vulnerable road users (VRUs), because, as reviewed in [3], VRU accidents are often associated with possible occlusion, unexpected trajectories or difficulties of the VRU in threat perception. Communication as an enhancement to human perception in road traffic offers a possibility to improve safety in the VRU-to-AV interaction. ...
Preprint
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In order to investigate the interaction between automated vehicles (AVs) and bicyclists, we present a coupled driving simulator that enables these two traffic participants to interact in a virtual environment. To avoid potentially dangerous situations in road traffic, human perception can be extended by communication between vehicles and their environment. In order to assist the communication process between traffic participants, mobile devices are applied as human-machine interfaces (HMIs). The simulator links the simulation and visualization software with a web application to control the HMIs. The passenger of the AV can change priority rules at conflict situation in the simulation with that application and therefore influence the vehicles behavior via the communication application. To test the coupled simulator, a proof of concept study with 16 simulation runs and two participants each is conducted. The subjects rated the overall simulation impressions tending positive. Based on the evaluation of the study participants, the simulator setup will be further developed.
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This greatly expanded second edition of Survival Analysis- A Self-learning Text provides a highly readable description of state-of-the-art methods of analysis of survival/event-history data. This text is suitable for researchers and statisticians working in the medical and other life sciences as well as statisticians in academia who teach introductory and second-level courses on survival analysis. The second edition continues to use the unique "lecture-book" format of the first (1996) edition with the addition of three new chapters on advanced topics: Chapter 7: Parametric Models Chapter 8: Recurrent events Chapter 9: Competing Risks. Also, the Computer Appendix has been revised to provide step-by-step instructions for using the computer packages STATA (Version 7.0), SAS (Version 8.2), and SPSS (version 11.5) to carry out the procedures presented in the main text. The original six chapters have been modified slightly to expand and clarify aspects of survival analysis in response to suggestions by students, colleagues and reviewers, and to add theoretical background, particularly regarding the formulation of the (partial) likelihood functions for proportional hazards, stratified, and extended Cox regression models David Kleinbaum is Professor of Epidemiology at the Rollins School of Public Health at Emory University, Atlanta, Georgia. Dr. Kleinbaum is internationally known for innovative textbooks and teaching on epidemiological methods, multiple linear regression, logistic regression, and survival analysis. He has provided extensive worldwide short-course training in over 150 short courses on statistical and epidemiological methods. He is also the author of ActivEpi (2002), an interactive computer-based instructional text on fundamentals of epidemiology, which has been used in a variety of educational environments including distance learning. Mitchel Klein is Research Assistant Professor with a joint appointment in the Department of Environmental and Occupational Health (EOH) and the Department of Epidemiology, also at the Rollins School of Public Health at Emory University. Dr. Klein is also co-author with Dr. Kleinbaum of the second edition of Logistic Regression- A Self-Learning Text (2002). He has regularly taught epidemiologic methods courses at Emory to graduate students in public health and in clinical medicine. He is responsible for the epidemiologic methods training of physicians enrolled in Emory’s Master of Science in Clinical Research Program, and has collaborated with Dr. Kleinbaum both nationally and internationally in teaching several short courses on various topics in epidemiologic methods.
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