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OBJECTIVE: Drivers using level 2 automation are able to disengage with the dynamic driving task, but must still monitor the roadway and environment and be ready to takeover on short notice. However, people are still willing to engage with non-driving related tasks, and the ways in which people manage this tradeoff are expected to vary depending on the operational design domain of the system and the nature of the task. Our aim is to model driver gaze behavior in level 2 partial driving automation when the driver is engaged in an email task on a cell phone. Both congested highway driving, traffic jams, and a hazard with a silent automation failure are considered in a driving simulator study conducted in the NADS-1 high-fidelity motion-based driving simulator. METHODS: Sequence analysis is a methodology that has grown up around social science research questions. It has developed into a powerful tool that supports intuitive visualizations, clustering analysis, covariate analyses, and Hidden Markov Models. These methods were used to create models for four different gaze behaviors and use the models to predict attention during the silent failure event. RESULTS: Predictive simulations were run with initial conditions that matched driver state just prior to the silent failure event. Actual gaze response times were observed to fall within distributions of predicted glances to the front. The three drivers with the largest glance response times were not able to take back manual control before colliding with the hazard. CONCLUSIONS: The simulated glance response time distributions can be used in more sophisticated ways when combined with other data. The glance response time probability may be conditioned on other variables like time on task, time of day, prevalence of the current behavior for this driver, or other variables. Given the flexibility of sequence analysis and the methods it supports (clustering, HMMs), future studies may benefit from its application to gaze behavior and driving performance data.

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OBJECTIVE: Regulations are currently being drafted by the European Commission for the safe introduction of automated driving systems (ADSs) with conditional or higher automation (SAE level 3 and above). One of the main challenges for complying with the drafted regulations is proving that the residual risk of an ADS is lower than the existing state of the art without the ADS and that the current safety state of European roads is not compromised. Therefore, much research has been conducted to estimate the safety risk of ADS. One proposed method for estimating the risk is data-driven, scenario-based assessment, where tests are partially automatically generated based on recorded traffic data. Although this is a promising method, uncertainties in the estimated risk arise from, among others, the limited number of tests that are conducted and the limited data that have been used to generate the tests. This work addresses the following question: "Given the limitations of the data and the number of tests, what is the uncertainty of the estimated safety risk of the ADS?" METHODS: To compute the safety risk, parameterized test scenarios are based on large-scale collections of road scenarios that are stored in a scenario database. The exposure of the scenarios and the parameter distributions are estimated using the data as well as confidence bounds of these estimates. Next, virtual simulations are conducted of the scenarios for a variety of parameter values. Using a probabilistic framework, all results are combined to estimate the residual risk as well as the uncertainty of this estimation. RESULTS: The results are used to provide confidence bounds on the calculated fatality rate in case an ADS is implemented in the vehicle. For example, using the proposed probabilistic framework, it is possible to claim with 95% certainty that the fatality rate is less than 10-7 fatalities per hour of driving. The proposed method is illustrated with a case study in which the risk and its uncertainty are quantified for a longitudinal controller in 3 different types of scenarios. The case study code is publicly available. CONCLUSIONS: If results show that the uncertainty is too high, the proposed method allows answering questions like "How much more data do we need?" or "How many more (virtual) simulations must be conducted?" Therefore, the method can be used to set requirements on the amount of data and the number of (virtual) simulations. For a reliable risk estimate, though, much more data are needed than those used in the case study. Furthermore, because the method relies on (virtual) simulations, the reliability of the result depends on the validity of the models used in the simulations. The presented case study illustrates that the proposed method is able to quantify the uncertainty of the estimated safety risk of an ADS. Future work involves incorporating the proposed method into the type approval framework for future ADSs of SAE levels 3, 4, and 5, as proposed in the upcoming European Union implementing regulation for ADS.

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Autonomous driving of higher automation levels asks for optimal execution of critical maneuvers in all environments. A crucial prerequisite for such optimal decision-making instances is accurate situation awareness of automated and connected vehicles. For this, vehicles rely on the sensory data captured from onboard sensors and information collected through V2X communication. The classical onboard sensors exhibit different capabilities and hence a heterogeneous set of sensors is required to create better situation awareness. Fusion of the sensory data from such a set of heterogeneous sensors poses critical challenges when it comes to creating an accurate environment context for effective decision-making in AVs. Hence this exclusive survey analyses the influence of mandatory factors like data pre-processing preferably data fusion along with situation awareness toward effective decision-making in the AVs. A wide range of recent and related articles are analyzed from various perceptive, to pick the major hiccups, which can be further addressed to focus on the goals of higher automation levels. A section of the solution sketch is provided that directs the readers to the potential research directions for achieving accurate contextual awareness. To the best of our knowledge, this survey is uniquely positioned for its scope, taxonomy, and future directions.

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This study investigates interactive behaviors and communication cues of heavy goods vehicles (HGVs) and vulnerable road users (VRUs) such as pedestrians and cyclists as a means of informing the interactive capabilities of highly automated HGVs. Following a general framing of road traffic interaction, we conducted a systematic literature review of empirical HGV-VRU studies found through the databases Scopus, ScienceDirect and TRID. We extracted reports of interactive road user behaviors and communication cues from 19 eligible studies and categorized these into two groups: 1) the associated communication channel/mechanism (e.g., nonverbal behavior), and 2) the type of communication cue (implicit/explicit). We found the following interactive behaviors and communication cues: 1) vehicle-centric (e.g., HGV as a larger vehicle, adapting trajectory, position relative to the VRU, timing of acceleration to pass the VRU, displaying information via human-machine interface), 2) driver-centric (e.g., professional driver, present inside/outside the cabin, eye-gaze behavior), and 3) VRU-centric (e.g., racer cyclist, adapting trajectory, position relative to the HGV, proximity to other VRUs, eye-gaze behavior). These cues are predominantly based on road user trajectories and movements (i.e., kinesics/proxemics nonverbal behavior) forming implicit communication, which indicates that this is the primary mechanism for HGV-VRU interactions. However, there are also reports of more explicit cues such as cyclists waving to say thanks, the use of turning indicators, or new types of external human-machine interfaces (eHMI). Compared to corresponding scenarios with light vehicles, HGV-VRU interaction patterns are to a high extent formed by the HGV's size, shape and weight. For example, this can cause VRUs to feel less safe, drivers to seek to avoid unnecessary decelerations and accelerations, or lead to strategic behaviors due to larger blind-spots. Based on these findings, it is likely that road user trajectories and kinematic behaviors will form the basis for communication also for highly automated HGV-VRU interaction. However, it might also be beneficial to use additional eHMI to compensate for the loss of more social driver-centric cues or to signal other types of information. While controlled experiments can be used to gather such initial insights, deeper understanding of highly automated HGV-VRU interactions will also require naturalistic studies.

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The present study aimed to investigate the upper trapezius muscle activity during simulated car driving while adopting three different arm positions. Ten participants were instructed to maintain the following positions: hands on the steering wheel (Hands-On), hands not on the steering wheel (Hands-Off) and hands not on the steering wheel but arms on armrests (Armrests). During the tasks, multi-channel surface electromyography (EMG) was recorded from the upper trapezius muscle with 64 two-dimensionally distributed electrodes. Amplitudes of surface EMG in Armrests were lower than in Hands-On (p = 0.004). The spatial distribution of surface EMG changed with time in Hands-Off and Armrests (p < 0.05), but not in Hands-On (p > 0.05). These findings suggest that being freed from steering leads to the recruitment of various muscle fibers/motor units within the upper trapezius muscle and the use of armrests may help reduce the physiological burden loaded on the muscle of drivers.

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We are entering an era of automated vehicles (AVs), which has potential to improve road safety considerably. A compelling user experience is crucial to AV adoption in the future commercial market. The automated driving system (ADS) that replaces human drivers should be perceived as very useful before the latter are willing to give up their control and entrust their lives to the ADS. However, compared with the growing number of studies on public acceptance of AVs, there has been limited research focusing on user experience and usability. We examined AV and ADS user experience and usability, ADS failures' influence on them, and their influences on re-riding willingness. We conducted a field study using a real AV and a large-scale test track. We invited participants (N = 261) to travel in the AV as passengers in a low-speed environment. Participants were randomly assigned into the normal condition or the fault condition (its participants were exposed to an ADS failure). We measured participants' positive experience (feeling relaxed, safe, and comfortable) and negative experience (feeling tense and risky) while riding in the AV and perceived usability of the ADS based on the System Usability Scale. In both conditions, participants reported moderate positive experience and perceived usability but a relatively high level of willingness to ride in our AV again. The ADS failure reduced positive experience and perceived usability, and it increased negative experience. Positive experience and perceived usability, but not negative experience, influenced re-riding willingness. Compared with male participants, female participants reported less positive experience and lower perceived usability. We discuss implications of our results as well as limitations of this research.

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Data-based research approaches to generate crash scenarios have mainly relied on conventional vehicle crashes and naturalistic driving data, and have not considered differences between the autonomous vehicle (AV) and conventional vehicle crashes. As the AV's presence on roadways continues to grow, its crash scenarios take on new importance for traffic safety. This study therefore obtained crash patterns using the United States Department of Transportation pre-crash scenario typology, and used statistical analysis to determine the differences between AV and conventional vehicle pre-crash scenarios. Analysis of 122 AV crashes and 2084 conventional vehicle crashes revealed 15 types of scenario for AVs and 26 for conventional vehicles. The two groups showed differences in type of scenario, and differed in the proportion of crashes when the scenario was the same. The most frequent AV pre-crash scenarios were rear-end collisions (52.46%) and lane change collisions (18.85%), with the proportion of AVs rear-ended by conventional vehicles occurring with a frequency 1.6 times that of conventional vehicles. An in-depth crash investigation was conducted of the characteristics and causes of four AV pre-crash scenarios, summarized from the perspectives of perception and path planning. The perception-reaction time (PRT) difference between AVs and human drivers, AV's inaccurate identification of the intention of other vehicles to change lanes, and AV's insufficient path planning combining time and space dimensions were found to be important causes for the AV crashes. By increasing understanding of the complex characteristics of AV pre-crash scenarios, this analysis will encourage cooperation with vehicle manufacturers and AV technology companies for further study of crash causation toward the goals of improved test scenario construction and optimization of the AV's automated driving system (ADS).

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Lane detection and tracking in a complex road environment is one of the most important research areas in highly automated driving systems. Studies on lane detection cover a variety of difficulties, such as shadowy situations, dimmed lane painting, and obstacles that prohibit lane feature detection. There are several hard cases in which lane candidate features are not easily extracted from image frames captured by a driving vehicle. We have carefully selected typical scenarios in which the extraction of lane candidate features can be easily corrupted by road vehicles and road markers that lead to degradations in the understanding of road scenes, resulting in difficult decision making. We have introduced two main contributions to the interpretation of road scenes in dense traffic environments. First, to obtain robust road scene understanding, we have designed a novel framework combining a lane tracker method integrated with a camera and a radar forward vehicle tracker system, which is especially useful in dense traffic situations. We have introduced an image template occupancy matching method with the integrated vehicle tracker that makes it possible to avoid extracting irrelevant lane features caused by forward target vehicles and road markers. Second, we present a robust multi-lane detection by a tracking algorithm that incudes adjacent lanes as well as ego lanes. We verify a comprehensive experimental evaluation with a real dataset comprised of problematic road scenarios. Experimental result shows that the proposed method is very reliable for multi-lane detection at the presented difficult situations.

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OBJECTIVE: The aim of this special issue is to bring together the latest research related to driver interaction with various types of vehicle automation. BACKGROUND: Vehicle technology has undergone significant progress over the past decade, bringing new support features that can assist the driver and take on more and more of the driving responsibilities. METHOD: This issue is comprised of eight articles from international research teams, focusing on different types of automation and different user populations, including driver support features through to highly automated driving systems. RESULTS: The papers comprising this special issue are clustered into three categories: (a) experimental studies of driver interactions with advanced vehicle technologies; (b) analysis of existing data sources; and (c) emerging human factors issues. Studies of currently available and pending systems highlight some of the human factors challenges associated with the driver-system interaction that are likely to become more prominent in the near future. Moreover, studies of more nascent concepts (i.e., those that are still a long way from production vehicles) underscore many attitudes, perceptions, and concerns that will need to be considered as these technologies progress. CONCLUSIONS: Collectively, the papers comprising this special issue help fill some gaps in our knowledge. More importantly, they continue to help us identify and articulate some of the important and potential human factors barriers, design considerations, and research needs as these technologies become more ubiquitous.

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