The influence of age on the take-over of vehicle control in highly automated driving

The growing proportion of older drivers in the population plays an increasingly relevant role in road traffic that is currently awaiting the introduction of automated vehicles. In this study, it was investigated how older drivers (⩾60 years) compared to younger drivers (⩽28 years) perform in a critical traffic event when driving highly automated. Conditions of the take-over situation were manipulated by adding a verbal non-driving task (20 questions task) and by variation of traffic density. Two age groups consisting of 36 younger and 36 older drivers drove either with or without a non-driving task on a six-lane highway. They encountered three situations with either no, medium or high traffic density where they had to regain vehicle control and evade an obstacle on the road. Older drivers reacted as fast as younger drivers, however, they differed in their modus operandi as they braked more often and more strongly and maintained a higher time-to-collision (TTC). Deterioration of take-over time and quality caused by increased traffic density and engagement in a non-driving task was on the same level for both age groups. Independent of the traffic density, there was a learning effect for both younger and older drivers in a way that the take-over time decreased, minimum TTC increased and maximum lateral acceleration decreased between the first and the last situation of the experiment. Results highlight that older drivers are able to solve critical traffic events as well as younger drivers, yet their modus operandi differs. Nevertheless, both age groups adapt to the experience of take-over situations in the same way.     

Secondary task engagement and vehicle automation – Comparing the effects of different automation levels in an on-road experiment

Recent and upcoming advances in vehicle automation are likely to change the role of the driver from one of actively controlling a vehicle to one of monitoring the behaviour of an assistant system and the traffic environment. A growing body of literature suggests that one possible side effect of an increase in the degree of vehicle automation is the tendency of drivers to become more heavily involved in secondary tasks while the vehicle is in motion. However, these studies have mainly been conducted in strictly controlled research environments, such as driving simulators and test tracks, and have mainly involved either low levels of automation (i.e., automation of longitudinal control by Adaptive Cruise Control (ACC)) or Highly automated driving (i.e., automation of both longitudinal and lateral control without the need for continuous monitoring). This study aims to replicate these effects during an on-road experiment in everyday traffic and to extend previous findings to an intermediate level of automation, in which both longitudinal and lateral control are automated but the driver must still monitor the traffic environment continuously (so-called Partial automation). N = 32 participants of different age groups and different levels of familiarity with ACC drove in rush-hour traffic on a highway segment. They were assisted by ACC, ACC with steering assistance (ACC+SA), or not at all. The results show that while subjective and objective driving safety were not influenced by the degree of automation, drivers who were already familiar with ACC increased the frequency of interactions with an in-vehicle secondary task in both assisted drives. However, participants generally rated performing the secondary task as less effortful when being assisted, regardless of the automation level (ACC vs. ACC+SA). The results of this on-road experiment thus validate previous findings from more-controlled research environments and extend them to Partially automated driving.     

Pedestrian interaction with automated vehicles at uncontrolled intersections

Automated Vehicles (AVs) are being developed rapidly and tested on public roads, but pedestrians’ interaction with AV is not comprehensively understood or thoroughly investigated to ensure safe operations and the public’s trust of AVs. In this study, we aimed to provide another research evidence to enhance such understanding with the use of external interfaces for facilitating the interaction between pedestrians and AVs. We developed five external interfaces, including text, symbol, animated-eye, a combination of text and symbol, and speed. These interfaces communicated five types of information, including (1) intent of AV; 2) advice to pedestrians of what to do, (3) AV’s awareness of pedestrians, (4) combination of intent and advice, and (5) vehicle movement (i.e., speed). We tested the interfaces through two field studies at uncontrolled intersections with crosswalks. The Wizard of Oz method was used, in which an experimenter worked as a driver in an instrumented vehicle and wore an outfit to be invisible to the pedestrians, thus rendering the set-up to simulate an AV interacting with a pedestrian. The interfaces were displayed on an LED panel mounted on the AV. Results showed that the AV’s external interface did not change pedestrians’ response time in comparison with the baseline without any interface. There was no statistically significant difference in response time among the external interfaces either. According to the post-experimental interview, vehicle movement pattern (e.g., vehicle speed) continued to be a significant cue for pedestrians to decide when to cross the intersections. Participants perceived the communication of the AV’s intent and vehicle speed as more beneficial than the communication of AV’s awareness. The subjective ratings showed positive effects of those interfaces that were easy to understand (e.g., text interface and speed interface), which also helped pedestrians feel safer when interacting with the AV.     

Effects of explanation-based knowledge regarding system functions and driver’s roles on driver takeover during conditionally automated driving: A test track study

The present study was designed to examine the influence of explanation-based knowledge regarding system functions and the driver’s role in conditionally automated driving (Level 3, as defined in SAE J3016). In particular, we studied how safely and successfully drivers assume control of the vehicle when encountering situations that exceed the automation parameters. This examination was conducted through a test-track experiment. Thirty-two younger drivers (mean age = 37.3 years) and 24 older drivers (mean age = 71.1 years) participated in Experiments 1 and 2, respectively. Adopting a between-participants design, in each experiment the participants were divided into two age- and sex-matched groups that were given differing levels of explanation-based knowledge concerning the system limitations of automated driving. The only information given to the less-informed groups was that, during automated driving, drivers may be required to occasionally assume control of the vehicle. The well-informed groups were given the same information, as well as details regarding the auditory-visual alerts produced by the human–machine interface (HMI) during requests to intervene (RtIs), and examples of situations where RtIs would be issued. Ten and nine RtI events were staged for each participant in Experiment 1 and 2, respectively; the participants performed a non-driving-related task while the automated driving system was functioning. For both experiments it was found that, for all RtI events, more participants in the well-informed groups than the less-informed groups successfully assumed control of the vehicle. These results suggest that, in addition to providing information regarding the possible occurrence of RtIs, explanations of HMI and RtI-related situations are effective for helping both younger and older drivers safely and successfully negotiate such events.     

Consequences of autonomous vehicles: Ambivalent expectations and their impact on acceptance

Connected and autonomous vehicles (CAVs) are within reach of widespread deployment on public roads, but public perceptions are ambivalent. The objective of the present research was to assess expectations about the consequences of CAV introduction. These expectations should explain CAV acceptance, but their relative importance is poorly understood. We conducted a survey with a representatively drawn panel sample (N = 529) from France, Germany, Italy, and the UK. The survey consisted of a large item pool of expected consequences from CAV introduction, as well as general and affective evaluation of CAVs, ease of use, and behavioral intention to use CAVs. Exploratory factor analysis revealed four facets of expected consequences: road safety, privacy, efficiency and ecological sustainability. On average, expectations were mostly positive for ecological sustainability and safety, but negative for privacy. At the same time, substantial variance existed between respondents and between countries. For safety and efficiency, improvement was expected by a third of respondents, while another third expected worsening. Respondents from Italy expected more positive consequences for safety, while respondents from both France and Germany expected more negative consequences for privacy. To different degrees, all four facets predicted the intention to use CAVs in a structural equation model, primarily via affective evaluations. For policy makers, manufacturers, and service providers, understanding the trade-offs inherent to different CAV solutions will be central to ensure citizens’ needs are respected.     

Impact of interface design on drivers’ behavior in partially automated cars: An on-road study

In partially automated vehicles, the driver and the automated system share control of the vehicle. Consequently, the driver may have to switch between driving and monitoring activities. This can critically impact the driver’s situational awareness. The human–machine interface (HMI) is responsible for efficient collaboration between driver and system. It must keep the driver informed about the status and capabilities of the automated system, so that he or she knows who or what is in charge of the driving. The present study was designed to compare the ability of two HMIs with different information displays to inform the driver about the system’s status and capabilities: a driving-centered HMI that displayed information in a multimodal way, with an exocentric representation of the road scene, and a vehicle-centered HMI that displayed information in a more traditional visual way. The impact of these HMIs on drivers was compared in an on-road study. Drivers’ eye movements and response times for questions asked while driving were measured. Their verbalizations during the test were also transcribed and coded. Results revealed shorter response times for questions on speed with the exocentric and multimodal HMI. The duration and number of fixations on the speedometer were also greater with the driving-centered HMI. The exocentric and multimodal HMI helped drivers understand the functioning of the system, but was more visually distracting than the traditional HMI. Both HMIs caused mode confusions. The use of a multimodal HMI can be beneficial and should be prioritized by designers. The use of auditory feedback to provide information about the level of automation needs to be explored in longitudinal studies.     

Effects of visual factors during automated driving of mobility scooters on user comfort: An exploratory simulator study

An automated mobility scooter is expected to provide convenient and safe transportation for users in their living area. However, there is limited research on user comfort compared to that on user safety for the automated driving of mobility scooters. Because the user does not perform driving tasks in automated driving, the visual information from the peripheral environment and visual behavior is expected to closely affect the psychological comfort of the user. This study clarifies the effects of factors related to the automated driving of mobility scooters and the peripheral environment on the visual behavior and psychological comfort of the user. Effects of driving velocity and pedestrian density on the visual behavior and psychophysiological responses of users were investigated via a driving simulator. The results showed that automated driving in an environment with a high pedestrian density can result in a decrease in fixation duration, deactivation of visual processing, sympathetic activation, and feeling of negative emotion. This implies that the assessment of visual behaviors of users is important for the design of automated mobility scooters to improve user comfort.     

Differential effects of driver sleepiness and sleep inertia on driving behavior

ObjectivesDriver sleepiness is one of the major safety issues in conventional driving and sleep inertia emerges as a driver state in automated driving. The aim of the present study was to assess the differential impacts of sleepiness and sleep inertia on driving behavior.Method61 participants completed a 10-min manual driving task during an otherwise automated drive. They completed the task (a) under an alert state, (b) under a sleepy state, and (c) after EEG-confirmed sleep. Driving performance was assessed with the parameters lane-keeping, speed choice, and speed-keeping. The eye-blink-based sleepiness measure PERCLOS (the proportion of time with eyes closed) was compared for the three driver states.ResultsLane- and speed-keeping performance were impaired under the sleepy state and after sleep, relative to the alert state. After sleep, lane-keeping behavior recovered rapidly and speed-keeping recovered by trend. Under the sleepy state, performance deteriorated. After sleep, the mean speed was lower than in the sleepy state and in the alert state. PERCLOS was increased after sleep and under the sleepy state, relative to the alert state.ConclusionsAlthough sleep inertia had detrimental effects on driving parameters similar to sleepiness, this effect rapidly vanished. Hence, while brief naps might be suitable to restore alertness in general, the minimal time needed to regain full capacity after napping should be a focus of future research.     

Interaction between pedestrians and automated vehicles: Exploring a motion-based approach for virtual reality experiments

External human machine interfaces (eHMI) might contribute to an enhanced traffic flow and road safety by providing relevant information to surrounding road users. To quantify the effect of eHMI on traffic flow, the majority of studies required participants to indicate their crossing decision in an explicit manner, such as pressing a button. While this approach proved to be efficient, the transfer to real-world behavior is unclear. Here, we propose a more realistic, motion-based approach allowing pedestrians to actually cross the road in front of a vehicle in a virtual reality environment. Participants (N = 51) encountered simulated automated vehicles (AVs) in two scenarios. We investigated the effect of different eHMIs on traffic flow and road safety. Pedestrians’́ body movements were obtained using a motion capturing system with six sensors. Our approach was validated using a two-step procedure. First, we assessed crossing behavior and subjective safety feeling while approaching AVs with and without eHMI. Second, we tested to which extent objective crossing behavior matched self-reported safety feeling. For this purpose, we evaluated if subjective safety feeling can be reliably predicted from actual crossing behavior using a functional data analysis. The proposed motion-based approach proved a valid investigation method for eHMI designs. The results indicated that eHMIs have a beneficial effect on traffic flow and road safety. Regarding traffic flow, participants crossed the road earlier and felt significantly safer when encountering an AV with an eHMI compared to no eHMI. In addition, in situations in which only some of the AVs were equipped with an eHMI, participants’ crossing behavior and safety feeling became more conservative for encounters without eHMI, indicating higher road safety. Further, subjective safety feeling was significantly predicted from actual crossing behavior. These findings highlight that eHMIs are beneficial for pedestrians’ crossing decision, both from an objective and subjective perspective.     

Factors that influence the acceptance of future shared automated vehicles – A focus group study with United Kingdom drivers

The development of Shared Automated Vehicles (SAVs) is well underway to provide mobility as a service (MaaS) and bring benefits such as reduced traffic congestion, reduced reliance on privately owned vehicles and increased independence to non-drivers. To realise the benefits of SAVs, adoption by private vehicle users is crucial. Previous research has shown this subset of users as the least likely to adopt SAVs, and it is not well understood what factors are important to achieve such adoption. The purpose of this study is to obtain an in-depth understanding of attitudes, perceptions and preferences that influence the acceptance of future SAVs for drivers. This paper presents the results from an online asynchronous focus group study with 21 British drivers as participants. From the analysis, Service Quality, Trust and Price Value emerged as the three most prominent factors to understand user acceptance of SAVs. These three main factors may be of prime importance for convincing naïve private car owners to accept high-speed SAVs. Productive use of travel time has been frequently mentioned in previous research as a benefit of vehicle automation but was scarcely mentioned by participants in this study. Shared Space Quality in introduced as an indicator for Service Quality, together with Security and Trusting Co-passengers as two indicators of Trust. Based on the findings, this paper concludes with a conceptual SAV technology acceptance model is introduced, with the results added as extended model predictors to the Unified Theory of Acceptance and Use of Technology (UTAUT2).