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.     

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.     

The power and sensitivity of four core driver workload measures for benchmarking the distraction potential of new driver vehicle interfaces

This study evaluated the power and sensitivity of several core driver workload measures in order to better understand their use as a component of future driver distraction potential evaluation procedures of the in-vehicle human machine interface (HMI). Driving is a task that requires visual, manual and cognitive resources to perform. Secondary tasks, such as mobile phone use and interaction with in-built navigation, which load onto any of these three processing resources increase driver workload and can lead to impaired driving. Because workload and distraction potential are interrelated, a comprehensive method to assess driver workload that produces valid and predictive results is needed to advance the science of distraction potential evaluation. It is also needed to incorporate into New Car Assessment Program (NCAP) testing regimes. Workload measures of cognitive (DRT [Detection Response Task] Reaction Time), visual (DRT Miss Rate), subjective (NASA-TLX [driver workload questionnaire]), and temporal demand (Task Interaction Time) were collected as participants drove one of 40 vehicles while completing a variety of secondary tasks with varying interaction requirements. Of the evaluated measures, variance and power analyses demonstrated that Task Interaction Time is the most sensitive in detecting differences in driver workload between different in-vehicle HMIs, followed by DRT Miss Rate, NASA-TLX and finally DRT Reaction Time. There were relatively weak correlations between each of the four measures. These results suggest that Task Interaction Time, coupled with a reliable visual demand metric such as DRT Miss Rate, eye glance coding, or visual occlusion, more efficiently detect differences in driver workload between different HMIs compared to DRT Reaction Time and the NASA-TLX questionnaire. These results can be used to improve the understanding of the utility of each of these core driver workload measures in assessing driver distraction potential.     

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.     

Supporting cooperative driving behaviour by technology – HMI solution,acceptance by drivers and effects on workload and driving behaviour

The paper compares and evaluates three different HMIs (Human Machine Interface) for an ADAS (Advanced Driver Assistance System) supporting cooperative interactions between drivers while merging and turning left. In road traffic, cooperation means that drivers (cooperation partners) coordinate their driving behaviour in a way that they facilitate each other’s intended driving manoeuvres. An experimental study was conducted with 30 participants in a static high-end simulator. The test scenarios included merging onto a motorway and turning left at a rural intersection. As independent variables, the HMI (Baseline vs. Sensor vs. C2X (Car-to-everything)) was varied in addition to the cooperation situation (merging vs. turning left). All HMI variants were based on a HUD (Head-Up Display). In the Baseline condition, the HMI only showed information about speed and navigation. The Sensor HMI visualised additionally the driving situation as it can be detected by the vehicle's own sensors. The C2X HMI was based on C2X communication and also represented the different phases of manoeuvre coordination with the cooperation partner. The traffic flow and the behaviour of the surrounding traffic did not differ between the different HMI variants, in order to ensure that the traffic situation did not influence the participants’ evaluation of the HMI variants. The dependent variables included subjective (e.g. acceptance, usability) and objective measures (e.g. driving and gaze behaviour). The results showed that a system supporting cooperative interactions is generally accepted by drivers. The most preferred system was the C2X HMI. The advantages of a C2X based HMI were an improved user experience leading to a greater intent to use the ADAS for cooperative driving interactions, increased system trust, and an easier handling of the system. The workload of the C2X HMI did not exceed the level reported for the Baseline or the Sensor HMI – although the C2X HMI presented more information. The results are used to derive indications for the design of assistance systems supporting cooperative driving behaviour.     

Manual drivers’ experience and driving behavior in repeated interactions with automated Level 3 vehicles in mixed traffic on the highway

In the near future, conditionally automated vehicles (CAVs; SAE Level 3) will travel alongside manual drivers (≤ SAE level 2) in mixed traffic on the highway. It is yet unclear how manual drivers will react to these vehicles beyond first contact when they interact repeatedly with multiple CAVs on longer highway sections or even during entire highway trips. In a driving simulator study, we investigated the subjective experience and behavioral reactions of N = 51 manual drivers aged 22 to 74 years (M = 41.5 years, SD = 18.1, 22 female) to driving in mixed traffic in repeated interactions with first-generation Level 3 vehicles on four highway sections (each 35 km long), each of which included three typical speed limits (80 km/h, 100 km/h, 130 km/h) on German highways. Moreover, the highway sections differed regarding the penetration rate of CAVs in mixed traffic (within-subjects factor; 0%, 25%, 50%, 75%). The drivers were assigned to one of three experimental groups, in which the CAVs differed regarding their external marking, (1) status eHMI, (2) no eHMI, and (3) a control group without information about the mixed traffic. After each highway section, drivers rated perceived safety, comfort, and perceived efficiency. Drivers were also asked to estimate the penetration rate of CAVs on the previous highway section. In addition, we analyzed drivers’ average speed and their minimum time headways to lead vehicles for each speed zone (80 km/h, 100 km/h, 130 km/h) as well as the percentage of safety critical interactions with lead vehicles (< 1 s time headway). Results showed that manual drivers experienced driving in mixed traffic, on average, as more uncomfortable, less safe and less efficient than driving in manual traffic, but not as dangerous. A status eHMI helps manual drivers identify CAVs in mixed traffic, but the eHMI had no effect on manual drivers’ subjective ratings or driving behavior. Starting at a level of 25% Level 3 vehicles in mixed traffic, participants' average speed decreased significantly. At the same time, the percentage of safety critical interactions with lead vehicles increased with an increasing penetration rate of CAVs. Accordingly, additional measures may be necessary in order to at least keep the existing safety level of driving on the highway.     

The Body-Machine Interface: A New Perspective on an Old Theme

ABSTRACT

Body-machine interfaces establish a way to interact with a variety of devices, allowing their users to extend the limits of their performance. Recent advances in this field, ranging from computer interfaces to bionic limbs, have had important consequences for people with movement disorders. The authors provide an overview of the basic concepts underlying the body-machine interface with special emphasis on their use for rehabilitation and for operating assistive devices. They outline the steps involved in building such an interface and highlight the critical role of body-machine interfaces in addressing theoretical issues in motor control as well as their utility in movement rehabilitation.     

The Need for Speed: Agility Selling

In turbulent business times, organizations have sought to become more agile. Organizational agility has been asserted to require workforce agility, but thus far, little focus has been given to workers. Traditionally, salespeople have sought ways to differentiate themselves and create advantage for customers. This paper asserts that, in order to continue this tradition of differentiation and advantage, salespeople must engage in agility thinking. Agility has been studied as it applies to manufacturing processes and other firm activities. Agile salespeople have a mental dexterity at the point of customer contact. They are quick to observe opportunities and are able to anticipate customers’ ever-changing needs, wants, and preferences. This paper presents a framework for agility selling, applying concepts from the agility manufacturing literature and proffering distinct characteristics of sales force agility.     

Phonon densities of states of gamma-cerium and delta-cerium measured by time-of-flight inelastic neutron scattering

Time-of-flight inelastic neutron scattering spectra were measured on cerium metal at temperatures near the fcc (gamma)-to-bcc (delta) transition temperature. Phonon density-of-states (DOS) curves were extracted from data acquired over a wide range of momentum transfers. A large softening of the phonon DOS was found in going from gamma-cerium to delta-cerium, and this accounts for an increase in vibrational entropy of (0.71 +/- 0.05)k _B/atom. To be consistent with the latent heat of the gamma-delta transition, this increase in vibrational entropy must be accompanied by a large decrease in electronic entropy. The results not only confirm the recent discovery of a significant electronic contribution to the gamma-delta transition but also suggest that it may be twice that previously reported.