Semi-automated versus highly automated driving in critical situations caused by automation failures

The purpose of this study was to examine the effects of vehicle automation and automation failures on driving performance. Previous studies have revealed problems with driving performance in situations with automation failures and attributed this to drivers being out-of-the-loop. It was therefore hypothesized that driving performance is safer with lower than with higher levels of automation. Furthermore, it was hypothesized that driving performance would be affected by the extent of the automation failure. A moving base driving simulator was used. The design contained semi-automated and highly automated driving combined with complete, severe, and moderate deceleration failures. In total the study involved 36 participants. The results indicate that driving performance degrades when the level of automation increases. Furthermore, it is indicated that car drivers are worse at handling complete than partial deceleration failures.     

External communication of automated vehicles in mixed traffic: Addressing the right human interaction partner in multi-agent simulation

The urban traffic system is most likely to change in the next years to a mixed traffic with human drivers, vulnerable road users, and automated vehicles. In the past, the development of external communication approaches for automated vehicles focused on scenarios where an automated vehicle communicates with either a pedestrian or a human driver. However, interactions with more than one traffic partner are more realistic. Therefore, a study with 42 participants was conducted with a multi-agent simulation in which an automated vehicle interacted simultaneously with two participants, a pedestrian and a driver of a manual vehicle. In this study, two main scenarios were investigated in order to evaluate the safety and efficiency of the interactions and to determine whether the human road users feel correctly addressed. In one scenario, the pedestrian had to cross the road in front of the automated and the manual vehicle, which were approaching from different sides. In the other, the manual vehicle had to drive through a bottleneck in front of the oncoming automated vehicle, while the pedestrian had to cross the road after both vehicles passed. The communication approach of the automated vehicle consisted of implicit signals using a speed profile and lateral offset within its lane, and explicit signals using an external human–machine interface. The results of the study show that no collisions were observed in terms of safety and no significant negative effects on efficiency were measured. However, in contrast to single agent interactions, a majority of participants felt wrongly addressed in situations where the automated vehicle signals the right-of-way to the other human road user. It can be concluded that the communication approach of the automated vehicle needs to be modified in order to address certain road users more clearly.     

Assessing the driving distraction effect of vehicle HMI displays using data mining techniques

With the rapid development of human–machine interface (HMI) systems in vehicles, driving distraction caused by HMI displays affects road safety. This study presents a data mining technique to model the four driving distraction indicators: speed deviation, lane departure standard deviation, dwell time, and mean glance time. Driving distraction data was collected on a real-car driving simulator. 3 secondary tasks in 13 mass produced cars were tested by 24 drivers. The random forest algorithm outperformed linear regression, extreme gradient boosting, and multi-layer perceptron as the best model, demonstrating good regression performance as well as good interpretability. The result of random forest showed that the importance of target speed is large for all driving distraction indicators. Among the variables of interaction and user interface design, less step and less on-screen distance of finger movement are efficient for lowering lane departure standard deviation and dwell time. The position of right point is another important variable, and should be between 37 and 47 degrees on a typical sample in this study. A larger angle leads to bigger lane departure, while a smaller angle leads to bigger mean glance time. Most variables of HMI display positioning themselves are not important. This study provides one driving distraction assessment method with a variable impact trend analysis for HMI secondary tasks in an early phase of product development.     

Human machine interface design for continuous support of mode awareness during automated driving: An online simulation

In the transition towards higher levels of vehicle automation, one of the key concerns with regards to human factors is to avoid mode confusion, when drivers misinterpret the driving mode and therewith misjudge their own tasks and responsibility. To enhance mode awareness, a clear human centered Human Machine Interface (HMI) is essential. The HMI should support the driver tasks of both supervising the driving environment when needed and self-regulating their non-driving related activities (NDRAs). Such support may be provided by either presenting continuous information on automation reliability, from which the driver needs to infer what task is required, or by presenting continuous information on the currently required driving task and allowed NDRA directly. Additionally, it can be valuable to provide continuous information to support anticipation of upcoming changes in the automation mode and its associated reliability or required and allowed driver task(s). Information that could support anticipation includes the available time until a change in mode (i.e. time budget), information on the upcoming mode, and reasons for changing to the upcoming mode. The current work investigates the effects of communicating this potentially valuable information through HMI design. Participants received information from an HMI during simulated drives in a simulated car presented online (using Microsoft Teams) with an experimenter virtually accompanying and guiding each session. The HMI either communicated on automation reliability or on the driver task, and either included information supporting anticipation or did not include such information. Participants were thinking aloud during the simulated drives and reported on their experience and preferences afterwards. Anticipatory information supported understanding about upcoming changes without causing information overload or overreliance. Moreover, anticipatory information and information on automation reliability, and especially a combination of the two, best supported understandability and usability. Recommendations are provided for future work on facilitating supervision and NDRA self-regulation during automated driving through HMI design.     

After you?! – Use of external human-machine interfaces in road bottleneck scenarios

In the near future, automated vehicles (AVs) will enter the urban transport system. This fact will lead to mixed traffic consisting of AVs, human car drivers and vulnerable road users. Since the AV’s passenger no longer has to monitor the driving scene, conventional communication does not exist anymore, which is essential for traffic efficiency and safety. In research, there are plenty of studies focusing on how AVs could communicate with pedestrians. One approach is to use external human-machine interfaces (eHMIs) on the AV’s surface. In contrast to the studies dealing with AV-pedestrian communication, this paper focuses on communication strategies of AVs with drivers of regular vehicles in different road bottleneck scenarios. The eHMI development and design is building on previously defined requirements and on fundamentals of human visual perception. After designing several eHMI drafts, we conducted a user survey with 29 participants resulting in the final eHMI concept. The evaluation of the evolved eHMI was conducted in a driving simulator experiment with 43 participants investigating the AV-human driver interaction at road bottlenecks. The participants were assigned either to the experimental group being faced with the eHMI or to the baseline group without explicit communication. The results show significantly shorter passing times and fewer crashes among the human drivers in the group with the eHMI. Additionally, the paper researches the aftereffects of an automation failure, where the AV first yields the right of way and then changes its strategy and insisted on priority. Experiencing the automation failure is reflected in increased passing times, reduced acceptance ratings and a lower perceived usefulness. In conclusion, especially in unregulated bottleneck scenarios flawless communication via eHMIs increases traffic efficiency and safety.     

How does glance behavior influence crash and injury risk? A ‘what-if’ counterfactual simulation using crashes and near-crashes from SHRP2

As naturalistic driving data become increasingly available, new analyses are revealing the significance of drivers’ glance behavior in traffic crashes. Due to the rarity of crashes, even in the largest naturalistic datasets, near-crashes are often included in the analyses and used as surrogates for crashes. However, to date we lack a method to assess the extent to which driver glance behavior influences crash and injury risk across both crashes and near-crashes. This paper presents a novel method for estimating crash and injury risk from off-road glance behavior for crashes and near-crashes alike; this method can also be used to evaluate the safety impact of secondary tasks (such as tuning the radio). We apply a ‘what-if’ (counterfactual) simulation to 37 lead-vehicle crashes and 186 lead-vehicle near-crashes from lead-vehicle scenarios identified in the SHRP2 naturalistic driving data. The simulation combines the kinematics of the two conflicting vehicles with a model of driver glance behavior to estimate two probabilities: (1) that each event becomes a crash, and (2) that each event causes a specific level of injury. The usefulness of the method is demonstrated by comparing the crash and injury risk of normal driving with the risks of driving while performing one of three secondary tasks: the Rockwell radio-tuning task and two hypothetical tasks. Alternative applications of the method and its metrics are also discussed. The method presented in this paper can guide the design of safer driver–vehicle interfaces by showing the best tradeoff between the percent of glances that are on-road, the distribution of off-road glances, and the total task time for different tasks.     

The impact of smart driving aids on driving performance and driver distraction

In-vehicle information systems (IVIS) have been shown to increase driver workload and cause distraction, both of which are causal factors for accidents. This simulator study evaluates the impact that two prototype ergonomic designs for a smart driving aid have on workload, distraction and driving performance. Scenario complexity was also manipulated as an independent variable. Results showed that real-time delivery of smart driving information did not increase driver workload or adversely affect driver distraction, while also having the positive effect of decreasing mean driving speed in both the simple and complex driving scenarios. Subjective workload was shown to increase with task difficulty, as well as revealing important differences between the two interface designs. The findings are relevant to the development and implementation of smart driving interface designs in the future.     

自动驾驶车中的人机信任

自动驾驶是当前智能汽车发展的重要方向。在实现完全自动化驾驶前, 驾驶员和自动驾驶系统共享车辆控制权, 协同完成驾驶任务。在该人-机共驾阶段, 人对自动驾驶系统的信任是影响自动驾驶中人机协同效率与驾驶安全的关键要素; 驾驶员对自动驾驶车辆保持适当的信任水平对驾驶安全至关重要。本研究结合信任的发展阶段与影响因素提出了动态信任框架。该框架将信任发展分为倾向性信任、初始信任、实时信任和事后信任四个发展阶段, 并结合操作者特征(人)、系统特征(自动驾驶车系统)、情境特征(环境)三个关键因素分析不同阶段的核心影响因素以及彼此间的内在关联。根据该框架, 信任校准可从监测矫正、驾驶员训练、优化HMI设计三类途径展开。未来研究应更多关注驾驶员和人机系统设计特征对信任的影响, 考察信任的实时测量和功能特异性, 探讨驾驶员和系统的相互信任机制, 以及提升信任研究的外部效度。