Analysis of the occurrence and severity of vehicle-pedestrian conflicts in marked and unmarked crosswalks through naturalistic driving study

BackgroundAlthough many studies have been conducted on the safety of pedestrian crossings, few researches have been focused on drivers' behavior in unmarked crosswalk and marked crosswalk areas. Considering that statistics of pedestrian accidents are not the same in the two types of crossing area, based on the last report of the World Health Organization, it is very critical to evaluate driver yielding behavior to determine the differences in the actions of drivers when encountering pedestrians in the two areas.MethodsThis study was conducted based on surrogate measures of safety (SMoS) collected through a Naturalistic Driving Study on 52 participants in Iran. The study was carried out from April 2017 to April 2018 using the installation of cameras in the private vehicle of the participants. The analysis of the recorded films showed that 956 conflicts have occurred in unmarked crosswalks and 392 conflicts in marked crosswalks, respectively.ResultsA model was developed for driver yielding behavior using binary logistic regression, and showed that yielding rates in unmarked crosswalsk were about fifty percent of the yielding rates in marked crosswalks. Based on the model, it is indicated that the aggressive behavior of pedestrians during the crossing, such as running, zigzag and diagonal crossing, as well as the late detection of pedestrians by drivers resulting from high-speed driving in the unmarked crossing areas, will reduce the yielding behavior rate. Also, using the Swedish traffic conflicts technique, the severity of the conflicts was classified into four general categories: encounter, potential, slight, and serious conflict, through 30 different levels on the basis of conflicting speed and time to the accident. The results showed that pedestrians behavior during conflicts of the group “encounter” and drivers’ behavior during conflicts of the groups of “potential”, “slight” and “serious”, were the principal factors in preventing collision through an evasive maneuver. The results showed that increasing the level of conflict severity, which indicates an increase of the conflicting speed and a decrease of the time remaining to point of a possible collision with pedestrian, causes drivers to yield a harsh-maneuver to prevent collision. Soft-maneuvers such as deceleration and acceleration, as well as harsh-maneuvers such as changing the lane/stop during conflicts were most driver yielding behavior during conflict groups of slight and serious. According to the results of the analysis, the behavior of drivers in marked crossing areas is better than in the unmarked crossing area, leading to safer crossing for pedestrians.ConclusionsThis study suggests that the significant differences in driver yielding behavior in the two areas is due to the late detection of pedestrians by drivers and also the less proper action by them in unmarked crosswalk areas. Thus, the probability of accidents in Unmarked Crossing areas is higher than in marked crossing areas. Consequently, the design of improved advanced driver assistance systems to identify the risk of pedestrian accident may improve the driver yielding behavior and thus increase the safety of pedestrians.     

Visualizing the autonomous vehicle’s maneuvers – Does an ecological interface help to increase the hedonic quality and safety?

This paper examines whether ecological speed information describing ongoing driving maneuvers during automated driving enhances the hedonic quality and driving safety immediately after a driving takeover. Visualizing maneuvers and trajectories has already proven effective. However, planned acceleration and deceleration in an automated vehicle have not yet been investigated. Therefore, this paper assesses how an automated vehicle’s speed control information might be presented by an ecological interface. Besides a possible increase in the hedonic quality, this information might enhance safe behavior of the human driver when it comes to a takeover. To assess these two aspects, 43 drivers participated in a dynamic driving simulator study. Using a within-subject design, two scenarios were used to compare an ecological interface, dynamically visualizing speed changes, to a conventional pop-up interface, using pop-up icons to visualize speed changes. The experimental results indicate that ecological feedback and conventional pop-up feedback do not differ regarding the hedonic quality, which was reflected by the state anxiety, usefulness, and satisfaction with the overall human-machine interface (HMI). Nonetheless, the post-hoc questionnaire on situational awareness showed a significantly lower rating for the ecological interface which may be the result of a more automatic and subconscious processing of the information given. Analyzing the takeover performance, the initial takeover time was comparably low for both interfaces. However, concerning safety, the ecological interface significantly enhanced the lateral control after takeover, and the drivers looked at the vehicle mirrors significantly earlier. In conclusion, the results show that the information given by the ecological interface may help drivers cope with a sudden takeover in a faster and more controlled way. Future applications of these findings might serve to enhance the acceptance and safety of semi-autonomous vehicles by implementing ecological interfaces.     

驾驶员选择性注意过程中的规则图式启动效应

本研究将驾驶无意视盲范式与交通违规事件识别任务相结合, 通过两个实验分别探究任务性质(有提示、无提示)和规则图式训练(训练、未训练)对驾驶员识别交通信号和违规事件的影响。结果发现, 交通规则提示能够提高有经验的驾驶员识别交通信号的正确率(实验1); 对于新手驾驶员, 则需要结合规则图式训练才能起到相同效果(实验2)。研究表明驾驶员选择性注意过程中存在规则图式启动效应, 图式训练能够弥补新手驾驶员经验的不足。     

Supporting the changing driver’s task: Exploration of interface designs for supervision and intervention in automated driving

Driving automation leads to a changing role for drivers, that is from manual vehicle control to supervising automation. Supervision of partial automation requires now and then intervention. Since the automation causes low vigilance and out-of-the-loop performance problems, this changing role is not well suited for human operators. To explore how driver-vehicle interfaces can support drivers in their changed role, we tested three concepts. Concept A was a baseline reference, providing only acoustic warnings. Concept B presented status-information and warnings behind the steering wheel. Concept C used illumination and haptic feedback in the seat-pan to direct attention outside the vehicle and to stimulate response. Concept C only provided vibrotactile feedback when intervention was needed. Results of our study show improved support for supervision with the illumination-concept, i.e. better hazard-detection and raised levels of Situation Awareness in some scenarios relevant for supervisory control. Knowing that supervision will be the dominating driver’s responsibility during partially automated driving, the illumination-concept is a recommended solution for support of the driver’s changing role. Nonetheless, neither concept B, nor C, showed additional support for intervention compared to the baseline. It was hypothesised that the combination of concept C’s stimuli for intervention-support caused counter-productive levels of annoyance. Furthermore, we concluded that intervention and supervision benefit from different interface-features and discussed possible causes underlying ambiguity between support for supervision and support for intervention shown with concept C. Therewith, the considerations in this paper contribute to further development of – and knowledge about – appropriate driver-vehicle interaction while vehicle-operation advances into operating partially automated driving systems.     

Effectiveness investigation of travel demand management measures in Beijing: Existing measures and a potential measure–tradable driving credit

Facing serious challenges of traffic congestion and air pollution, Beijing has implemented a series of traffic polices. In this paper, we first investigate the effectiveness of existing traffic measures in Beijing and analyse the underlying factors from the perspective of drivers, which provide insights for the traffic regulations in other cities worldwide. While the car ownership restriction has effectively limited the total number of vehicle in Beijing, the effect of the car use restriction only lasts for a short period. A survey on drivers’ opinions and attitudes towards the traffic situation and a potential measure, i.e., tradable driving credit (TDC), is conducted. Although traffic data shows that congestion improved and traffic was slightly congested in recent years in Beijing, most respondents believe congestion is still serious in Beijing. However, they think the impact of traffic congestion on their personal car use is relatively low, which could partly explain why so many travellers still depend on driving given the relative convenient transit facilities in Beijing. In addition, a large proportion of respondents treat personal cars as a representation of social status and think driving bring them pleasures, which indicates that it may be hard to further decrease the share of car trips in Beijing. According to their attitudes and social-economical characters, respondents’ willingness on their changes of travel behaviours under TDC is investigated. The results show that individuals’ social-economic characters such as income level, education and family size and the symbolic meaning of personal cars have significant influence on respondents’ willingness to switch to other transport modes.     

Behavioral changes to repeated takeovers in automated driving: The drivers’ ability to transfer knowledge and the effects of takeover request process

Only a couple of studies evaluated whether drivers of automated vehicles change their takeover behavior when they experience takeover requests repeatedly. Even less evidence was accumulated regarding the question whether drivers are able to transfer learned behavior to takeover situations with varying visibility characteristics and whether drivers’ takeover behavior depends on the takeover process in these situations. This paper therefore examines three research questions. First, it assesses how drivers change their behavior with the repeated experience of a takeover situation with the same visibility (fog or no fog). Second, it tests whether drivers can transfer their learned takeover behavior from a takeover situation with high or low visibility to the same takeover situation with different visibility conditions. Third, it assesses whether drivers’ takeover behavior and their experience of the situation differ between a one-step and a two-step takeover request process. Forty participants experienced a takeover situation three times. Experimental trials varied between-subjects concerning the permanent presence or absence of fog in the adaptation condition, the change of visibility conditions from fog to no fog or vice versa in the transfer condition, and the design of the takeover process with one-step or two-steps. Dependent variables included participants’ takeover time, minimum time-to-collision (TTC_min) with the construction site, deceleration and maximum steering behavior, and their ratings of criticality of the driving situation and perceived effort. Results show that participants adapted their deceleration behavior when repeatedly experiencing a takeover situation with the same visibility characteristics (adaptation condition). Changing these characteristics (transfer condition) lead to increased minimum TTCs, criticality and perceived effort ratings. In general, participants were able to maintain their takeover behavior in takeover situations with varying visibility characteristics indicating that they can transfer their takeover behavior across situations. Finally, the two-step takeover request process was associated with longer takeover times. However, minimum TTCs were larger and maximum steering movements and criticality ratings were lower compared to the one-step process. We conclude that drivers transfer their behavior across takeover situations. However, this performance comes at higher costs in terms of perceived effort and criticality. In addition, two-step takeover request processes should be favored over one-step processes when designing takeover requests. Future studies should examine the validity of the results in various takeover situations and on-the-road studies.     

What and how to tell beforehand: The effect of user education on understanding,interaction and satisfaction with driving automation

The success of introducing automated driving systems to consumers will depend on an appropriate understanding and human-automation interaction with this technology. Educating users on driving automation technology bears the potential to attain these two requirements. In a driving simulator study, we investigated the effects of user education on mental models, human-automation interaction performance (i.e., time on task, error rate, experimenter rating) and satisfaction with a Human-Machine Interface (HMI) for automated driving. N = 80 participants were randomly assigned to one of three different user education conditions or to a baseline. Subsequently, they completed several driver-initiated control transitions between manual, Level 2 (L2), and Level 3 (L3) automated driving. The results revealed that user education promoted an accurate evolution of mental models for driving automation. These, in turn, facilitated interaction performance in transitions from manual to both L2 and L3 automated driving. There was no comparable influence of prior education on performance in transitions between the automation levels. Due to the performance enhancing effects of user education, no further improvements of interaction performance were observed for educated users in comparison to uneducated users. There was no effect of user education on satisfaction. The current findings emphasize the necessity to provide information about automated vehicle HMIs to first-time users to support accurate understanding and behavior. Based on the current findings, we propose conceptual approaches to teach users and derive implications for user studies on automated vehicle HMIs.     

Effects of secondary tasks and display position on glance behavior during partially automated driving

The driving task is becoming increasingly automated, thus changing the driver’s role. Moreover, in-vehicle information systems using different display positions and information processing channels might encourage secondary task engagement. During manual driving scenarios, varying secondary tasks and display positions could influence driver’s glance behavior. However, their impact on the driver’s capability to monitor the partially automated driving system has not yet been determined. The current study assessed both the effects of different secondary tasks (Surrogate Reference Task (SuRT) vs. text reading) and display positions (head-up display (HUD) vs. center console) on driver’s glance behavior during partially automated driving in a simulated car following task. Different automation system failures regarding the lateral and longitudinal control occurred while driving. Furthermore, participants’ reported advantages, disadvantages and preferences regarding the investigated display positions as well as regarding the secondary task engagement during partially automated driving in general. Mixed design ANOVAs revealed that the HUD yielded considerably longer eyes-on display time (total and mean glance durations) than the center console. Moreover, the text reading task resulted in longer total and mean glance durations than the SuRT. Similar to manual driving scenarios, the results showed a consistent effect of display position and secondary task on the driver’s glance behavior. Despite the longer eyes-on display time for the HUD, its proximity to the driving environment might enable a faster identification of and reaction to critical situations (e.g., due to system failures). Participants would prefer the HUD as display position compared to the center console. Regarding secondary task engagement during partially automated driving participants seemed to be aware of the benefits but also of the risks.     

Drivers’ driving style and their take-over-control judgment

Advanced driver assistance systems (ADASs), which help a driver drive a car safely and easily (e.g., warning alerts, steering control, and brake/acceleration pedal operation), have increased in popularity. However, such systems have not yet been perfected. Sometimes, humans must take over control from the systems; otherwise, they can cause an accident. In this study, we focused on one of the ADASs, adaptive cruise control (ACC), which automatically maintains a selected distance from the preceding car, and investigated individual differences in take-over-control judgment and related factors. The candidate factors included driver’s manual driving style, driving performance without the ACC, and the usability evaluation of ACC. Ten participants repeated the short, strictly controlled trials in a driving simulator (DS), with a varying value of only one parameter (deceleration of the preceding car) affecting the need for intervention. First, we confirmed that the participants made the judgment based on the dangerousness of the situation and that there were individual differences in the take-over-control judgments. Some participants intervened in the ACC control in less dangerous trials, whereas other participants did not, even if their own car got very close to the preceding car. We conducted a correlation analysis and confirmed the results with the estimation of the confidence interval using a bootstrap method. As a result, we found that driving style and driving performance without ACC had a stronger relationship to the number of interventions, rather than the usability evaluation. In particular, methodical drivers, who obeyed traffic rules and manners, began to intervene in less dangerous situations. The tendency to avoid utilizing brake operations was also related to take-over-control judgment. This might be because the participants intervened by pressing the brake pedal. Our study showed that drivers’ driving style could affect the usage of ACC independently from the performance of the ACC.     

Overall performance impairment and crash risk due to distracted driving: A comprehensive analysis using structural equation modelling

Distracted driving leads to performance changes in both longitudinal and lateral control. However, driving performance, being a multidimensional phenomenon, is very difficult to be interpreted from individual performance measures. The present study aimed to exhibit the distraction impacts on overall driving performance estimated by a single measure rather than assessing distraction effects on individual performance metrics such as speed, acceleration, lateral variation, etc. The study also focused on modelling and quantifying the impacts of overall deteriorated driving performance on crash risk. To achieve the objectives, a comparative analysis of investigating phone and music player usage was conducted using Structural Equation Modelling (SEM). In total, 90 drivers’ demographic details and driving performance data in distracted and non-distracted driving environments were collected. Firstly, the latent variable “performance degradation” was derived from longitudinal and lateral performance measures. Then the structural model revealed a positive relationship between the distractions and the overall performance degradation. Finally, the crash risk was modelled against the presence of distraction and performance degradation. With a factor loading of 0.29, the impact of deteriorated driving performance (i.e., indirect impact) was found to be the highest among all the contributory factors of the crash risk. Further, the results showed that among the distractions (i.e., direct impact), texting had the highest impact (factor loading = 0.28) on crash risk followed by visual-manual tasks related to music player (factor loading = 0.21). Thus, the present study quantified the effects of deteriorated driving performance caused by distracted driving on the crash risk. Further, the study also presented quantified effects of each distracting activity on the crash risk which accounted for the factors that could not be considered through the performance degradation measure. The approach used in the present study can be adopted in designing the countermeasures using advanced driver warning systems.