Driver Situation Awareness Research Articles

The Impact of Pedestrian Interactions at Intersections on the Three Levels of Drivers’ Situation Awareness

Evaluating drivers’ situation awareness (SA) is important in the implementation of alert prioritization. This study investigates the relationship between driving performance measures (speed, acceleration and brake usage, steering wheel and lane deviation), pedestrian interaction (location, direction and motion), and driver SA. To achieve this, a controlled study was conducted with 56 participants using a Balanced Incomplete Block Design, where each participant drove 18 out of 48 possible intersections in a driving simulator environment. The Situational Awareness Global Assessment Technique (SAGAT) method was used to assess drivers’ SA. Mixed effects logit models were developed to examine the different SA Levels (perception, comprehension, projection). The driving performance measures were aggregated across three time windows (1, 3, and 5 s). The findings show significant contributions from both driving performance measures and pedestrian interactions in predicting driver SA. More specifically, a one-second time window was useful for predicting pedestrian direction and a three-second time window was best for predicting pedestrian location and intention to cross. The results indicate the importance of considering different time windows for predicting various levels of driver SA responses. These findings offer insights into factors to be considered in driver SA predictive models.

Driver Situation Awareness for Regaining Control from Conditionally Automated Vehicles: A Systematic Review of Empirical Studies.

An up-to-date and thorough literature review is needed to identify factors that influence driver situation awareness (SA) during control transitions in conditionally automated vehicles (AV). This review also aims to ascertain SA components required for takeovers, aiding in the design and evaluation of human-vehicle interfaces (HVIs) and the selection of SA assessment methodologies. Conditionally AVs alleviate the need for continuous road monitoring by drivers yet necessitate their reengagement during control transitions. In these instances, driver SA is crucial for effective takeover decisions and subsequent actions. A comprehensive review of influential SA factors, SA components, and SA assessment methods will facilitate driving safety in conditionally AVs but is still lacking. A systematic literature review was conducted. Thirty-four empirical research articles were screened out to meet the criteria for inclusion and exclusion. A conceptual framework was developed, categorizing 23 influential SA factors into four clusters: task/system, situational, individual, and nondriving-related task factors. The analysis also encompasses an examination of pertinent SA components and corresponding HVI designs for specific takeover events, alongside an overview of SA assessment methods for conditionally AV takeovers. The development of a conceptual framework outlining influential SA factors, the examination of SA components and their suitable design of presentation, and the review of SA assessment methods collectively contribute to enhancing driving safety in conditionally AVs. This review serves as a valuable resource, equipping researchers and practitioners with insights to guide their efforts in evaluating and enhancing driver SA during conditionally AV takeovers.

Pairing in-vehicle intelligent agents with different levels of automation: implications from driver attitudes, cognition, and behaviors in automated vehicles

ABSTRACT In-vehicle intelligent agents (IVIAs) have been developed to improve user experience in autonomous vehicles. Yet, the impact of the automation system on driver behavior and perception toward IVIAs is unclear. In this study, we conducted three experiments with 73 participants in a driving simulator to examine how automation system parameters (the level of automation system and IVIA features) influence driver attitudes, cognition, and behaviors when driving or riding in a simulated vehicle. We focused on subjective evaluations of driver-agent interaction and driver trust toward IVIAs to assess driver attitudes, driver situation awareness, and visual distraction to capture their cognition, and their driving performance to understand their behaviors. Our results show that the level of automation system affects drivers’ attitudes toward agent capabilities (e.g. perceived intelligence). Embodiment benefits are more pronounced with Level 5 systems, while speech style, in general, is more influential in determining affective aspects of user attitudes (e.g. Warmth, Likability). As the level of automation increases, drivers engage in more visual distractions. In addition, conversational speech style in general encouraged safer driving behaviors indicated by more stable lateral control under lower levels of automation. Our findings uncover the path of how system parameters affect driver behaviors through system evaluation and trust in agents. These findings have important implications for the development of cohesive user experiences in future transportation systems.

Analysis of driver behavior at grade-separated intersections to support design

Understanding driver behaviors in varied traffic scenarios is critical to the design of safe and efficient roadways and traffic control device. This research presents an analysis of driver cognitive workload, situation awareness (SA) and performance for three different scenarios, including a standard intersection and contraflow grade-separated intersections (C-GSI) and quadrant GSI (Q-GSI) with lane assignment sign manipulations. The study used a simulator-based driving experiment with application of the NASA Task Load Index and Situation Awareness Global Assessment Technique to assess the influence of the scenarios on driver behavioral responses. The findings reveal challenges for drivers navigating the C-GSI, characterized by diminished SA and elevated workload. These states were associated with behaviors such as delayed lane changes, missed opportunities for appropriate lane changes, heightened acceleration behavior within deceleration segments, and frequent speeding. In contrast, while drivers in the Q-GSI scenario faced elevated workloads, their SA remained steady, largely due to lane-specific signs facilitating early lane changes. Although the Q-GSI led to increased speed variability and slight increases in deceleration, the use of supplementary speed signage revealed a promising alternative to the S-intersection. Correlation analysis highlighted a significant relationship between mental workload and acceleration responses, indicating that increased acceleration was associated with higher mental workload. In addition, a significant negative correlation between driver perceived performance and absolute lane deviations indicated that drivers with higher self-assessed performance were more accurate in lane-keeping. The study underscores the need for GSIs and signage designs that support driver SA, manage cognitive workload to improve driver performance and increase road safety.

Evaluation of eye-catching effect in highway tunnel entrance area based on saccade behavior

Objective This study aimed to investigate the effects of visual attraction in the entrance areas of highway tunnels on drivers’ saccade behavior and driving safety, with the objective of providing insights for tunnel entrance design and driver education. Methods Fifty participants were recruited for the naturalistic driving experiment. Four different visually attractive driving scenarios (baseline, landscape-style architecture, tip slogan, and billboard) were selected. Eye-tracking technology was utilized to record and analyze the scanning behavior of participating drivers. Various metrics, including saccade duration, frequency, amplitude, velocity, and the peak-to-average ratio of saccade velocity (PARSV), were measured and compared across different scenarios. Results The results revealed different patterns of visual scanning dynamics among the four experimental scenarios, reflecting the different levels of visual-cognitive demands and situation awareness of drivers. The visual attraction in the tunnel entrance area resulted in prolonged saccade duration, increased saccade amplitude, reduced saccade frequency, and slower, less stable saccade velocity. Among these, drivers exhibited the most intricate and unstable scanning behavior, accompanied by the lowest level of situational awareness, particularly under the influence of visual attraction of tip slogan. Conclusions The findings highlight the importance of considering visual attraction in tunnel entrance design. Minimizing unnecessary visual attraction can help reduce distractions and maintain driver attention, ultimately enhancing driving safety. Driver education programs should emphasize the significance of adapting to visual attraction at tunnel entrances to promote safer driving practices. Overall, this study contributes valuable insights into the effects of visual attraction on driver scanning behavior, facilitating the development of strategies to improve tunnel design and driver education.

Examining Driver Situation Awareness in the Takeover Process of Conditionally Automated Driving With the Effect of Age

Examining Driver Situation Awareness in the Takeover Process of Conditionally Automated Driving With the Effect of Age

Post Take-Over Performance Varies in Drivers of Automated and Connected Vehicle Technology in Near-Miss Scenarios.

This study examined the impact of monitoring instructions when using an automated driving system (ADS) and road obstructions on post take-over performance in near-miss scenarios. Past research indicates partial ADS reduces the driver's situation awareness and degrades post take-over performance. Connected vehicle technology may alert drivers to impending hazards in time to safely avoid near-miss events. Forty-eight licensed drivers using ADS were randomly assigned to either the active driving or passive driving condition. Participants navigated eight scenarios with or without a visual obstruction in a distributed driving simulator. The experimenter drove the other simulated vehicle to manually cause near-miss events. Participants' mean longitudinal velocity, standard deviation of longitudinal velocity, and mean longitudinal acceleration were measured. Participants in passive ADS group showed greater, and more variable, deceleration rates than those in the active ADS group. Despite a reliable audiovisual warning, participants failed to slow down in the red-light running scenario when the conflict vehicle was occluded. Participant's trust in the automated driving system did not vary between the beginning and end of the experiment. Drivers interacting with ADS in a passive manner may continue to show increased and more variable deceleration rates in near-miss scenarios even with reliable connected vehicle technology. Future research may focus on interactive effects of automated and connected driving technologies on drivers' ability to anticipate and safely navigate near-miss scenarios. Designers of automated and connected vehicle technologies may consider different timing and types of cues to inform the drivers of imminent hazard in high-risk scenarios for near-miss events.

Regular looks out the window do not maintain situation awareness in highly automated driving

The loss of situation awareness (SA) caused by the out-of-the-loop phenomenon is a major problem for successful take-overs after highly automated driving. Despite recent advances in driving automation, a human backup for the automated system is currently indispensable. For uneventful situations, the automation works and drivers can perform engaging non-driving-related tasks (NDRTs). However, they need to be ready to respond at all times in case of a take-over request (TOR). For smooth and safe transition from automated to manual control, SA has to be re-established within seconds. We investigated whether regular out-the-window looks can facilitate this process. This driving simulation study is the first to investigate whether such situation awareness maintenance can improve take-over performance after a TOR. In a repeated-measures design, 89 participants experienced 5-minutes of highly automated driving with a NDRT followed by a critical take-over situation. Every 30 s, participants were asked either to monitor the traffic scene for 3 s (monitoring condition) or to merely interrupt the NDRT for 3 s (control condition). We ensured that incidental traffic monitoring was impossible during the NDRT. In the control condition, participants saw a blank screen during the 3-seconds intervals. Contrary to common intuition, the results show neither improvements in objective take-over performance nor in perceived SA, driver availability, criticality, stress, risk assessment, or adequacy of the TOR. We conclude that brief interruptions of the NDRT to look at the ongoing traffic are unable to maintain the driver's SA during highly automated driving. Future studies should therefore focus on other measures to optimize the recovery of SA.

The Effect of AR-HUD Takeover Assistance Types on Driver Situation Awareness in Highly Automated Driving: A 360-Degree Panorama Experiment

Human-machine co-driving presents a significant hurdle in automated driving system. The takeover process in automated driving system involves complex human factors, failure to takeover the vehicle and control driving behavior during the takeover process may lead to severe traffic safety hazards. An augmented reality head-up display (AR-HUD) takeover assistance information can provide real-time assistance information to the driving environment, enhancing drivers’ situation awareness (SA) and takeover decisions in highly automated driving system. This study investigated the impact of different AR-HUD types of takeover assistance information display. Three AR-HUD types, corresponding to the three pre-takeover behavioral processes (perception, understanding, and prediction), were evaluated: PSR (assistance in perceiving the source of risk), AS (assistance in analyzing situations), and MD (assistance in making decisions). The baseline (without assistance information) was used as the control group. In a driving simulation experiment using 360° panoramic video, seventy-nine participants performed SA assessment and visual tracking tasks. Questionnaire and eye-tracking data indicated that the type of AR-HUD displayed positively influenced drivers’ SA and takeover decisions, with AS being the most effective in enhancing SA and improving takeover performance. Additionally, this study compared the differences between the three types of AR-HUD and the baseline under two takeover request lead times (TORlt) of 5 seconds and 7 seconds. It was found that drivers’ SA was lower when TORlt was shorter (with the corresponding AR-HUD display also being shorter). This study provides insight concerning the impact of various types of AR-HUD takeover assistance information display and TORlt on driving safety. The findings support the further optimization of AR-HUD takeover assistance information design.

Research on Enhanced Situation Awareness Model with DMI Visualization Cues for High-Speed Train Driving

With the rapid increase of high-speed train driving automation, the human-computer interaction of high-speed train driving is changing. It is a major dilemma to maintain a well-interactive process between the driver and the driving system for the intelligent train operation. Nevertheless, the enhancement of the driver’s situation awareness (SA) is an effective way to promote the process of driver-driving system interaction. To achieve this purpose, we proposed an enhanced SA model based on the perceptual cycle model (PCM). The model helps to identify the high-speed train DMI interface elements that affect the different SA level of drivers and provides theoretical support for describing the SA change pattern of driving tasks. We proposed a new display mode (ESA mode) for enhancing driver SA according to this model, and conducted driving simulator experiments to explore the differences of SA change patterns between the ESA mode and the normal mode (NSA mode) for 16 subjects. The experimental results showed that the ESA mode successfully overcame the subjects’ SA recession in the NSA mode. The interaction with enhanced SA can help the driver remain vigilant and further improve the general driving performance during monotonous process of automated high-speed train driving.

Design of head-up display interfaces for automated vehicles

This study aimed to identify which information should be displayed on a head-up display (HUD) in semi-automated vehicles to enable the driver to maintain better situational awareness during the manual operation of the vehicle. It further explored how does the size of the field of view of the HUD affects such information presentation, while taking into consideration user's personal preferences and opinions. Four head-up display interfaces were developed differing in the amount, frequency and field of view of visual information presentation, and were implemented in a simulated semi-automated vehicle. The HUDs were evaluated in a user-study with a within-subject design with 30 participants. The obtained results revealed that versions with smaller and larger field of view HUDsevoke similar driving performance, levels of cognitive load, user experience and perceived usability, suggesting that the HUD's field of view size does not have an overall significant effect on the driver's situational awareness. The results reveal that display of information, which can help with obtaining and maintaining higher situational awareness levels, contribute to better driving performance. Drivers on the other hand, prefer also display of information for lower situational awareness levels.

Hazard perception test among young inexperienced drivers and risk analysis while driving through a T-junction

In this article, the hazard-based accident duration model and the reaction time due to various types of distractions are considered. This analysis is mainly considered T-junction or T-junctions like all other forms of road geometries which are more prone to accident. The model proposed is constructed by using a electronics devices along with driving simulator, to study the behavior of the yang inexperienced drivers as well as an experienced driver about their reaction by calling their phone from unknown numbers intentionally. The results show that drivers distracted by mobile phones uses the hard breaking due to least available time to respond after identification of an event. Some of the research and theory bearing on decision making and risk perception, driver situation awareness, and possible mediators of risk-taking is also analyzed here.

Human-Vehicle Interaction to Support Driver's Situation Awareness in Automated Vehicles: A Systematic Review

Autonomous driving will change the role of the driver. From being the main actor in driving, the driver will now have a supervisory role during the autonomous driving phases. However, if the driver has to take over control of the vehicle, he must be aware of the situation around him. This is why it is important to develop interfaces to keep him in the loop. This article proposes a systematic review of Human Vehicle Interaction (HVI) providing situation awareness in the context of autonomous driving. 37 articles presenting such interactions are analyzed in terms of design of the interaction (modalities, location, conveyed information) but also in term of evaluation and experimental conditions. We present an overview of previous studies in order to highlight the work already done or in progress. Current studies present mainly monomodal interfaces although the evaluation of multimodal interactions present promising results in this field.

Driver Take-Over Behaviour Study Based on Gaze Focalization and Vehicle Data in CARLA Simulator.

Autonomous vehicles are the near future of the automobile industry. However, until they reach Level 5, humans and cars will share this intermediate future. Therefore, studying the transition between autonomous and manual modes is a fascinating topic. Automated vehicles may still need to occasionally hand the control to drivers due to technology limitations and legal requirements. This paper presents a study of driver behaviour in the transition between autonomous and manual modes using a CARLA simulator. To our knowledge, this is the first take-over study with transitions conducted on this simulator. For this purpose, we obtain driver gaze focalization and fuse it with the road's semantic segmentation to track to where and when the user is paying attention, besides the actuators' reaction-time measurements provided in the literature. To track gaze focalization in a non-intrusive and inexpensive way, we use a method based on a camera developed in previous works. We devised it with the OpenFace 2.0 toolkit and a NARMAX calibration method. It transforms the face parameters extracted by the toolkit into the point where the user is looking on the simulator scene. The study was carried out by different users using our simulator, which is composed of three screens, a steering wheel and pedals. We distributed this proposal in two different computer systems due to the computational cost of the simulator based on the CARLA simulator. The robot operating system (ROS) framework is in charge of the communication of both systems to provide portability and flexibility to the proposal. Results of the transition analysis are provided using state-of-the-art metrics and a novel driver situation-awareness metric for 20 users in two different scenarios.

A Computational Cognitive Model of Driver Response Time for Scheduled Freeway Exiting Takeovers in Conditionally Automated Vehicles.

This study develops a computational model to predict drivers' response time and understand the underlying cognitive mechanism for freeway exiting takeovers in conditionally automated vehicles (AVs). Previous research has modeled drivers' takeover response time in emergency scenarios that demand a quick response. However, existing models may not be applicable for scheduled, non-time-critical takeovers as drivers take longer to resume control when there is no time pressure. A model of driver response time in non-time-critical takeovers is lacking. A computational cognitive model of driver takeover response time is developed based on Queuing Network-Model Human Processor (QN-MHP) architecture. The model quantifies gaze redirection in response to takeover request (ToR), task prioritization, driver situation awareness, and driver trust to address the complexities of drivers' takeover strategies when sufficient time budget exists. Experimental data of a preliminary driving simulator study were used to validate the model. The model accounted for 97% of the experimental takeover response time for freeway exiting. The current model can successfully predict drivers' response time for scheduled, non-time-critical freeway exiting takeovers in conditionally AVs. This model can be applied to the human-machine interface design with respect to ToR lead time for enhancing safe freeway exiting takeovers in conditionally AVs. It also provides a foundation for future modeling work towards an integrated driver model of freeway exiting takeover performance.

The driver’s instantaneous situation awareness when the alarm rings during the take-over of vehicle control in automated driving

Objective The driver’s instantaneous situation awareness in the process of take-over of vehicle control in automated driving has not yet been thoroughly investigated. The proposed research can provide a better understanding of the driver’s perceived characteristics and identify the most urgent information requirements of the on-site scenario when the driver’s eye sight returns from other distractors to the driving scene. Methods We conducted an experiment in simulated automated driving to study the participants’ ability of instantaneous hazard perception and judgment. The scene pictures, which were displayed in millisecond time, were used to imitate the situations that drivers would see when the distracted drivers returned their gaze from the distractive sources to the road in the simulated automated driving. Results The results show that the driving state, scene representation time and hazard levels affect the instantaneous situation awareness of drivers. In addition, the scene perception accuracy of the group who played games during automated driving is much lower than that of the group that chatted with the copilot. The longer picture-showing duration decreases the accuracy of hazard identification, whereas the shorter picture-showing duration increases the accuracy of hazard perception and the hazard rating score. Conclusions In conclusion, distraction reduces the accuracy of the instantaneous scene perception of drivers, and drivers behave more cautiously in decision making when the driving situations are more hazardous. This study provides a good theoretical basis for the design of hazard warning information for automated driving.

A Preliminary Study of Modeling Driver Situational Awareness based on SEEV and ACT-R Models

In automated driving, it is important to maintain drivers’ situational awareness (SA) in order to help them avoid unnecessary interventions and negotiate challenging scenarios where human takeovers are needed. Our study developed computational models to predict a driver’s SA of a target object. Using the SEEV (Salience, Effort, Expectancy, and Value) and ACT-R (Adaptive Control of Thought-Rational) framework, the model achieved an accuracy of 78.3%, an F1-score of 0.66, and the area under the receiver operating characteristic (AUROC) value of 0.773 with object features as inputs. On average, the model had a Root Mean Square Error (RMSE) of 0.18 to predict the SA of a target object across participants. In relative to the existing models, our model not only had comparable predictive performance but also considered the underlying mechanism of SA to increase model interpretability. Our research will provide essential and necessary steps toward developing in-vehicle SA prediction and assistance systems.

Identifying the Out Of The Loop phenomenon during driving automation using spontaneous gaze behavior

Human factors research that addresses driver-automation interaction is required to mitigate crashes related to the deployment of automated vehicles. While Automated Driving Systems still require driver supervision, there will remain room for human error. This paper presents results from the first in a series of experiments that aim to estimate driver situation awareness using spontaneous gaze behavior and to ensure the driver is ready for takeover. Spontaneous gaze behavior was studied and compared among 13 participants between partial driving automaton and driver assistance automations condition to extract indicators of the out of the loop phenomenon.

Allocation of Drivers’ Visual Attention During Preliminary Uses of Automated Driving: A Wizard-of-Oz Study

In this experiment, we analyzed drivers’ visual attention during their first experiences with automated driving (AD). At the beginning of the study, participants ( n = 45) received one of three AD training modules: paper, video, or practice. They then drove two sessions on a public road with a Wizard-Of-Oz vehicle, each ending with a request to intervene (RTI). The first session (forward path) included 10 min of AD with a mandatory non-driving-related task (NDRT). The second session (return path) included 10 min of AD with a nonmandatory NDRT. Control checks (number and duration of gazes) toward the road and rear-view mirrors and three self-assessment questionnaires (trust, acceptability, and technophilia) were measured during both sessions. The results indicated a decrease in road and rear-view mirror gazes over time, mainly for the practice group. Drivers with a low level of trust glanced at the road and mirrors more often than participants with a high level of trust. In addition, participants with a high level of technophilia spent less time controlling the road traffic but only during the forward path. Visual attention on the road decreased rapidly during the first minutes of AD, mainly among drivers who followed the practice training and those who had a high level of trust and were tech-savvy. We observed that participants looked at the rear-view mirrors after 10 and 14 s on average after the RTI. These results raise questions about driver situation awareness in critical situations, including the capacity to intervene in an efficient and safe way during takeover requests.

An Analysis of Driver Gaze Behaviour at Roundabouts

Determining driver situational awareness is one of the most difficult tasks in driver performance profiling. Perception, being the first level of driver situational awareness, is perhaps the only objectively quantifiable factor for driver inattention detection. In the context of this paper, driver perception is defined as their ability to understand their operating environment and peripheral contextual information. One of the most reliable means of determining driver perception is through eye tracking and gaze concentration. In this study, a set of carefully chosen in-vehicle gaze targets are used to determine driver gaze concentration on/off the road. The time drivers spend looking at these predefined gaze targets has been used to characterise driver visual behaviour at roundabouts during normal driving. In total, 24 drivers completed a 22 km route, including two 5-exit two-lane roundabouts in urban and suburban traffic. This study shows that on the approach to roundabouts, driver gaze direction and gaze concentration was often diverted away from the direction of travel of the vehicle and for some drivers it took a significant amount of time to return their gaze towards the direction of travel of the vehicle. The study is based on the premise that a driver not looking at key regions in a scene cannot perceive the contents of those regions with sufficient detail for safe driving. It is not possible to determine a driver’s level of understanding of a scene, unless the parameters of the scene as well as the driver’s response is determined in real time.