Disengagement from driving when using automation during a 4-week field trial

IntroductionA small body of research on the real-world use of commercially available partial driving automation suggests that drivers may struggle with or otherwise lapse in adequately monitoring the system and highway environment, and little is known about key issues such as how behavior associated with system use changes over time. The current study assessed how driver disengagement, defined as visual-manual interaction with electronics or removal of hands from the wheel, differed as drivers became more accustomed to partial automation over a 4-week trial.MethodsTen volunteers drove a Volvo S90 with adaptive cruise control (ACC), which automates speed and headway, and Pilot Assist, which combines ACC and continuous lane centering. Instrumentation captured automation use, secondary task activity, hands-on-wheel status, vehicle speed, and GPS location during all trips.ResultsThe longer drivers used the Pilot Assist partial automation system, the more likely they were to become disengaged, with a significant increase in the odds of observing participants with both hands off the steering wheel or manipulating a cell phone relative to manual control. Results associated with use of ACC found comparable or lower levels of disengagement compared to manual driving as the study progressed.DiscussionThis study highlights concerns about vehicle control and the degree to which drivers remain actively in the loop when using automation. Calls for implementing more robust driver monitoring with partial automation appear warranted—particularly those that track head or eye position.     

How impressions of other drivers affect one’s behavior when merging lanes

In recent years, systems have been developed to realize automatic driving based on objective information such as the relative distance and relative speed between vehicles. However, humans still must drive in complex situations, for instance, when merging lanes. In such driving situations, it is possible that people make decisions based not only on objective information, but also on subjective information. This study examined how subjective information, specifically, a driver’s impression of the other vehicle, affects the decision to merge in front of or behind the other vehicle when merging lanes on a highway. Twenty participants (n_male = 10; n_female = 10; M_age = 43.92 [SD_age = 11.40]) joined two experiments, Days 1E and 2E, using a driving simulator. Two months after participating in Day 1E the participants joined Day 2E. In the Day 1E, they drove either on the merging lane or the main lane and merged lanes while considering the other vehicle driving along the adjacent lane. This experiment measured the probability that the participants drove in front of another vehicle upon merging, which is defined as “lead probability.” The Day 2E was similar to 1E, except for the manipulation of the participants’ impression of the other vehicle as being aggressive/cautious via acceleration/deceleration of the other vehicle, and through the contents of the instructions regarding the other vehicle’s driving characteristics. In the Day 2E, the participants were randomly assigned to two: Aggressive or Cautious conditions. As the result of comparing the lead probabilities, it was found that only when the participants were driving on the merging lane and had the impression that the other vehicle is aggressive, the impression lowered the lead probability. The result indicates that people make decisions based not only on objective information but also on subjective information for specific driving situations, such as merging lanes. These findings can help in the development of automated driving systems that allow safer merging.     

Situational influencing factors on understanding cooperative actions in automated driving

Cooperative interacting vehicles are a promising approach in the context of automated driving. To ensure understanding and acceptance of such systems, the underlying mechanisms of human cooperation in the context of traffic must be understood. In a driving simulator study, we investigated how situational factors influence cooperative behaviour in a lane change situation on a two-lane German highway during automated driving in the left lane. When another car in the right lane was approaching a slower truck, participants (N = 32) were asked by an automated system to either accelerate, decelerate or maintain speed. The driver’s scope of action, the situation’s criticality for the lane-changing vehicle and the display of intention to change lanes were manipulated. A hierarchical multinomial logistic regression revealed that a wider scope of action, a higher situation’s criticality and signalling the intention to change lanes positively influenced cooperative behaviour by accelerating and decelerating. These results might be applied to design user-centred automated cooperatively interacting vehicles.     

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.     

Longer-term exposure vs training: Their effect on drivers' mental models of ADAS technology

Drivers often learn about the advanced driver assistance systems (ADAS) on their vehicles over time and through trial and error. While this experience can aid drivers’ understanding about the systems, it may not necessarily lead to sufficient and accurate mental models, especially concerning less frequent “edge case” situations. This study recruited 39 new owners of vehicles equipped with ADAS technology to which the owners were naïve. The initial mental model of these owners was evaluated using a mental model assessment. To understand changes in mental models over time the assessment was repeated six times over the course of approximately 6 months. Weekly mileage, technology usage, and information regarding their exposure to edge case scenarios was also collected. At the end of the 6 months, participants completed a simulator drive using adaptive cruise control (ACC) that included several edge cases. Over the course of the first 6 months of vehicle ownership, drivers’ scores on the mental model assessment improved. These improvements were largely due to increased understanding of the technology’s limitations as opposed to improvements in knowledge about system function. With respect to driving performance in the simulator session, the mental model scores were not predictive of responses to the edge cases. However, a comparison of those mental model scores against weak and strong mental model benchmark scores gathered in a previous study revealed that mental models improve over 6 months (for some drivers), but not to the level of understanding of a group that received a short but extensive introduction to ACC. This suggests that there is room for improvement in how drivers gain understanding about driver support features and further underscores the need of training and education for proper use and interactions.     

Effect of driving inactivity on driver’s lateral positioning control: A driving simulator study

Overall on-road driving skill is a cumulated acquisition of driving skills learned during training period and furthered tempered with post-licensing driving exposure on the roads. Training serves to develop a person’s basic driving skills to operate safely on the roads at first, while subsequent on-road driving hones the driving skills further. Inactivity from on-road driving can result in deterioration of driving skills. Particularly for the case of inactivity immediately following licensure, which is not uncommon in Singapore, the learned skills can degrade rapidly. This research aims to examine the effect of driving inactivity on the learned driving skill, i.e. lateral positioning control, specifically for young-inexperienced drivers. A series of experiments were conducted on a driving simulator for different subject groups involving active driver, novice driver (freshly-licensed driver), inactive driver (never ever drive upon licensure), and no licence subjects. The participants drove through a stretch of expressway under free-flow conditions, and lane positioning control was monitored. It is found that without on-road driving exposure, driving skills like maintenance of lane positioning control in terms of lane wandering and lane encroachment may deteriorate after some periods of inactivity, i.e. 3 months. The deterioration varies at different rate, with faster deterioration for more complex tasks. The skills level can regress close to un-trained level for complex driving tasks. The findings also suggest that on-road driving exposure is needed to not only retain but also to improve the lane positioning control skills.     

Human factors evaluation of an in-vehicle active traffic and demand management (ATDM) system

This research study focused on the evaluation of an emulated in-vehicle Active Traffic and Demand Management (ATDM) system on Interstate 66 in Northern Virginia. Traditional ATDM systems rely on infrastructure-mounted variable message signs (VMS) to provide information (speed limits, lane availability, etc.) to the traveling public. By providing information about dynamic roadway conditions on an in-vehicle device, the ATDM may improve driving safety and performance by allowing drivers to remain consistently aware of forthcoming traffic conditions and roadway requirements; even when external signage is inaccessible. This study represents an initial investigation of an emulated in-vehicle ATDM to provide developers with design guidance and ensure that unintended consequences, such as distraction, do not undermine the potential benefits.Twenty younger and 20 older participants, accompanied by a member of the research team, experienced the following ATDM features on an in-vehicle device (IVD) mounted to the dashboard: (1) dynamic speed limits, (2) dynamic lane use/shoulder control, (3) High Occupancy Vehicle (HOV) restrictions, and (4) roadway information from variable message signs. The IVD was equipped with auditory and visual alerts notifying the driver when relevant visual information was updated. Research questions addressed distraction and driver behavior associated with use of the system. Qualitative and quantitative participant data was acquired from the instrumented vehicle, various questionnaires, and researcher observation.Several key findings were uncovered: (1) The IVD, as tested, did not warrant classification as a source of distraction according to the NHTSA guidelines; v2) There was a significant difference in eye-glance durations to the IVD when comparing the VMS alerts to both the speed limit and lane management alerts; and (3) The speed limit alert motivated participants to alter their speed (per survey results and participant speed data).     

Compensating for failed attention while driving

While operating a motor vehicle, drivers must pay attention to other moving vehicles and the roadside environment in order to detect and process critical information related to the driving task. Using a driving simulator, this study investigated the effects of an unexpected event on driver performance in environments of more or less clutter and under situations of high attentional load. Attentional load was manipulated by varying the number of neighboring vehicles participants tracked for lane changes. After baseline-driving behavior was established, the unexpected event occurred: a pedestrian ran into the driver’s path. Tracking-accuracy, brake initiation, swerving, and verbal report of the unexpected pedestrian were used to assess driver performance. All participants verbally reported noticing the pedestrian. However, analyses of driving behavior revealed differences in the reactions to the pedestrian: drivers braked faster and had significantly less deviation in their steering heading with a lower attentional load, and participants in low clutter environments had a larger overall change in velocity. This research advances the understanding of how drivers allocate attention between various stimuli and the trade-offs between a driver’s focus on an assigned task and external objects within the roadway environment. Moreover, the results of this research lend insight into how to construct roadway environments that encourage driver attention toward the most immediate and relevant information to reduce both vehicle-to-vehicle and vehicle-to-pedestrian interactions.     

Driving around bends with or without shoulders: The influence of bend direction

Paved shoulders have long been used to create “forgiving” roads where drivers can maintain control of their vehicles even when as they drift out of the lane. While the safety benefits of shoulders have been well documented, their effects on driver behavior around curves have scarcely been examined. The purpose of this paper is to fill this gap by assessing whether the addition of shoulders affects driver behavior differently as a function of bend direction. Driver behavior in a driving simulator was analyzed on left and right curves of two-lane rural roads in the presence and absence of 0.75-m and 1.25-m shoulders. The results demonstrated significant changes in drivers’ lateral control when shoulders were provided. In the absence of oncoming traffic, the shoulders caused participants to deviate more toward the inner lane edge at curve entry, at the apex and at the innermost position on right bends but not left ones. In the presence of oncoming traffic, this also occurred at the apex and the innermost position, leading participants to spend more time off the lane on right curves. Participants did not slow down in either traffic condition to compensate for steering farther inside, thereby increasing the risk of lane departure on right curves equipped with shoulders. These findings highlight the direction-specific influence of shoulders on a driver’s steering control when driving around bends. They provide arguments supporting the idea that drivers view paved shoulders as a new field of safe travel on right curves. Recommendations are made to encourage drivers to keep their vehicle within the lane on right bends and to prevent potential interference with cyclists when a shoulder is present.     

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.     

Lane change manoeuvres and safety margins

The relation between perceptual information and the motor response during lane-change manoeuvres was studied in a fixed-based driving simulator. Eight subjects performed 48 lane changes with varying vehicle speed, lane width and direction of movement. Three sequential phases of the lane change manoeuvre are distinguished. During the first phase the steering wheel is turned to a maximum angle. After this the steering wheel is turned to the opposite direction. The second phase ends when the vehicle heading approaches a maximum that generally occurs at the moment the steering wheel angle passes through zero. During the third phase the steering wheel is turned to a second maximum steering wheel angle in opposite direction to stabilize the vehicle in the new lane. Duration of the separate phases were analysed together with steering amplitudes and Time-to-Line Crossing in order to test whether and how drivers use the outcome of each phase during the lane change manoeuvre to adjust the way the subsequent phase is executed. During the first phase the time margin to the outer lane boundary was controlled by the driver such that a higher speed was compensated for by a smaller steering wheel amplitude. Due to this mechanism the time margin to the lane boundary was not affected by vehicle speed. During the second phase the speed with which the steering wheel was turned to the opposite direction was affected by the time margins to the lane boundary at the start of the second phase. Thereafter, smaller minimum time margins were compensated for by a larger steering wheel amplitude to the opposite direction. The results suggest that steering actions are controlled by the outcome of previous actions in such a way that safety margins are maintained. The results also suggest that visual feedback is used by the driver during lane change manoeuvres to control steering actions, resulting in flexible and adaptive steering behaviour. Evidence is presented in support of the idea that temporal information on the relation between the vehicle and lane boundaries is used by the driver in order to control the motor response.     

Interaction between pedestrians and automated vehicles: A Wizard of Oz experiment

Automated vehicles (AVs) will be introduced on public roads in the future, meaning that traditional vehicles and AVs will be sharing the urban space. There is currently little knowledge about the interaction between pedestrians and AVs from the point of view of the pedestrian in a real-life environment. Pedestrians may not know with which type of vehicle they are interacting, potentially leading to stress and altered crossing decisions. For example, pedestrians may show elevated stress and conservative crossing behavior when the AV driver does not make eye contact and performs a non-driving task instead. It is also possible that pedestrians assume that an AV would always yield (leading to short critical gaps). This study aimed to determine pedestrians’ crossing decisions when interacting with an AV as compared to when interacting with a traditional vehicle. We performed a study on a closed road section where participants (N = 24) encountered a Wizard of Oz AV and a traditional vehicle in a within-subject design. In the Wizard of Oz setup, a fake ‘driver’ sat on the driver seat while the vehicle was driven by the passenger by means of a joystick. Twenty scenarios were studied regarding vehicle conditions (traditional vehicle, ‘driver’ reading a newspaper, inattentive driver in a vehicle with “self-driving” sign on the roof, inattentive driver in a vehicle with “self-driving” signs on the hood and door, attentive driver), vehicle behavior (stopping vs. not stopping), and approach direction (left vs. right). Participants experienced each scenario once, in a randomized order. This allowed assessing the behavior of participants when interacting with AVs for the first time (no previous training or experience). Post-experiment interviews showed that about half of the participants thought that the vehicle was (sometimes) driven automatically. Measurements of the participants’ critical gap (i.e., the gap below which the participant will not attempt to begin crossing the street) and self-reported level of stress showed no statistically significant differences between the vehicle conditions. However, results from a post-experiment questionnaire indicated that most participants did perceive differences in vehicle appearance, and reported to have been influenced by these features. Future research could adopt more fine-grained behavioral measures, such as eye tracking, to determine how pedestrians react to AVs. Furthermore, we recommend examining the effectiveness of dynamic AV-to-pedestrian communication, such as artificial lights and gestures.     

Analysis of the factors influencing overtaking in two-lane highways: A driving simulator study

BackgroundHuman factors are among the leading causes of frontal collision accidents. Therefore, understanding the factors that affect driver behaviour during overtaking is essential.ObjectiveThis research proposed a methodology to observe passing manoeuvres on two-lane highways in a driving simulator and to investigate the effects of the speed of an impeding vehicle, the type of the vehicle to be overtaken and the passing sight distance on the following gap distance as an indicator of driver behaviour.MethodThe repeated measures experiment allowed for 640 possible overtakings with a sample of 80 participants. The speed of the impeding vehicle, the type of the impeding vehicle and the passing sight distance were used as within-subject factors with eight treatments. The driver’s age, gender, and experience were considered the between-subject factors.ResultsWhen the speed of the impeding vehicle was 60 km/h, the participants adopted a following gap in passing sight distance of 446 m (M = 81.32 m), which was larger than the following gap in passing sight distance of 560 m (M = 70.84 m).ConclusionAmong the factors that were considered, the effect of the speed of an impeding vehicle on the following gap at the beginning of overtaking was higher than the effect of the type of the impeding vehicle or the passing sight distance. Together, these combination values can describe the driver behaviour and help to improve the standards-based design values to increase safety.     

The effects of adaptive cruise control (ACC) headway time on young-experienced drivers' overtaking tendency in a driving simulator

The preference to maintain a certain desired speed is perhaps the most prevalent explanation for why a driver of a manually driven car decides to overtake a lead vehicle. Still, the motivation for overtaking is also affected by other factors such as aggressiveness, competitiveness, or sensation-seeking caused by following another vehicle. Whether such motivational factors for overtaking play a role in partially automated driving is yet to be determined. This study had three goals: (i) to investigate whether and how a driver's tendency to overtake a lead vehicle changes when driving a vehicle equipped with an adaptive cruise control (ACC) system. (ii) To study how such tendencies change when the headway time configuration of the ACC system varies. (iii) To examine how the manipulation of the speed and speed variance of the lead vehicle affect drivers' tendencies to overtake a lead vehicle. We conducted two different experiments, where the second experiment followed the first experiment's results. In each experiment, participants drove three 10–12 min simulated drives under light traffic conditions in a driving simulator under manual and level one (L1) automation driving conditions. The automation condition included an ACC with two headway time configurations. In the first experiment, it was 1 sec and 3 secs, and in the second, it was 1 sec and 2 secs. Each drive included six passing opportunities representing three different speeds of the lead vehicle (−3 km/h, +3 km/h, +6 km/h relative to the participant), with or without speed variance. Results show that drivers tended to overtake a lead vehicle more often in manual mode than in automated driving modes. In the first experiment, ACC with a headway time of 1 sec led to more overtaking events than ACC with 3 secs headway time. In addition, the relative speed of the lead vehicle and its speed variability affected overtaking tendencies. In the second experiment, the relative speed of the lead vehicle and its speed variability affected overtaking tendencies only when interacting with each other and with driving configuration. When the speed of the lead vehicle was +3 km/h and included variability, more overtaking events occurred in manual mode than both automation modes. This work has shown that driving with ACC might help reduce overtaking frequencies and more considerable when the headway time is set to 3 secs.     

Physiological indicators of driver workload during car-following scenarios and takeovers in highly automated driving

This driving simulator study, conducted as a part of Horizon2020-funded L3Pilot project, investigated how different car-following situations affected driver workload, within the context of vehicle automation. Electrocardiogram (ECG) and electrodermal activity (EDA)-based physiological metrics were used as objective indicators of workload, along with self-reported workload ratings. A total of 32 drivers were divided into two equal groups, based on whether they engaged in a non-driving related task (NDRT) during automation (SAE Level 3) or monitored the drive (SAE Level 2). Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ∼ 18 min each, of Short (0.5 s) and Long (1.5 s) Time Headway conditions during automated car-following (ACF), which was followed by a takeover that happened with or without a lead vehicle. Results showed that driver workload due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that a lead vehicle maintain a shorter THW can significantly increase driver workload during takeover scenarios, potentially affecting driver safety. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional cognitive or attentional demands on the driver. Our results indicated that ECG and EDA signals are sensitive to variations in workload, which warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help future driver monitoring systems respond appropriately to the limitations of the driver, and predict their performance in the driving task, if and when they have to resume manual control of the vehicle after a period of automated driving.     

Haptic Guidance Can Enhance Motor Learning of a Steering Task

Haptic guidance can improve the immediate performance of a motor task by enforcing a desired pattern of kinematics, but several studies have found that it impairs motor learning. In this study, we studied whether guidance from a robotic steering wheel can improve one's short-term learning of steering a simulated vehicle. We developed a computer algorithm that adapted the firmness of the guidance based on ongoing error. Training with "guidance-as-needed" or "fixed guidance" allowed participants to learn to steer without experiencing large errors and produced slightly better immediate retention than did training without guidance, apparently because participants were better able to learn when to initiate turns. Training with guidance-as-needed produced slightly better results than training with fixed guidance: the guidance-as-needed participants' errors were significantly smaller when guidance was removed. However, this difference quickly dissipated with more practice. We conclude that haptic guidance can benefit short-term learning of a steering-type task while also limiting performance errors during training.     

The impact of auditory continual feedback on take-overs in Level 3 automated vehicles

ObjectiveTo implement auditory continual feedback into the interface design of a Level 3 automated vehicle and to test whether gaze behavior and reaction times of drivers improved in take-over situations.BackgroundWhen required to assume manual control in take-over situations, drivers of Level 3 automated vehicles are less likely than conventional drivers to spot potential hazards, and their reaction time is longer. Therefore, it is crucial that the interface of Level 3 automated vehicles will be designed to improve drivers’ performance in take-over situations.MethodIn two experiments, participants drove a simulated route in a Level 3 automated vehicle for 35 min with one imminent take-over event. Participants’ gaze behavior and performance in an imminent take-over event were monitored under one of three auditory interface designs: (1) Continual feedback. A system that provides verbal driving-related feedback; (2) Persistent feedback. A system that provides verbal driving-related feedback and a persistent beep; and (3) Chatter feedback. A system that provides verbal non-driving-related feedback. Also, there was a control group without feedback.ResultsUnder all three auditory feedback designs, the number of drivers' on-road glances increased compared to no feedback, but none of the designs shortened reaction time to the imminent event.ConclusionIncreasing the number of on-road glances during automated driving does not necessarily improve drivers’ attention to the road and their reaction times during take-overs.ApplicationPossible implications for the effectiveness of auditory continual feedback should be considered when designing interfaces for Level 3 automated vehicles.     

Computerized attention retraining for individuals with elevated health anxiety

Background and Objectives: Current cognitive-behavioral theorists conceptualize hypochondriasis as excessive health anxiety (HA). Growing evidence suggests that elevated HA is associated with attentional bias (AB) toward potential health-threat information. Design: This study aimed to examine the effects of attention retraining among individuals with elevated HA, using the established attention modification programs (AMP) designed to train participants to disengage attention from ideographically chosen health-threat words. Methods: Thirty-six randomly assigned individuals with elevated HA completed eight twice-weekly sessions of the AMP (n = 18) or the attention control condition (ACC; n = 18). Results: Despite using the well-established AMP protocol widely used within the field of anxiety disorders, we did not find evidence for change in AB following training. Further, AMP did not outperform ACC in reducing HA and other relevant emotional symptoms. However, both AMP and ACC evidenced overall significant symptom reduction in most of the outcome measures, including overall HA, anxiety sensitivity, general depression and anxiety, and somatic complaints. Conclusions: Further research is needed to better understand the effects and mechanisms of AMP as a possible cognitive intervention for HA.     

Learning and development of trust,acceptance and the mental model of ACC. A longitudinal on-road study

To harness the potential of advanced driver assistance systems, drivers must learn how to use them in a safe and appropriate manner. The present study investigates the learning process, as well as the development of trust, acceptance and the mental model for interacting with adaptive cruise control (ACC). Research questions aim to model the learning process in mathematical/statistical terms, examine moments and conditions when these processes stabilize, and assess how experience changes the mental model of the system. A sample of fifteen drivers without ACC experience drove a test vehicle with ACC ten consecutive times on the same route within a 2-month period. All participants were fully trained in ACC functionality by reading the owner’s manual in the beginning. Results show that learning, as well as the development of acceptance and trust in ACC follows the power law of learning. All processes stabilize at a relatively high level after the fifth session, which corresponds to 185 km or 3.5 h of driving. No decline is observable with ongoing system experience. However, limitations that are not experienced tend to disappear from the mental model if they are not activated by experience. Therefore, it is recommended that users be periodically reminded of system limitations (e.g. by intelligent tutoring systems) to make sure that corresponding knowledge nodes are activated.     

The effect of visual HMIs of a system assisting manual drivers in manoeuvre coordination in system limit and system failure situations

Ambiguous situations in traffic often require communication and cooperation between road users. In order to resolve these situations and increase cooperative driving behavior in situations of merging or turning left, manual drivers could be assisted by an advanced driver assistance system (ADAS) for cooperative driving. This simulator study investigated the behavior of drivers confronted with system limits and failures of such a system. The ADAS used in this study informed the driver about an upcoming cooperation situation and gave advice on how to behave (e.g. reduce speed, change lane). Two test situations were implemented: a system freeze and an unexpected event, which could not be detected by the system. In order to find the most fitting HMI solution, the place of presentation (head-up display (HUD) vs. instrument cluster) as well as the form of presentation (dynamic vs. symbolic) were varied. The results indicated that the most fitting HMI solution to support the driver in a complex coordinated driving situation is a dynamic HUD, mainly due to the positive effect on glance behavior. However, advantages of both forms of presentation were revealed, as each form of presentation increased the probability of recognition for one of the test situations. The fewest collisions took place with the dynamic form of presentation.