Physiological Indicators of Driver Workload During Car-Following Scenarios and Takeovers in Highly Automated Driving

2021 ◽  
Author(s):  
Vishnu Radhakrishnan ◽  
Natasha Merat ◽  
Tyron Louw ◽  
Rafael Goncalves ◽  
Wei Lyu ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Electrodermal Activity ◽  
Automated Driving ◽  
Physiological Indicators ◽  
Car Following ◽  
Time Headway ◽  
Driver Workload ◽  
Driving Task ◽  
Vehicle Automation ◽  
Lead Vehicle

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 or monitored the drive. Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ~18 minutes 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. We observed that the workload on the driver due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that shorter THWs and the presence of a lead vehicle can significantly increase driver workload during takeover scenarios, potentially affecting the safety of the vehicle. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional mental or attentional demands on the driver. To conclude, our results indicated that ECG and EDA signals are sensitive to variations in workload, and hence, warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help the system respond appropriately to the limitations of the driver and predict their performance in driving task if and when they have to resume manual control of the vehicle.

scholarly journals Do drivers change their manual car-following behaviour after automated car-following?

2020 ◽  
Author(s):  
Tyron Louw ◽  
Rafael Goncalves ◽  
Guilhermina Torrao ◽  
Vishnu Radhakrishnan ◽  
Wei Lyu ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Cruise Control ◽  
Automated Driving ◽  
Behavioural Adaptation ◽  
Car Following ◽  
Time Headway ◽  
Driving Behaviour ◽  
Driving Assistance ◽  
Change In Mean ◽  
Lead Vehicle

There is evidence that drivers’ behaviour adapts after using different advanced driving assistance systems. For instance, drivers’ headway during car-following reduces after using adaptive cruise control. However, little is known about whether, and how, drivers’ behaviour will change if they experience automated car-following, and how this is affected by engagement in non-driving related tasks (NDRT). The aim of this driving simulator study, conducted as part of the H2020 L3Pilot project, was to address this topic. We also investigated the effect of the presence of a lead vehicle during the resumption of control, on subsequent manual driving behaviour. Thirty-two participants were divided into two experimental groups. During automated car-following, one group was engaged in an NDRT (SAE Level 3), while the other group was free to look around the road environment (SAE Level 2). Both groups were exposed to Long (1.5 s) and Short (.5 s) Time Headway (THW) conditions during automated car-following, and resumed control both with and without a lead vehicle. All post-automation manual drives were compared to a Baseline Manual Drive, which was recorded at the start of the experiment. Drivers in both groups significantly reduced their time headway in all post-automation drives, compared to a Baseline Manual Drive. There was a greater reduction in THW after drivers resumed control in the presence of a lead vehicle, and also after they had experienced a shorter THW during automated car following. However, whether drivers were in L2 or L3 did not appear to influence the change in mean THW. Subjective feedback suggests that drivers appeared not to be aware of the changes to their driving behaviour, but preferred longer THWs in automation. Our results suggest that automated driving systems should adopt longer THWs in car-following situations, since drivers’ behavioural adaptation may lead to adoption of unsafe headways after resumption of control.

scholarly journals Do drivers change their manual car-following behaviour after automated car-following?

2020 ◽  
Author(s):  
Tyron Louw ◽  
Rafael Goncalves ◽  
Guilhermina Torrao ◽  
Vishnu Radhakrishnan ◽  
Wei Lyu ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Cruise Control ◽  
Automated Driving ◽  
Behavioural Adaptation ◽  
Car Following ◽  
Time Headway ◽  
Driving Behaviour ◽  
Driving Assistance ◽  
Change In Mean ◽  
Lead Vehicle

AbstractThere is evidence that drivers’ behaviour adapts after using different advanced driving assistance systems. For instance, drivers’ headway during car-following reduces after using adaptive cruise control. However, little is known about whether, and how, drivers’ behaviour will change if they experience automated car-following, and how this is affected by engagement in non-driving-related tasks (NDRT). The aim of this driving simulator study, conducted as part of the H2020 L3Pilot project, was to address this topic. We also investigated the effect of the presence of a lead vehicle during the resumption of control, on subsequent manual driving behaviour. Thirty-two participants were divided into two experimental groups. During automated car-following, one group was engaged in an NDRT (SAE Level 3), while the other group was free to look around the road environment (SAE Level 2). Both groups were exposed to Long (1.5 s) and Short (.5 s) Time Headway (THW) conditions during automated car-following, and resumed control both with and without a lead vehicle. All post-automation manual drives were compared to a Baseline Manual Drive, which was recorded at the start of the experiment. Drivers in both groups significantly reduced their time headway in all post-automation drives, compared to a Baseline Manual Drive. There was a greater reduction in THW after drivers resumed control in the presence of a lead vehicle, and also after they had experienced a shorter THW during automated car-following. However, whether drivers were in L2 or L3 did not appear to influence the change in mean THW. Subjective feedback suggests that drivers appeared not to be aware of the changes to their driving behaviour, but preferred longer THWs in automation. Our results suggest that automated driving systems should adopt longer THWs in car-following situations, since drivers’ behavioural adaptation may lead to adoption of unsafe headways after resumption of control.

Does automated driving affect time-to-collision judgments?

2018 ◽  
Vol 62 (1) ◽  
pp. 1833-1833 ◽  
Author(s):  
Natalie R. Lodinger ◽  
Patricia R. DeLucia
Keyword(s):  
Reaction Time ◽  
Visual Information ◽  
Secondary Task ◽  
Driving Simulator ◽  
Cognitive Resources ◽  
Automated Driving ◽  
Time To Collision ◽  
Perceptual Judgments ◽  
Driving Task ◽  
Lead Vehicle

Automation presumably frees cognitive resources because drivers do not have to control the vehicle. Those resources may be reallocated to processing visual information relevant to driving, such as optic flow, which is relevant for judgments of time-to-collision (TTC). On the other hand, drivers may not use cognitive resources freed during automation to process information relevant to the driving task and improve performance. Drivers may choose to allocate cognitive resources freed during automation to non-driving, secondary tasks (Merat, Jamson, Lai, & Carsten, 2012; Rudin-Brown & Parker, 2004). Therefore, automated driving may lead to performance decrements, particularly when drivers need to resume manual control of the vehicle (Strand, Nilsson, Karlsson, & Nilsson, 2014). The current study compared TTC judgments between automated and manual driving, using a prediction-motion (PM) task which presumably relies on cognitive resources (Tresilian, 1995). We included a braking task to determine whether we could replicate prior reports that drivers brake later during automated driving compared to manual driving (Rudin-Brown & Parker, 2004; de Winter, Happee, Martens & Stanton, 2014). Including PM and braking tasks let us determine whether automation affected only responses (i.e., brake reaction time) or also affected visual perception (i.e., TTC estimation). We hypothesized that automation would affect perceptual judgments rather than solely responses. We expected TTC judgments to be more accurate during automated driving compared to manual driving. We also expected that adding a secondary task that demands cognitive resources would be more detrimental to TTC judgments during automation because the driver would place more cognitive resources on the secondary task during automation than when manually controlling the vehicle. With a driving simulator, participants completed eight drives using manual or automated driving. During half of the drives, participants completed a secondary task, the twenty questions task (TQT), in addition to driving. The TQT is presumably similar to a cell phone conversation because it uses a “question and answer” format (Horrey, Lesch, & Garabet, 2009; Merat et al., 2012, p. 765). At the end of each drive, a critical incident occurred. A vehicle directly in front of the participant’s vehicle decelerated at a rate faster than the automation was capable of braking. Therefore, the automation did not respond to this vehicle’s deceleration. In the braking task, participants used the brake pedal to avoid collision with the lead vehicle. In the PM task, the lead vehicle decelerated for between 0.24 and 3.04 s and then the screen went black. Participants pressed a button to indicate when they thought their vehicle would have hit the lead vehicle if the vehicles’ motions continued in the same manner after the screen went black. Results suggest that automation can affect perceptual judgments in addition to driving responses (e.g., braking). TTC judgments were more accurate, and brake reaction time was faster, during automated driving than manual driving. This occurred even while performing a cognitively-demanding secondary task, suggesting that participants used resources freed by automation to process visual information relevant to TTC judgments rather than complete non-driving tasks. To realize this safety benefit, it is important to design automated systems so that freed cognitive resources are assigned to information relevant to the driving task and not to non-driving tasks.

scholarly journals Takeover Requests in Highly Automated Truck Driving: How Do the Amount and Type of Additional Information Influence the Driver–Automation Interaction?

2018 ◽  
Vol 2 (4) ◽  
pp. 68 ◽  
Author(s):  
Natalie T. Richardson ◽  
Lukas Flohr ◽  
Britta Michel
Keyword(s):  
Driving Simulator ◽  
Situational Awareness ◽  
Automated Driving ◽  
Human Machine Interaction ◽  
Transport Efficiency ◽  
Additional Information ◽  
Level 3 ◽  
Driving Task ◽  
Machine Interface ◽  
Vehicle Automation

Vehicle automation is linked to various benefits, such as increase in fuel and transport efficiency as well as increase in driving comfort. However, automation also comes with a variety of possible downsides, e.g., loss of situational awareness, loss of skills, and inappropriate trust levels regarding system functionality. Drawbacks differ at different automation levels. As highly automated driving (HAD, level 3) requires the driver to take over the driving task in critical situations within a limited period of time, the need for an appropriate human–machine interface (HMI) arises. To foster adequate and efficient human–machine interaction, this contribution presents a user-centered, iterative approach for HMI evaluation of highly automated truck driving. For HMI evaluation, a driving simulator study [n = 32] using a dynamic truck driving simulator was conducted to let users experience the HMI in a semi-real driving context. Participants rated three HMI concepts, differing in their informational content for HAD regarding acceptance, workload, user experience, and controllability. Results showed that all three HMI concepts achieved good to very good results in these measures. Overall, HMI concepts offering more information to the driver about the HAD system showed significantly higher ratings, depicting the positive effect of additional information on the driver–automation interaction.

Safety Enhancement by Automated Driving: What are the Relevant Scenarios?

2020 ◽  
Vol 64 (1) ◽  
pp. 1686-1690
Author(s):  
Niklas Grabbe ◽  
Michael Höcher ◽  
Alexander Thanos ◽  
Klaus Bengler
Keyword(s):  
Traffic Safety ◽  
Road Traffic ◽  
Test Cases ◽  
Automated Driving ◽  
Road Traffic Safety ◽  
Human Driver ◽  
Driving Task ◽  
Promising Solution ◽  
Vehicle Automation ◽  
Safety Enhancement

Automated driving offers great possibilities in traffic safety advancement. However, evidence of safety cannot be provided by current validation methods. One promising solution to overcome the approval trap (Winner, 2015) could be the scenario-based approach. Unfortunately, this approach still results in a huge number of test cases. One possible way out is to show the current, incorrect path in the argumentation and strategy of vehicle automation, and focus on the systemic mechanisms of road traffic safety. This paper therefore argues the case for defining relevant scenarios and analysing them systemically in order to ultimately reduce the test cases. The relevant scenarios are based on the strengths and weaknesses, in terms of the driving task, for both the human driver and automation. Finally, scenarios as criteria for exclusion are being proposed in order to systemically assess the contribution of the human driver and automation to road safety.

Time Headway in Car following and Operational Performance during Unexpected Braking

1997 ◽  
Vol 84 (3_suppl) ◽  
pp. 1247-1257 ◽  
Author(s):  
Wim Van Winsum ◽  
Wiebo Brouwer
Keyword(s):  
Driving Simulator ◽  
Theoretical Perspective ◽  
Open Loop ◽  
Braking Performance ◽  
Individual Level ◽  
Car Following ◽  
Time Headway ◽  
Motor Component ◽  
Visual Motor ◽  
The Individual

The relation between car-following behaviour and braking performance was studied in a driving simulator. The theoretical perspective was that individual differences in tactical car-driving behaviour may be related to skills on the operational level of the driving task via a process of adaptation. In a sample of 16 young and middle-aged experienced drivers independent assessments were made of preferred time headway during car following and of braking skill. Starting from modern theories of visual-motor learning, braking performance was analyzed in terms of a reaction time component, an open-loop visual-motor component, and a closed-loop visual-motor component involving the precise adjustment of braking (timing and force) to the situation. The efficiency of the visual-motor component of braking was a strong and significant predictor of choice of time headway to the lead vehicle in such a way that less efficient braking indicated a preference for a longer time headway. This result supports the theory of adaptation on the individual level.

scholarly journals Comparing the Effect of Age, Gender, and Desired Speed on Car-Following Behavior by Using Driving Simulator

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Ehsan Ramezani-Khansari ◽  
Masoud Tabibi ◽  
Fereidoon Moghadas Nejad ◽  
Mahmoud Mesbah
Keyword(s):  
Genetic Algorithm ◽  
Steady State ◽  
Driving Simulator ◽  
General Motors ◽  
Car Following ◽  
Time Headway ◽  
Age Factor ◽  
And Gender ◽  
Gender Factor ◽  
Effect Of Age

In this study, the effect of age, gender, and desired speed (DS) factors on General Motors car-following (CF) behavior was investigated. DS was defined as the speed selected by the driver in free driving situation. A low-level driving simulator was used to collect data. The CF model for each driver was calibrated by genetic algorithm. Gender and DS were effective in CF behavior, while the age factor was not. The drivers’ sensitivity to the variables of speed and distance in the CF model increased with increasing the DS. The gender factor affected only the magnitude of deceleration which was higher in women. For further investigation, the effect of the desired speed on the time headway in the steady-state CF was also examined. DS factor was effective in steady-state CF behavior. As the DS increased, the time headway decreased. Examining CF threshold demonstrated that women maintained larger distance than men. Finally, it can be said that DS and gender would be more important than age to be considered in CF models.

scholarly journals Impact of Age on Takeover Behavior in Automated Driving in Complex Traffic Situations: A Case Study of Beijing, China

2022 ◽  
Vol 14 (1) ◽  
pp. 483
Author(s):  
Jianguo Gong ◽  
Xiucheng Guo ◽  
Lingfeng Pan ◽  
Cong Qi ◽  
Ying Wang
Keyword(s):  
Driving Simulator ◽  
Age Groups ◽  
Policy Implications ◽  
Driving Ability ◽  
Automated Driving ◽  
Driving Conditions ◽  
Lateral Offset ◽  
Vehicle Automation ◽  
Driving Stability ◽  
Beijing China

Research on the influence of age on various automated driving conditions will contribute to an understanding of driving behavior characteristics and the development of specific automated driving systems. This study aims to analyze the relationship between age and takeover behavior in automated driving, where 16 test conditions were taken into consideration, including two driving tasks, two warning times and four driving scenarios. Forty-two drivers in Beijing, China in 2020 were recruited to participate in a static driving simulator with Level 3 (L3) conditional automation to obtain detailed test information of the recorded takeover time, mean speed and mean lateral offset. An ANOVA test was proposed to examine the significance among different age groups and conditions. The results confirmed that reaction time increased significantly with age and the driving stability of the older group was worse than the young and middle groups. It was also indicated that the older group could not adapt to complex tasks well when driving due to their limited cognitive driving ability. Additionally, the higher urgency of a scenario explained the variance in the takeover quality. According to the obtained influencing mechanisms, policy implications for the development of vehicle automation, considering the various driving behaviors of drivers, were put forward, so as to correctly identify the high-risk driving conditions in different age groups. For further research, on-road validation will be necessary in order to check for driving simulation-related effects.

scholarly journals The Effect of Takeover Lead Time on Driver Workload

2019 ◽  
Vol 63 (1) ◽  
pp. 1872-1873
Author(s):  
Yongdeok Yun ◽  
Hyungseok Oh ◽  
Rohae Myung
Keyword(s):  
False Alarm ◽  
Lead Time ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Physiological Signals ◽  
Sufficient Time ◽  
Subjective Workload ◽  
Driver Workload ◽  
Level 3 ◽  
Driving Task

Autonomous driving has been one of the most interesting technologies in recent years with expectation of solving accidents, pollution, and traffic jams (Jo, Lee, & Kim, 2013; Schrank, Eisele, & Lomax, 2012; Singh, 2018). However, current autonomous vehicles cannot handle all driving situations. Therefore, drivers must intervene in certain situations. SAE international defined these levels of autonomous driving as partial (level 2) and conditional (level 3) autonomous driving (SAE international, 2016). In level 3 autonomous driving, drivers are not required to monitor the driving situations and they may perform non-driving related tasks (NDRTs). However, drivers must pay attention to driving situations and make an appropriate reaction when takeover request (TOR) occurs. Takeover request (TOR) is one of the major issues in autonomous driving. A human driver must be ready to transfer the control of the vehicle when TOR is given. Therefore, how and when to request the driver to transfer the control is important. In this regard, takeover lead time (TORlt) has received great attention and there are many existing scholarly works on the effect of TORlt on takeover performance (Gold et al., 2013; Gold et al., 2017; Mok, Johns, Lee, Ive et al., 2015; Mok Johns Lee, Miller et al., 2015; Payre et al., 2016; Van den Beukel & Van der Voort, 2013; Wan & Wu, 2018; Zhang et al., 2018). Besides its impact on takeover performance, TORlt also has an effect on driver workload (Eriksson & Stanton, 2017; Wan & Wu, 2018). Inappropriate TORlt makes driver overload or underload and the abnormal workload deteriorates driver performance in takeover situation (De Winter et al., 2014; Eriksson & Stanton, 2017; Hajek et al., 2013; Wan & Wu, 2018). However, these studies either did not investigate workload induced by TOR or measure driver workload in a subjective method. This study focused on workload induced by TOR. Wan & Wu (2018) stated that takeover request without sufficient time budget may increase driver workload and generate erratic driver's response. However, many researches have focused on workload while performing NDRT alone. Additionally, a few research that assessed workload induced by TOR used subjective methods. The objective of this study is to investigate the effects of TORlt on driver workload in takeover situation. This study hypothesizes that workload would increase when TORlt is too short or too long. To investigate the hypothesis, an experiment was conducted with driving simulator and workload was measured by subjective and objective methods. The experiment with driving simulator was conducted with 28 participants to investigate the effect of TORlt on the driver workload. TORlt was controlled in 7 levels (3s, 7s, 10s, 15s, 30s, 45s, 60s). Each session of the experiment was dealt with one TORlt level and was conducted in random sequence. At the beginning of the session, participants had to perform the NDRT during autonomous driving. Then, they are required to identify an obstacle in ego-lane and make a lane change to avoid the collision when TOR occurs. The dependent variables in this experiment include workload measured by subjective and physiological methods. Driving Activity Load Index (DALI; Pauzie, 2008) was conducted to measure subjective workload and physiological measures including respiration rate (RSP), heart rate (HR), and galvanic skin reponse (GSR) were conducted to evaluate objective workload. The results of this study showed that TORlt had a significant effect on subjective workload. Subjective workload was increased in short TORlt as expected. Drivers, who were given the TOR with short lead time, did not have sufficient time to perceive and comprehend the driving situation nor make an appropriate decision. As a result, drivers could not cope with the takeover situation and their workload increased. However, driver workload was not increased in excessively long TORlt. Long TORlt was expected to increase driver workload because driver could assume long TORlt to be a false alarm or feel distraction (Wan & Wu, 2018). This might be because participants did not consider that 60s of TORlt was long or there was no false alarm in the experiment. There was no significant effect of TORlt on mean RSP and mean HR. This is because each participant behaved differently or regarded driving situation after the takeover as a simple driving task. In contrast to RSP and HR, TORlt had a significant effect on mean GSR. According to Kramer (1991), Physiological signals are sensitive to different resource demands (Kramer, 1991; Ryu & Myung, 2005). In this study, excessive temporal demand because of short TORlt and distraction caused by long TORlt were demands imposed on the participants. Hence, GSR which is sensitive to emotion and frustration (Kramer, 1991) was influenced by TORlt. In conclusion, the results of the study were different from the hypothesis which expected excessive workload with too short or long TORlt. Even though subjective workload and GSR partially support the hypothesis, more complicated and controlled experiment is needed to confirm the hypothesis. In the future research, experiment including complex driving task and false alarm should be conducted and it is necessary to measure physiological signals while controlling various resource demand to investigate the effect of TORlt on physiological signals.

scholarly journals Mobile Phone Use in a Car-Following Situation: Impact on Time Headway and Effectiveness of Driver’s Rear-End Risk Compensation Behavior via a Driving Simulator Study

2020 ◽  
Vol 17 (4) ◽  
pp. 1328 ◽  
Author(s):  
Yunxing Chen ◽  
Rui Fu ◽  
Qingjin Xu ◽  
Wei Yuan
Keyword(s):  
Regression Model ◽  
Mobile Phone ◽  
Driving Simulator ◽  
Accident Risk ◽  
Mobile Phone Use ◽  
Car Following ◽  
Time Headway ◽  
Accident Probability ◽  
Compensation Behavior ◽  
The Relationship

Mobile phone use while driving has become one of the leading causes of traffic accidents and poses a significant threat to public health. This study investigated the impact of speech-based texting and handheld texting (two difficulty levels in each task) on car-following performance in terms of time headway and collision avoidance capability; and further examined the relationship between time headway increase strategy and the corresponding accident frequency. Fifty-three participants completed the car-following experiment in a driving simulator. A Generalized Estimating Equation method was applied to develop the linear regression model for time headway and the binary logistic regression model for accident probability. The results of the model for time headway indicated that drivers adopted compensation behavior to offset the increased workload by increasing their time headway by 0.41 and 0.59 s while conducting speech-based texting and handheld texting, respectively. The model results for the rear-end accident probability showed that the accident probability increased by 2.34 and 3.56 times, respectively, during the use of speech-based texting and handheld texting tasks. Additionally, the greater the deceleration of the lead vehicle, the higher the probability of a rear-end accident. Further, the relationship between time headway increase patterns and the corresponding accident frequencies showed that all drivers’ compensation behaviors were different, and only a few drivers increased their time headway by 60% or more, which could completely offset the increased accident risk associated with mobile phone distraction. The findings provide a theoretical reference for the formulation of traffic regulations related to mobile phone use, driver safety education programs, and road safety public awareness campaigns. Moreover, the developed accident risk models may contribute to the development of a driving safety warning system.

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