Driver behavior and the use of automation in real-world driving

A conceptual framework for automation disengagements

A better understanding of automation disengagements can lead to improved safety and efficiency of automated systems. This study investigates the factors contributing to automation disengagements initiated by human operators and the automation itself by analyzing semi-structured interviews with 103 users of Tesla's Autopilot and FSD Beta. The factors leading to automation disengagements are represented by categories. In total, we identified five main categories, and thirty-five subcategories. The main categories include human operator states (5), human operator's perception of the automation (17), human operator's perception of other humans (3), the automation's perception of the human operator (3), and the automation incapability in the environment (7). Human operators disengaged the automation when they anticipated failure, observed unnatural or unwanted automation behavior (e.g., erratic steering, running red lights), or believed the automation is not capable to operate safely in certain environments (e.g., inclement weather, non-standard roads). Negative experiences of human operators, such as frustration, unsafe feelings, and distrust represent some of the adverse human operate states leading to automation disengagements initiated by human operators. The automation, in turn, monitored human operators and disengaged itself if it detected insufficient vigilance or speed rule violations by human operators. Moreover, human operators can be influenced by the reactions of passengers and other road users, leading them to disengage the automation if they sensed discomfort, anger, or embarrassment due to the automation's actions. The results of the analysis are synthesized into a conceptual framework for automation disengagements, borrowing ideas from the human factor's literature and control theory. This research offers insights into the factors contributing to automation disengagements, and highlights not only the concerns of human operators but also the social aspects of this phenomenon. The findings provide information on potential edge cases of automated vehicle technology, which may help to enhance the safety and efficiency of such systems.

Constructing the behavioral sequence of the takeover process-TOR, behavior characteristics and phases division: A real vehicle experiment

Autonomous driving will still use human-machine co-driving to handle complex situations for a long term, which requires the driver to control the vehicle and avoid hazards by executing appropriate behavioral sequences after takeover prompts. Previous studies focused on the division of static behavioral indicators and major phases in the initial phase of takeover, while lacking the construction of behavioral sequences based on the dynamic changes of behavioral characteristics during the takeover process. This study divides the takeover process in a detailed manner and investigates the impact of audio types on the behavioral sequence at each phase. 20 professional drivers performed the NDRT in autonomous driving mode on real roads, and after receiving audio prompts, they took over the vehicle and performed hazard avoidance maneuvers. The results show that the behavioral characteristics could construct the behavioral sequence of different phases, with the dynamic characteristics of the takeover operation change. In addition, different types of audio prompts will affect the timing of the takeover operation and its driving performance. Choosing different audio prompts or combinations can help improve the effect of taking over the vehicle. This study helps to provide guidance on the design of human-machine interaction for behavior optimization at different phases, so that guiding the driver to take over the vehicle safely and effectively.

Driver response and recovery following automation initiated disengagement in real-world hands-free driving

OBJECTIVE

Advanced driver assistance systems are increasingly available in consumer vehicles, making the study of drivers' behavioral adaptation and the impact of automation beneficial for driving safety. Concerns over driver's being out-of-the-loop, coupled with known limitations of automation, has led research to focus on time-critical, system-initiated disengagements. This study used real-world data to assess drivers' response to, and recovery from, automation-initiated disengagements by quantifying changes in visual attention, vehicle control, and time to steady-state behaviors.

METHODS

Fourteen drivers drove for one month each a Cadillac CT6 equipped with Super Cruise (SC), a partial automation system that, when engaged, enables hands-free driving. The vehicles were instrumented with data acquisition systems recording driving kinematics, automation use, GPS, and video. The dataset included 265 SC-initiated disengagements identified across 5,514 miles driven with SC.

RESULTS

Linear quantile mixed-effects models of glance behavior indicated that following SC-initiated disengagement, the proportions of glances to the Road decreased (Q50_Before=0.91, Q50_After=0.69; Q85_Before=1.0, Q85_After=0.79), the proportions of glances to the Instrument Cluster increased (Q50_Before=0.14, Q50_After=0.25; Q85_Before=0.34, Q85_After=0.45), and mean glance duration to the Road decreased by 4.86 sec in Q85. Multinomial logistic regression mixed-models of glance distributions indicated that the number of transitions between glance locations following disengagement increased by 43% and that glances were distributed across fewer locations. When driving hands-free, take over time was significantly longer (2.4 sec) compared to when driving with at least one hand on the steering wheel (1.8 sec). Analysis of moment-to-moment distributional properties of visual attention and steering wheel control following disengagement indicated that on average it took drivers 6.1 sec to start the recovery of glance behavior to the Road and 1.5 sec for trend-stationary proportions of at least one hand on the steering wheel.

CONCLUSIONS

Automation-initiated disengagements triggered substantial changes in driver glance behavior including shorter on-road glances and frequent transitions between Road and Instrument Cluster glance locations. This information seeking behavior may capture drivers' search for information related to the disengagement or the automation state and is likely shaped by the automation design. The study findings can inform the design of more effective driver-centric information displays for smoother transitions and faster recovery.

Do driver's characteristics, system performance, perceived safety, and trust influence how drivers use partial automation? A structural equation modelling analysis

The present study surveyed actual extensive users of SAE Level 2 partially automated cars to investigate how driver’s characteristics (i.e., socio-demographics, driving experience, personality), system performance, perceived safety, and trust in partial automation influence use of partial automation. 81% of respondents stated that they use their automated car with speed (ACC) and steering assist (LKA) at least 1–2 times a week, and 84 and 92% activate LKA and ACC at least occasionally. Respondents positively rated the performance of Adaptive Cruise Control (ACC) and Lane Keeping Assistance (LKA). ACC was rated higher than LKA and detection of lead vehicles and lane markings was rated higher than smooth control for ACC and LKA, respectively. Respondents reported to primarily disengage (i.e., turn off) partial automation due to a lack of trust in the system and when driving is fun. They rarely disengaged the system when they noticed they become bored or sleepy. Structural equation modelling revealed that trust had a positive effect on driver’s propensity for secondary task engagement during partially automated driving, while the effect of perceived safety was not significant. Regarding driver’s characteristics, we did not find a significant effect of age on perceived safety and trust in partial automation. Neuroticism negatively correlated with perceived safety and trust, while extraversion did not impact perceived safety and trust. The remaining three personality dimensions ‘openness’, ‘conscientiousness’, and ‘agreeableness’ did not form valid and reliable scales in the confirmatory factor analysis, and could thus not be subjected to the structural equation modelling analysis. Future research should re-assess the suitability of the short 10-item scale as measure of the Big-Five personality traits, and investigate the impact on perceived safety, trust, use and use of automation.

An efficient driver behavioral pattern analysis based on fuzzy logical feature selection and classification in big data analysis

In recent years, driver behavior analysis plays a vital role to enhance passenger coverage and management resources in the smart transportation system. The real-world environment possesses the driver principles contains a lot of information like driving activities, acceleration, speed, and fuel consumption. In big data analysis, the driver pattern analyses are complex because mining information is not utilized to feature evaluations and classification. In this paper, a new efficient Fuzzy Logical-based driver behavioral pattern analysis has been proposed to offer effective recommendations to the drivers. Primarily, the feature selection can be carried out with the assist of fuzzy logical subset selection. The selected features are then evaluated using frequent pattern information and these measures will be optimized with a multilayer perception model to create behavioral weight. Afterward, the information weights are trained with a test through an optimized spectral neural network. Finally, the neurons are activated by a recurrent neural network to classify the behavioral approach for the superior recommendation. The proposed method will learn the characteristics of driving behaviors and model temporal features automatically without the need for specialized expertise in feature modelling or machine learning techniques. The simulation results manifest that the proposed framework attains better performance with 98.4% of prediction accuracy and 86.8% of precision rate as compared with existing state-of-the-art methods.

Perceived safety and trust in SAE Level 2 partially automated cars: Results from an online questionnaire

The present online study surveyed drivers of SAE Level 2 partially automated cars on automation use and attitudes towards automation. Respondents reported high levels of trust in their partially automated cars to maintain speed and distance to the car ahead (M = 4.41), and to feel safe most of the time (M = 4.22) on a scale from 1 to 5. Respondents indicated to always know when the car is in partially automated driving mode (M= 4.42), and to monitor the performance of their car most of the time (M = 4.34). A low rating was obtained for engaging in other activities while driving the partially automated car (M= 2.27). Partial automation did, however, increase reported engagement in secondary tasks that are already performed during manual driving (i.e., the proportion of respondents reporting to observe the landscape, use the phone for texting, navigation, music selection and calls, and eat during partially automated driving was higher in comparison to manual driving). Unsafe behaviour was rare with 1% of respondents indicating to rarely monitor the road, and another 1% to sleep during partially automated driving. Structural equation modeling revealed a strong, positive relationship between perceived safety and trust (β = 0.69, p = 0.001). Performance expectancy had the strongest effects on automation use, followed by driver engagement, trust, and non-driving related task engagement. Perceived safety interacted with automation use through trust. We recommend future research to evaluate the development of perceived safety and trust in time, and revisit the influence of driver engagement and non-driving related task engagement, which emerged as new constructs related to trust in partial automation.