Non-driving related tasks' effects on takeover and manual driving behavior in a real driving setting: A differentiation approach based on task switching and modality shifting

Triboelectric sensor gloves for real-time behavior identification and takeover time adjustment in conditionally automated vehicles

The takeover issue, especially the setting of the takeover time budget, is a critical factor restricting the implementation and development of conditionally automated vehicles. The general fixed takeover time budget has certain limitations, as it does not take into account the driver's non-driving behaviors. Here, we propose an intelligent takeover assistance system consisting of all-round sensing gloves, a non-driving behavior identification module, and a takeover time budget determination module. All-round sensing gloves based on triboelectric sensors seamlessly detect delicate motions of hands and interactions between hands and other objects, and then transfer the electrical signals to the non-driving behavior identification module, which achieves an accuracy of 94.72% for six non-driving behaviors. Finally, combining the identification result and its corresponding minimum takeover time budget obtained through the takeover time budget determination module, our system dynamically adjusts the takeover time budget based on the driver's current non-driving behavior, significantly improving takeover performance in terms of safety and stability. Our work presents a potential value in the application and implementation of conditionally automated vehicles.

Effects of integrated takeover request warning with personal portable device on takeover time and post-takeover performance in level 3 driving automation

INTRODUCTION

Level 3 driving automation defined by the Society of Automotive Engineers (SAE) requests human drivers to drive manually when the vehicle cannot perform the driving task. In this regard, researchers have studied the integrated takeover request (TOR) which provides visual and auditory TOR warning in both vehicle interface (e.g., dashboard, windshield (head-up display) and personal portable device (PPD) (e.g., cell phone, tablet). However, these studies neither used auditory TOR warning in PPD nor examined the effect of use of headphone on takeover. Thus, this study evaluates the effects of the integrated TOR with the use of headphones on the takeover time and the post-takeover performance.

METHOD

The behavior of 60 drivers was observed in the driving simulator experiment. During the experiment, the drivers watched a video on a tablet in automated driving, received the TOR warning, and manually drove in the lane change and pullover scenarios. The survey was also conducted to ask drivers' experience and preference for TOR warning.

RESULTS

The integrated TOR significantly reduced the takeover time compared to the conventional TOR which provides the TOR warning in vehicle interface only. The integrated TOR also improved the post-takeover performance as indicated by more stable steering operation and safer driving behavior after TOR warning. However, the use of headphones did not significantly reduce the takeover time or improve the post-takeover performance for the integrated TOR. The participants generally perceived higher subjective comfort and safety level with the integrated TOR than the conventional TOR.

CONCLUSIONS

The integrated TOR with auditory warning in PPD can significantly reduce the takeover time and improve the post-takeover performance in both urgent and less urgent conditions.

PRACTICAL APPLICATIONS

The integrated TOR with auditory warning in PPD can be applied to SAE Level 3 driving automation for safe transition from automated to manual driving.

Is driving experience all that matters? Drivers' takeover performance in conditionally automated driving

INTRODUCTION

In conditionally automated driving, drivers are allowed to engage in non-driving related tasks (NDRTs) and are occasionally requested to take over vehicle control in situations that the automation system cannot handle. Drivers may not be able to adequately perform such requests if they have limited driving experience. This study investigates the influence of driving experience on takeover performance in conditionally automated driving.

METHOD

Nineteen subjects participated in this driving simulator study. The NDRTs consisted of three tasks: writing business emails (working condition), watching videos (entertaining condition), and taking a break with eyes closed (resting condition). These three NDRTs require drivers to invest high, moderate, and low levels of mental workload, respectively. The duration of engagement in each NDRT before a takeover request (TOR) was either 5 minutes (short interval) or 30 minutes (long interval).

RESULTS

Drivers' driving experience and performance during the control period are highly correlated with their TOR performance. Furthermore, the type and duration of NDRT influence TOR performance, and inexperienced drivers exhibit poorer TOR performance than experienced drivers.

CONCLUSIONS AND PRACTICAL APPLICATIONS

These findings have relevance for the types of NDRTs that ought to be permitted during automated driving, the design of automated driving systems, and the formulation of regulations regarding the responsible use of automated vehicles.

Impact of non-driving related tasks while operating automated driving systems (ADS): A systematic review

Automated Driving Systems (ADS) (SAE, 2021), promise improved safety and comfort for drivers. Current technological advances have resulted in increased automation capabilities. However, with the increase in automation capabilities, there is a shift in how drivers interact with their vehicles. Drivers can now temporarily hand over the control of the driving task to ADS under certain conditions. However, with ADS in temporary control of the vehicle, drivers may choose to engage in non-driving related tasks (NDRT). The current capabilities of ADS do not allow drivers to hand over control of the driving task indefinitely. Drivers must remain aware and be ready to take back control if necessary. There is a need to better understand drivers' performance and behaviors when driving with ADS, especially when engaged in NDRTs. This literature review, therefore, aims to understand the state of knowledge on automated vehicle systems and driver distraction. This review was conducted as per PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies found a significant increase in takeover times while engaging in NDRTs and driving with automation active. Studies also discuss a change in driver's visual attention, with more focus given to NDRTs as compared to the front roadway. The concerning effects of increasing reaction times and decreases in visual attention can be mitigated by using interventions and studies have had success in redirecting drivers attention and reorient them to the task of driving. The review, therefore, includes a discussion of ADS and NDRT engagement and its impact on driving behaviors such as take-over times, visual attention, trust, and workload. Implications on driver safety and performance are discussed in light of this synthesis.