Key Takeaways & Executive Findings
- •• Airflow and surround sound peripheral interactions significantly improve drivers' situation awareness in L3 automated driving. • The integration of airflow and surround sound yields the best SA improvement, especially during hard non-driving-related tasks. • Peripheral interaction methods reduce subjective workload and enhance user experience. • Findings provide design insights for in-vehicle systems that balance safety and NDRT efficiency.
Abstract
L3 automated driving has introduced a trend of drivers engaging in non-driving-related tasks (NDRTs), but it also poses safety challenges for reconstructing drivers’ situation awareness (SA). Two consecutive empirical studies in a driving simulator were conducted to investigate the effect of two peripheral interactions (airflow conveying the intended behaviors of vehicles and surround sound conveying the information of road users) on drivers’ SA performance, NDRT efficiency, workload, and user experience. The first study (n=21) explored the differential effects of airflow, surround sound, and their integration. The second study (n=30) investigated how the integrated interaction performed across different NDRT difficulties. Results demonstrated that airflow and surround sound could significantly improve drivers’ SA when used individually, each having distinct advantages. The integration of these two interactions yielded the best results. Notably, the integrated interaction showed greater effectiveness in improving SA during hard NDRT compared to the easy one. Furthermore, drivers reported reduced subjective workloads and enhanced user experience when leveraging these peripheral interaction methods. Our work offers insights for designing in-vehicle interaction systems that not only reconstruct drivers’ SA but also support NDRT participation, ensuring safety and productivity.
1. Introduction
Automated driving technology is transforming the driving experience. SAE (2021) classified automated driving into six levels (L0–L5), where each level has special requirements on drivers’ situation awareness (SA). L2 automated driving systems maintain drivers’ SA and support their monitoring of driving, thus keeping drivers vigilant and ready to intervene when necessary (National Transportation Safety Board, 2018; DeGuzman and Donmez, 2024). L3 automated driving has introduced a new trend in which drivers engage in non-driving-related tasks (NDRTs), offering a productive use of travel time (Wörle and Metz, 2020; SAE, 2021). However, it also introduces new challenges between safety and drivers’ productivity. This shift to NDRT engagement in L3 systems results in a decrease in SA and an increased workload of drivers (Zangi et al., 2022). Drivers need to disengage from NDRT and “reconstruct” SA to take control when facing situations that exceed the automated vehicle’s operational design domain (Clark et al., 2017; Chen et al., 2024). This is risky and diminishes the NDRT efficiency. Therefore, how to support drivers in reconstructing SA while maintaining NDRT efficiency in L3 automated driving systems has become a critical issue.
SA is a significant safety factor for performing complex tasks, such as aircraft control and driving (Endsley, 1995, 2001). Endsley’s model structured SA into three hierarchical levels: (1) Level 1—perception of the elements in the environment; (2) Level 2—comprehension of the current situation; (3) Level 3—projection of future status in the dynamic environment. In each level of SA for automated driving, two types of elements are considered: road users (e.g., other vehicles, pedestrians, and cyclists) and the state of the automated vehicle (e.g., operational modes and intended behaviors such as lane changing and braking) (Endsley, 2020). Road users are characterized by their direction and relative speed (Xing et al., 2017; Li et al., 2019; Löcken et al., 2020), which supports the perception level of SA. A higher SA level requires dynamic information, such as the trajectory and changing distance, which is absent from most studies. Researchers have begun to address this gap by using signals such as red arrows to indicate the forward trajectories of nearby vehicles and pedestrians (Naujoks and Neukum, 2014; Yang Z et al., 2019), and to communicate the information of blind-spot road users (Xing et al., 2017). Supplementing the information on the vehicle’s intended behavior enriches situational information and may improve drivers’ SA. However, recent research that exhibits behaviors such as braking, steering, and turning overlooks their continuous and dynamic nature by using a fixed form, such as a 0.5 Hz pulse light emitting diode (LED) for turning (Yang YC et al., ...).
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Hanfei ZHU, Wei XIANG, Yifu ZHANG, Ziyue LEI, Lingyun SUN (2025). Leveraging peripheral interactions to improve drivers’ situation awareness and NDRT efficiency. Engineering Information Technology & Electronic Engineering. https://doi.org/10.1631/ENG_ITEE_2025_0159
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Frequently Asked Questions
What are peripheral interactions in automated driving?
Peripheral interactions are non-visual or ambient feedback modalities, such as airflow and surround sound, that convey information about the vehicle's intended behaviors and surrounding road users without requiring the driver's full attention, helping to maintain or reconstruct situation awareness.
How do airflow and surround sound improve driver situation awareness?
Airflow conveys the intended behaviors of the automated vehicle (e.g., lane changes, braking), while surround sound provides information about other road users (e.g., position, movement). Both modalities significantly improve drivers' situation awareness when used individually, and their integration yields the best performance.
Does the integrated peripheral interaction work better for difficult non-driving-related tasks?
Yes. The second study (n=30) showed that the integrated airflow and surround sound interaction was more effective in improving situation awareness during hard non-driving-related tasks compared to easy ones, while also supporting NDRT efficiency.
What are the benefits of peripheral interactions for driver workload and experience?
Drivers reported reduced subjective workload and enhanced user experience when leveraging these peripheral interaction methods, suggesting that well-designed ambient feedback can ease the cognitive burden and improve acceptance.
What are the design implications of this research?
The findings provide insights for designing in-vehicle interaction systems that reconstruct drivers' situation awareness and support NDRT participation, ensuring both safety and productivity in L3 automated driving.
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