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Effects of different takeover request interfaces on takeover behavior and performance during conditionally automated
Yang-Kun Ou1, Wan-Xuan Huang1, Chen-Wen Fang2
1Department of Creative Product Design, Southern Taiwan University of Science and Technology, Tainan, Taiwan.
Accident; Analysis and Prevention
|October 3, 2021
Summary
Drivers can transition more efficiently from automated driving to manual control with advanced prediction interfaces and directional takeover alerts. This improves safety and comfort in conditionally automated driving systems.
Area of Science:
- Human-computer interaction
- Automotive engineering
- Cognitive psychology
Background:
- Conditionally automated driving (CAD) systems are emerging, requiring drivers to manage non-driving related tasks (NDRTs) and perform timely takeovers.
- Driver distraction during NDRTs can impair the ability to respond to takeover requests (TORs), impacting safety in automated vehicles.
- Efficient transitions between automated and manual driving are crucial for driver comfort and security.
Purpose of the Study:
- To investigate methods for enabling drivers immersed in NDRTs to quickly detect TORs and regain control.
- To evaluate the impact of different vehicle display interfaces and TOR information loads on driver takeover performance.
- To analyze driver behavior, performance, and subjective perceptions during the takeover process from NDRT immersion.
Main Methods:
- A 3x2x2 experimental design examined interface information load, TOR information load, and NDRT immersion levels.
- 48 participants performed smartphone-related NDRTs while responding to various TOR prompts on automotive displays.
- Key metrics included takeover behavior, performance efficiency, and subjective driver perceptions.
Main Results:
- The advanced prediction interface facilitated a more efficient takeover process compared to basic or prediction interfaces.
- Directional information notifications led to faster and more accurate takeovers across all interface types and NDRT immersion levels.
- Driver immersion in NDRTs was a significant factor influencing takeover efficiency.
Conclusions:
- Advanced prediction interfaces and directional TOR notifications enhance driver takeover efficiency in CAD systems.
- Optimizing interface design and information delivery is critical for safe transitions from automated to manual driving.
- Future research should focus on minimizing driver distraction and maximizing situational awareness during automated driving.
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