English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Safety has become one of the primary concerns of level 3 automated driving, especially during the takeover process. Since most studies have focused on impacts of various factors on takeover performance of drivers, there seems to be a gap between the causes of crashes and the desired means to mitigate their occurrence and consequences. Hence, the main objective of this study is to extract from crash data during takeovers drivers’ patterns of gaze behaviors and maneuvers and then utilize them to extract some guidance on human-machine-interface design to enhance safety and acceptability of automated driving. A study involving 27 subjects was conducted on a high-fidelity driving simulator with a Steward motion platform of six degrees of freedom. Each subject participated in 6 takeover scenarios with a lead time of 5 s and different duration of monitoring (DoM), with their maneuvers recorded by the system and eye gazes recorded by the Smart Eye Pro and Smart Recorder. Crash data collected during the takeover process were then utilized for the analysis. From 132 valid takeovers collected from 23 out of the 27 participants, 15 crashes were recorded. Based on which, five typical patterns of unsafe behaviors were recognized that may have caused the crashes, denoted as Type I to Type V, respectively. Besides, it appears that even if drivers were given more time to observe the surroundings, i.e., longer DoM, the number of crashes has not decreased as anticipated. Therefore, what is more important seems to be drivers’ gaze behaviors and maneuvers shortly after TOR. For takeovers to be safe, good cooperations between drivers’ gaze behaviors and maneuvers are essential. Overall, it seems that in emergent situations that require takeovers, some drivers have difficulty in allocating attentions reasonably, which appears to have less to do with the time left for drivers to observe the surroundings. While designing HMIs, we may as well consider providing enough information to guide drivers according to drivers’ states and maneuvers at the time to improve safety of takeovers in emergent situations, and more importantly, to provide the information timely and effectively.

Where drivers are looking at during takeover: Implications for safe takeovers during conditionally automated driving