PMID- 37732402
OWN - NLM
STAT- Publisher
LR  - 20230921
IS  - 1547-8181 (Electronic)
IS  - 0018-7208 (Linking)
DP  - 2023 Sep 21
TI  - Exploratory Development of Algorithms for Determining Driver Attention Status.
PG  - 187208231198932
LID - 10.1177/00187208231198932 [doi]
AB  - OBJECTIVE: Varying driver distraction algorithms were developed using vehicle 
      kinematics and driver gaze data obtained from a camera-based driver monitoring 
      system (DMS). BACKGROUND: Distracted driving characteristics can be difficult to 
      accurately detect due to wide variation in driver behavior across driving 
      environments. The growing availability of information about drivers and their 
      involvement in the driving task increases the opportunity for accurately 
      recognizing attention state. METHOD: A baseline for driver distraction levels was 
      developed using a video feed of 24 separate drivers in varying naturalistic 
      driving conditions. This initial assessment was used to develop four buffer-based 
      algorithms that aimed to determine a driver's real-time attentiveness, via a 
      variety of metrics and combinations thereof. RESULTS: Of those tested, the 
      optimal algorithm included ungrouped glance locations and speed. Notably, as an 
      algorithm's performance of detecting very distracted drivers improved, its 
      accuracy for correctly identifying attentive drivers decreased. CONCLUSION: At a 
      minimum, drivers' gaze position and vehicle speed should be included when 
      designing driver distraction algorithms to delineate between glance patterns 
      observed at high and low speeds. Distraction algorithms should be designed with 
      an understanding of their limitations, including instances in which they may fail 
      to detect distracted drivers, or falsely notify attentive drivers. APPLICATION: 
      This research adds to the body of knowledge related to driver distraction and 
      contributes to available methods to potentially address and reduce occurrences. 
      Machine learning algorithms can build on the data elements discussed to increase 
      distraction detection accuracy using robust artificial intelligence.
FAU - Herbers, Eileen
AU  - Herbers E
AUID- ORCID: 0000-0002-5055-7777
AD  - Virginia Tech Transportation Institute, Blacksburg, VA, USA. RINGGOLD: 630666
AD  - Virginia Tech, Biomedical Engineering and Mechanics, Blacksburg, VA, USA.
FAU - Miller, Marty
AU  - Miller M
AD  - Virginia Tech Transportation Institute, Blacksburg, VA, USA. RINGGOLD: 630666
FAU - Neurauter, Luke
AU  - Neurauter L
AD  - Virginia Tech Transportation Institute, Blacksburg, VA, USA. RINGGOLD: 630666
FAU - Walters, Jacob
AU  - Walters J
AD  - Virginia Tech Transportation Institute, Blacksburg, VA, USA. RINGGOLD: 630666
FAU - Glaser, Daniel
AU  - Glaser D
AD  - General Motors, Detroit, MI, USA. RINGGOLD: 2957
LA  - eng
PT  - Journal Article
DEP - 20230921
PL  - United States
TA  - Hum Factors
JT  - Human factors
JID - 0374660
SB  - IM
OTO - NOTNLM
OT  - automation
OT  - autonomous driving
OT  - cognition
OT  - distraction
OT  - distraction and interruptions
OT  - driver behavior
OT  - expert systems
OT  - eye movements
OT  - motor behavior
OT  - surface transportation
OT  - tracking
OT  - trust in automation
OT  - vehicle automation
EDAT- 2023/09/21 06:42
MHDA- 2023/09/21 06:42
CRDT- 2023/09/21 04:33
PHST- 2023/09/21 06:42 [medline]
PHST- 2023/09/21 06:42 [pubmed]
PHST- 2023/09/21 04:33 [entrez]
AID - 10.1177/00187208231198932 [doi]
PST - aheadofprint
SO  - Hum Factors. 2023 Sep 21:187208231198932. doi: 10.1177/00187208231198932.