PMID- 33054370
OWN - NLM
STAT- MEDLINE
DCOM- 20220527
LR  - 20220628
IS  - 1547-8181 (Electronic)
IS  - 0018-7208 (Linking)
VI  - 64
IP  - 4
DP  - 2022 Jun
TI  - Evaluating Driver Features for Cognitive Distraction Detection and Validation in 
      Manual and Level 2 Automated Driving.
PG  - 746-759
LID - 10.1177/0018720820964149 [doi]
AB  - OBJECTIVE: This study aimed to investigate the impacts of feature selection on 
      driver cognitive distraction (CD) detection and validation in real-world 
      nonautomated and Level 2 automated driving scenarios. BACKGROUND: Real-time 
      driver state monitoring is critical to promote road user safety. METHOD: 
      Twenty-four participants were recruited to drive a Tesla Model S in manual and 
      Autopilot modes on the highway while engaging in the N-back task. In each driving 
      mode, CD was classified by the random forest algorithm built on three 
      "hand-crafted" glance features (i.e., percent road center [PRC], the standard 
      deviation of gaze pitch, and yaw angles), or through a large number of features 
      that were transformed from the output of a driver monitoring system (DMS) and 
      other sensing systems. RESULTS: In manual driving, the small set of glance 
      features was as effective as the large set of machine-generated features in terms 
      of classification accuracy. Whereas in Level 2 automated driving, both glance and 
      vehicle features were less sensitive to CD. The glance features also revealed 
      that the misclassified driver state was the result of the dynamic fluctuations 
      and individual differences of cognitive loads under CD. CONCLUSION: Glance 
      metrics are critical for the detection and validation of CD in on-road driving. 
      APPLICATIONS: The paper suggests the practical value of human factors domain 
      knowledge in feature selection and ground truth validation for the development of 
      driver monitoring technologies.
FAU - Yang, Shiyan
AU  - Yang S
AUID- ORCID: 0000-0002-2402-8126
AD  - 557108 Seeing Machines, Canberra, ACT, Australia.
FAU - Wilson, Kyle M
AU  - Wilson KM
AD  - 557108 Seeing Machines, Canberra, ACT, Australia.
FAU - Roady, Trey
AU  - Roady T
AUID- ORCID: 0000-0002-0945-1321
AD  - 557108 Seeing Machines, Canberra, ACT, Australia.
FAU - Kuo, Jonny
AU  - Kuo J
AD  - 557108 Seeing Machines, Canberra, ACT, Australia.
FAU - Lenne, Michael G
AU  - Lenne MG
AD  - 557108 Seeing Machines, Canberra, ACT, Australia.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20201015
PL  - United States
TA  - Hum Factors
JT  - Human factors
JID - 0374660
SB  - IM
MH  - Accidents, Traffic
MH  - Algorithms
MH  - *Automobile Driving/psychology
MH  - Cognition
MH  - *Distracted Driving
MH  - Humans
OTO - NOTNLM
OT  - automated driving
OT  - cognitive distraction
OT  - driver state monitoring
OT  - feature selection
OT  - ground truth validation
EDAT- 2020/10/16 06:00
MHDA- 2022/05/28 06:00
CRDT- 2020/10/15 17:06
PHST- 2020/10/16 06:00 [pubmed]
PHST- 2022/05/28 06:00 [medline]
PHST- 2020/10/15 17:06 [entrez]
AID - 10.1177/0018720820964149 [doi]
PST - ppublish
SO  - Hum Factors. 2022 Jun;64(4):746-759. doi: 10.1177/0018720820964149. Epub 2020 Oct 
      15.