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The relative importance of real-time in-cab and external feedback in managing fatigue in real-world commercial transport operationsMichael Fitzharris, Sara Liu, Amanda N. StephensMUARCMichael G. Lenné,

Seeing Machines

ACCIDENT RESEARCH CENTRE

25th International Technical Conference on Enhanced Safety of Vehicles (ESV), Detroit, 5 – 9th June 2017

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Fatigue, its measurement and risk Driver fatigue and its measurement has been the subject of

substantial research. The increased crash risk associated with driver fatigue is

well established. Naturalistic driving studies have overcome some of the key

limitations in past self-report-based research, but also require significant post-processing of data to identify events.

Driver monitoring systems (DMS) represent a way to objectively assess ‘driver-state’ – and intervene, in real-time.

For fatigue, eye-closure metrics (PERCLOS, see Dinges et al., 1998) and other inputs (i.e., head position) are the basis of some systems.

MONASHUNIVERSITYACCIDENT RESEARCH CENTRE

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A role for DMS in commercial vehicles Commercial vehicle sector is critical to the economy, and

this includes transport of freight and people.

Relatively long operating hours and operational pressure interact to influence driver ‘state’ (i.e., fatigue, distraction).

Fatigue-related crashes in this sector are well documented:– Commercial vehicle drivers who were fatigued or fell asleep were 21

times more likely to be involved in a fatal crash (Bunn et al., 2005);

– Analysis of drowsiness (PERCLOS) shows greater drowsiness levels for drivers found at-fault for safety-related driving events (Hanowskiet al., 2003);

– Drivers are poor at predicting sleep onset (Kaplan et al., 2007).

DMS offer a way to address these risks objectively and in real-time


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Study AimWith the DMS permitting the capture of fatigue events in real-time, this paper seek to address three aims:

1. Document the incidence of driver fatigue in real-world operations in a large scale commercial vehicle company;

2. Assess whether provision of driver warnings in ‘real-time’ is associated with a reduction in the incidence of fatigue events, and well as changing their timing and duration, and

3. Determine whether external event monitoring (and company notification) is associated with a further reduction in fatigue events.


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Method: Driver Monitoring System Driver-facing camera is on the vehicle

dashboard pointing toward the driver.

Machine-learned classifiers based on eyelid opening, shape,& head pose, with 1.5 s threshold.

Direct feedback to driver (auditory, haptic) + / - to company.

Fatigue events uploaded to Monitoring Centre (AZ, USA) in a 3 second clip.

Expert uses range information and classifies each event:– Controlled eye closure (fatigue mitigation);– Microsleep with head roll / stable head, and– Other eye closure (drowsiness). Source: Seeing Machines

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Method: Analytical design Retrospective analysis of de-identified real-time driver fatigue

event data.

Short, medium, long-haul freight company (Australia).

Data collected from 2011 – 2015:– 342 vehicles over 1.1 million operational hours;– Trucks covered 45.9 million miles (73.9 million kilometres).

Driver Alarm +

Monitoring / Feedback

to companyFeb 2014 –Dec 2015

Driver Alarm(audio + haptic)

Nov 2011 –Jan 2014

Baseline (silent)

Jul-Nov/2011


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Reduction of fatigue events Significant reduction in percent of trips with fatigue events (%,

distance, time) & fewer trips with multiple events.

Alarm + Feedback

0.18%(of 530,075

trips)

Driver alarm 1.3%

(of 161,541 trips)

Baseline (silent)3.7%

(of 4539 trips)

11.2% trips > 3 events

2.37 /1000 mi56 / 1000 hrs


0.49 /1000 mi18 / 1000 hrs


0.03 /1000 mi1.3 / 1000 hrs

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Significant reduction in event duration from baseline. *p≤0.001

Distance covered and vehicle speed in ‘fatigue event state’ translates to a significant road safety problem.

Alarm + Feedback

2.35s*(5%trim: 1.97)

(Median: 1.85)*[1.4 – 59.3s]

Driver alarm 2.30s*

(5%trim: 2.11)(Median: 1.93)*

[1.6 – 48.5s]

Baseline (silent)2.38s

(5%trim: 2.36)(Median: 2.03)*[1.6s – 11.4s]

Duration of fatigue events

52.1 km/h36.6 metres



Robust SE regression ANOVA model for mean statistics; Quantile regression used for medians

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Statistical modelling of incidence rates Significant reduction in the incidence* of fatigue events,

adjusted for hours and distance travelled.

Driver alarm +

Feedback94.4%↓ (hrs)

(28% absolute, 84% relative)

Driver alarm

66.2% ↓ (hrs)

(-)

Baseline (silent)

* Incidence rates and differences determined using a random-effects negative binomial regression (with beta effect) controlling for trip distance / hours, and repeated trips undertaken by each truck (using truck identifier); reductions p≤0.001


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Statistical modelling of time-to-event Fatigue events occurred significantly sooner in baseline, or

alternatively, later into trips with each next level of feedback.

Alarm + Feedback

713th

minute*95%CI:712-715

Driver alarm 610th

minute*95%CI: 613-635

Baseline

510th

minute95%CI: 484-535

* Note: Kaplan Meier non-parametric survival curves using the Log Rank test to compare the equality of fatigue event occurrence across the three periods; p≤0.001,


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Probability of fatigue free, by time The longer the drive, the more likely drivers experience

fatigue event, but full intervention is highly ‘protective’.

probability of having (not) fatigue event was: 20% (80%)10% (90%)1.5% (98.5%)

After 7 hrs of driving (420 minutes)

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Key findings Real-time driver monitoring of driver fatigue resulted in:

– 94% reduction in the incidence of fatigue events;– When they did occur, the fatigue events were shorter in duration

and occurred much later into the trip, and– Direct feedback to the driver alone results in a 66% reduction.

Near total elimination was achieved only with additional direct feedback to the commercial vehicle company.

Company safety culture is critical in using information in fatigue management plans:– OHS considerations (schedules, work-hour regulations, training), &– Programs to aid the driver (health assessments / coaching).

Objective DMS data gave the driver and the company the ability to address key risks.


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Limitations Analysis was ‘truck-based’:

– Current work is focussed on using driver identifiers to account for driver specific behaviours.

Progressive rollout in fleet but reflects operational aspects.

Replication into other fleets remains to be tested.

Research did not examine false positive rate of the DMS as focus was on efficacy of feedback modes:– FP could influence driver behaviour and precipitate lifestyle changes;

– FP could influence driver acceptability of the DMS;

– Being examined in simulator and on-road in Advanced Safe Truck Concept (ATSC) in joint collaboration between Seeing Machines, MUARC, Ron Finemore Trucks) funded as CRC-P (Aust. Gov).


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Future opportunities for DMS research

1. What is the optimal way for drivers to be warned when safety critical events are detected (i.e., visual, auditory, haptic), given other in-vehicle systems?

2. How can driver oversight be achieved in non-fleet vehicles?

3. What is the link to in-cab behaviour and driver-vehicle safety in the external traffic environment?

4. Following (4), how can DMS be fully integrated with other ADAS and vehicle control functions for full benefit as we move to autonomous vehicles?


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Acknowledgments

The authors thank Francis Cremen, Seeing Machines, for data extraction from the SM Global Database

This work was funded by Seeing Machines as a research contract with MUARC

Full peer review paper available at: Traffic Injury Prevention, Volume 18, 2017 - Issue sup1: Peer-Reviewed Journal for the 25th International Technical Conference on the Enhanced Safety of Vehicles (ESV)


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