We are pleased to welcome you to the third Industry Partner Meeting for DRIVE S(AI)FE. This is part of our bi-monthly engagement series — a structured forum where we share progress, gather feedback, and align on next steps with the partners driving this initiative forward.
The Context
DRIVE S(AI)FE is a National Heavy Vehicle Regulator (NHVR) funded project to develop a human-centred AI system for fatigue prevention in the heavy vehicle industry. Our goal is to create technology that genuinely supports drivers and operators — not a surveillance tool, but a safety partner.
What We've Accomplished So Far
Over the past four months, we have:
The Bi-Monthly Engagement Model
Industry Partner Meetings occur every two months. This structured cadence ensures we maintain momentum, keep partners informed in real time, and create space for your input before key decisions are made. Think of these meetings as checkpoints where you can see the work in progress, ask questions, and help shape what comes next.
Industry Context & Trends to Watch
📋
Regulatory Scrutiny Post-2025
NHVR and NTI reports signal increased focus on driver fatigue as a boardroom and compliance issue. This is a window for human-centred positioning.
📹
In-Cab AI Commercialisation
Seeing Machines, Netradyne, and others are scaling. Critical risk: driver backlash if framed as surveillance rather than safety partnership.
🌐
Political & Economic Pressure
Margins and driver capacity are stretched by geopolitical tension and rising costs. Risk: fleets may see this as a “luxury” project. We must frame it as safety-critical infrastructure.
⚖️
Privacy & AI Safety Standards
Privacy Act reform + emerging AI Safety Standard raise the bar for data governance, transparent design, and operator control — our core positioning.
Section 2
Stakeholders & Industry Partners
Who is part of this initiative and how are they involved.
Lead Organisation
Opposite (Damien & Nick)
Project oversight, human-centred design lead, and central coordination across all partners. Stewards of ethics-by-design and transparency principles.
NHVR (Alexandra)
Funder and strategic partner. Sets the national safety agenda and ensures recommendations align with heavy vehicle regulation and industry needs.
Technical Partners
Murdoch (Amir)
AI architecture and system development. Leads technical design, data integration, and model development. Ensures system is scalable and robust for real-world deployment.
Monash University (Simon)
Behavioural science and evaluation. Ensures system accounts for human factors, trust dynamics, and unintended consequences. Leads impact evaluation framework.
Industry Partner Operators
Sadleirs (Danica)
Long-haul and regional freight. Provides operational context, driver feedback, and testing support for pilot validation.
Downer (Anthony)
Large-scale transport and logistics. Expertise in fleet management systems and integration pathways.
ANC Delivers (Lex)
Regional and metropolitan delivery. Diverse route and workload context. Driver-centric perspective on practical implementation.
Built Environs (Sanzid)
Construction and civil transport. Heavy vehicle context in project and event-driven work. Unique fatigue risk profile.
Additional Experts
Benny Button (Adrian)
Fatigue and fatigue risk management expertise. Ensures evidence base and methodology align with published research and industry best practice.
ReguSafe Consulting (Chris)
Regulatory and safety expertise across heavy vehicle operations. Provides guidance on compliance, chain-of-responsibility implications, and regulatory alignment.
Ben Bailey Consulting (Ben)
Industry consulting and operational safety advisory. Contributes practical perspective on implementation, adoption pathways, and operator engagement.
Section 3
Steering Committee Obligations
What we need from industry partners and how we will support you.
Bi-Monthly Engagement
Attend or send a representative to scheduled industry partner meetings. Your presence and feedback shape system direction.
Operational Context
Help us understand your fleet operations, fatigue challenges, and technology integration constraints. Real-world perspective is critical.
Pilot Support
If selected, participate in or support testing phases. We will provide training, data handling protocols, and close coordination.
Feedback on Deliverables
Review and comment on research, survey design, and system prototypes before finalisation. Your input is non-negotiable.
Stakeholder Referrals
Help us identify drivers and operators willing to participate in interviews and field testing. Early connections reduce admin burden for both of us.
Communications Support
Share social media assets and project updates within your networks. Industry awareness is critical to project success and adoption.
What We Provide
In return, we commit to:
Section 4
Project Progress & Updates
Key milestones and deliverables from Q1 2026.
Stage 1 — Planning (Complete)
Ethics Plan
Five binding commitments established: no surveillance, no disciplinary use, no performance management, full transparency on data use, and human dignity as non-negotiable principle.
Communications Plan
25-month engagement strategy finalised. Spanning social media, conferences, NHVR reporting, and stakeholder engagement. LinkedIn social media package now live.
Stakeholder Map & Governance
Distributed governance model defined: Steering Committee for strategic direction, Working Group for ethical oversight and technical coordination.
Stage 2 — Data Collection (In Progress)
Primary deliverable: Current State Report (nearing completion). Will consolidate:
Internal Pilot Methodology
Multi-modal data collection study with small internal cohort (5–10 team members) over 2–4 weeks. Data streams include:
Hardware Discussions Underway
We are in active discussions with a hardware company to provide the devices needed for the pilot. In parallel, initial focus is on 50 hours of recorded video for AI model training using existing resources, ensuring we are not dependent on any single vendor as we validate the approach.
Section 5
Internal Pilot Methodology
What we're testing, what data we're collecting, and what we'll learn.
Purpose of the Pilot
The internal pilot is not intended to produce definitive fatigue models. Instead, it will:
Participants & Duration
Participants
5–10 internal team members with mix of normal sleep patterns and natural workload variation. Voluntary participation with full informed consent.
Duration
2–4 weeks minimum to capture time-of-day variation, cumulative sleep debt, weekday differences, and variable workload patterns.
Target Scope
120–200 total driving hours across cohort. 20–40 trips per participant, 15–25 hours per person for reliable variation analysis.
Data Streams
Subjective Fatigue (Ground Truth)
Pre- and post-trip ratings using Karolinska Sleepiness Scale (KSS). Sleep duration, quality, work hours, and caffeine intake also recorded.
Biometric Data
Heart rate, Heart Rate Variability (HRV), and sleep data from wearables. HRV is particularly promising for fatigue and stress detection.
Visual & Behavioural
On-board video analysis: blink rate, blink duration, PERCLOS, head angle, postural drift, fidgeting frequency.
Trip Telemetry
Speed variability, harsh braking, acceleration smoothness, lane variability. Smartphone accelerometer can supplement formal telematics.
Environmental Context
Time of day, weather, traffic density, route type, light conditions. Prevents false attribution of signals.
Hardware Partner
In Active Discussions
We are currently in talks with a hardware company to provide the devices that will underpin the pilot. We'll share more detail with partners once the arrangement is confirmed. Our priority is ensuring the selected hardware meets the data quality, driver acceptability, and integration requirements set out in this methodology.
Three-Phase Pilot Structure
01
Phase 1
Setup & Calibration
1 week — April 2026
Wearable pairing, camera install, data pipeline validation, digital survey forms. Sensors verified for timestamp accuracy and export integrity.
02
Phase 2
Data Collection
2–4 weeks — Apr–May 2026
Daily driving sessions with pre/post fatigue ratings. Weekly check-in interviews. Continuous sensor validation to prevent dropouts.
03
Phase 3
Exploratory Analysis
Ongoing — June 2026
Within-subject and cross-participant fatigue analysis, signal robustness assessment. Outputs: feasibility report, signal trends, Stage 3 recs.
Beyond the Internal Pilot
Jul–Aug
Field Pilot Preparation
Refine hardware selection, integration architecture, and fleet recruitment materials.
Sep–Nov
Fleet Testing
Structured multi-site testing across diverse operators and route conditions.
Dec
Synthesis & Model
Integrated analysis, predictive model design, product roadmap recommendations.
Section 6
Fleet Operator Engagement Roadmap
How we'll approach engagement with operators and what we need from you.
Engagement Journey — From Pilot to Field
Apr–May
Requirements Snapshot
Concise summary of what we need from partner fleets — participants, context, technical specs — shared with you.
May–Jun
Recruitment & Planning
Early contact = more support from us. We handle initial outreach, training, and coordination to reduce admin load.
Jun–Jul
Phase 1 Field Testing
Small driver cohorts begin structured testing. On-site or remote-ready support for data collection and feedback.
Aug–Sep
Phase 2 Expansion
Larger field testing, system refinement, integration with fleet management. Feeds Stage 3 design.
What We Need From You
Contact Lists
By the next meeting, we'd like driver and manager contacts willing to participate in interviews and testing. More names = better options for us to match against needs.
Operational Context
Fleet size, route types, typical shift patterns, existing tech integration, safety culture. Helps us design testing that fits your reality, not academic theory.
Feedback Loop
Regular check-ins during testing. What's working? What's creating friction? Early feedback prevents misalignment later.
Section 7
Social Media & Awareness
10 LinkedIn posts — rolling out April–May 2026.
Campaign Overview
A coordinated social media campaign amplifies the DRIVE S(AI)FE message across industry. Posts cover project context, survey launch, partnership opportunities, ethics, and behind-the-scenes insights. We invite you to share, repost, and tag us as you engage.
Post 1
Survey: How Does Fatigue Really Show Up?
Understanding how fatigue develops across real-world driving conditions — not a snapshot, but the full journey. We've launched an industry survey and we need your voice.
As AI becomes embedded in safety systems, how it's designed matters as much as what it can do. At the Melbourne Conversations' Ethics in AI event, we explored automation, governance, and trust — all central to our work.
Truly understanding fatigue requires input from those who experience it firsthand. We're seeking drivers across long-haul, delivery, construction, and high-demand operations to shape the project with us.
Fatigue rarely appears all at once. It builds gradually, often before anyone realizes it's happening. DRIVE S(AI)FE explores how earlier indicators can be identified through data, behaviour, and context.
What data shows and what people feel don't always tell the same story. Our internal pilot brings together wearables, video, operational context, and self-reported fatigue to understand what actually matters in practice.
Fatigue is shaped by time pressure, monotony, cognitive load, scheduling, and environmental context — not just sleep. Understanding these interactions across a journey is central to identifying earlier signals.
Call for Partners: Collaboration Across the System
No single organisation sees the full picture. We're seeking transport operators, technology providers, safety bodies, and researchers to co-design solutions that work in the real world.
How data is collected, used, and accessed shapes whether people feel confident engaging with safety systems. We're designing with clear boundaries around ethical collection, use, and governance.
More data doesn't always mean better outcomes. In fast-paced, resource-constrained environments, we're exploring minimum viable signals that deliver useful insights without unnecessary complexity.
Progress across this space is shaped through ongoing engagement. We welcome perspectives from drivers, operators, researchers, partners, and anyone committed to safer transport. Get involved.
Project timeline and upcoming events where your input matters.
M1
Oct 2025
Launch Project
Funding Agreement signed
Project kick-off with NHVR. Commencement Date set. Initial funding released.
M2
28 Feb 2026
Stage 1 — Planning Complete
Foundation & governance
Project planning, ethics, governance and stakeholder frameworks established. Industry Partner engagement structure confirmed.
TODAY
15 Apr 2026
Industry Partner Meeting 3
Today's workshop
Recap, obligations, methodology, and engagement alignment with all industry partners. Launch draft of Industry-Wide Survey and draft outline of Current State Report.
M3
30 Apr 2026
Stage 2 — Data Collection Begins
Internal pilot active
Collect driver behaviour, biometric, and workplace fatigue data from partners. Industry survey in field.
May 2026
National Trucking Week
Industry presence
DRIVE S(AI)FE presented to broader industry. Partner coordination welcomed.
June 2026
Industry Partner Meeting 4
Bi-monthly cadence
Current State Report, internal pilot findings, Stage 3 design direction, formal Phase 1 recruitment.
M4
30 Aug 2026
Stages 2 & 3 Complete — AI System Design
Current State + architecture
Current State Report delivered. AI architecture, data pipeline, ethics and safeguards designed.
Oct 2026
Australasian Road Safety Conference 2026
Knowledge dissemination
Present research findings, methodology and early insights.
M5
31 Jan 2027
Stage 4 — AI System Development
Algorithms & interventions built
Fatigue detection and prediction models trained. Personalised intervention system and Fatigue Risk Advisor (FRA) integrated.
M6
30 Jun 2027
Stage 5 — Co-Design + Phase 1 Testing
Driver & manager dashboards
Interfaces co-designed and tested with drivers/fleet managers. Phase 1 field trials complete.
M7
31 Oct 2027
Stage 6 — Phase 2 Testing
Real-world validation
Broader fleet trials. Evaluate AI impact on fatigue-risk reduction at individual and fleet level.
M8
30 Nov 2027
Stage 7 — Training & Toolkit
Industry operationalisation
Driver/manager training modules and industry-facing fatigue & AI implementation toolkit. Tailored materials for small fleet operators.
M9
31 Dec 2027
Stage 8 — Evaluation & Reporting
Project evaluation
Project evaluation finalised. Dissemination activities delivered across industry channels.
31 Dec 2027
Final Report to NHVR
Project close-out
Stand-alone final report submitted for public dissemination. Total funding delivered to Opposite across the full program.
30 Jun 2028
Termination Date
Agreement end
Formal Termination Date of the Funding Agreement between Opposite and the NHVR.
Section 9
Industry-Wide Survey
Full survey content — see what respondents will complete. Estimated time: 10–15 minutes.
About You — Participant Information
Help us understand who we're hearing from. This information is confidential.
Q1
What best describes your primary role?
Driver (company employed)
Driver (owner-operator)
Fleet Manager
Transport Operator/Owner
Scheduler/Dispatcher
Safety/Compliance Officer
Other
Q2
What industry segment do you primarily work in?
Long-haul interstate
Regional/rural
Metropolitan/urban
Construction/civil
Mining/resources
Mixed operations
Q3
How many heavy vehicles does your organisation operate?
1–5
6–20
21–50
51–200
200+
Q4
How many years have you been in the heavy vehicle industry?
Less than 2
2–5
6–10
11–20
More than 20
Current State of Fatigue Management
Tell us about fatigue management practices in your workplace or operation.
Q7
How would you describe how fatigue is currently managed in your workplace?
Well managed with clear systems
Somewhat managed but inconsistent
Mostly left to individual drivers
Not well managed
Not sure
Q8
What fatigue management tools or systems are currently used? (Select all that apply)
Work diaries (paper)
Electronic Work Diary (EWD)
In-cab fatigue camera
Scheduling/rostering software
Fatigue risk management plan
Pre-trip fitness for duty checks
Wearable devices
None that I'm aware of
Q9
How confident are you that current fatigue management practices actually keep drivers safe?
Very confident
Somewhat confident
Neutral
Not very confident
Not at all confident
Q11
Have you ever felt pressure to continue driving when you believed you were too fatigued?
Never
Rarely
Sometimes
Often
Always
AI & The Future: Readiness and Opportunities
We want to understand your views on where AI could help — and what concerns you have.
Q24
How would you describe your overall attitude toward AI being used in fatigue management?
Very positive
Somewhat positive
Neutral
Somewhat negative
Very negative
Q25
Where do you think AI could be helpful in managing fatigue? (Optional — select all that apply)
Predicting when a driver is becoming fatigued before it's obvious
Identifying patterns in scheduling that increase fatigue risk
Providing personalised alerts and suggestions
Helping fleet managers make better scheduling decisions
Monitoring sleep quality and readiness for duty
Reducing paperwork and compliance burden
I don't think AI would be helpful
Q27
What would need to be true for you to feel comfortable with an AI-based fatigue management system? (Optional — select all that apply)
I understand exactly what data is collected
Data is used for safety only
I can see what the system sees about me
It's optional or I can opt out
Drivers were involved in designing it
It reduces my workload rather than adding to it
It works reliably without too many false alarms
My employer can't access individual data
Industry Needs and Gaps
Understand what the heavy vehicle industry needs most.
Q26
What is your BIGGEST concern about AI in fatigue management?
It will be used for surveillance, not safety
It won't understand real driving conditions
It will lead to job losses or changes
I don't trust the technology to be accurate
Data privacy and who has access
It will be too expensive for smaller operators
I don't have concerns
Q28
What does the heavy vehicle industry need MOST to improve fatigue management? (Select up to 3)
Better scheduling practices
More affordable technology solutions
Stronger enforcement of existing rules
Better training for managers and schedulers
More rest areas and facilities
Cultural change — make it OK to say 'I'm tired'
Integrated technology that connects different systems
Financial support for small operators
Evidence-based guidelines for AI use
Better understanding of driver wellbeing
Q30
Do you believe smaller fleet operators face greater challenges managing fatigue when compared to larger companies?
Yes, significantly
Yes, somewhat
About the same
No — smaller operators manage better
Not sure
Final Reflections & Next Steps
Tell us anything else that might help. Your input shapes this work.
Q33
Would you be willing to participate in a follow-up interview to share more about your experience?
Yes
No
Maybe — tell me more
Q34
If yes, share your email (collected during session)
Q32
Is there anything else you'd like to share about fatigue management, technology, or your experience in the industry? (Optional)
— Collected verbally in session or shared after
Section 10
Next Steps
What happens between now and our next Industry Partner Meeting.
Completed Work (This Phase)
Reflecting on our achievements to date:
Communications Plan
25-month engagement strategy finalised. Social media live. Industry awareness underway.
Ethics Plan
Five binding commitments: no surveillance, no disciplinary use, no performance management, full transparency, human dignity paramount.
Stakeholder Plan
Governance structure defined. Distributed decision-making model established. Partner roles and responsibilities clarified.
Current State Report — Nearing Completion
The Current State Report will be shared in early May 2026, formally closing this research phase. This report consolidates:
Between Now & Next Meeting (June 2026)
Partners
Nominate Participants & Contacts
Share driver and operator contacts for interviews and field testing. Early introductions reduce admin burden later.
Partners
Social Media Amplification
Share DRIVE S(AI)FE LinkedIn content within your networks. Encourage staff to complete the industry survey.
Partners
Operational Readiness
Prepare fleet data, contact lists, and feedback on the requirements snapshot (arriving late April).
Opposite
Deploy Industry Survey
Launch wide survey and semi-structured stakeholder interviews. Synthesise findings into Current State Report.
Opposite
Complete Internal Pilot
Finish Phase 1–3 data collection, analysis, and recommendations. Prepare learnings for next meeting.
Opposite
National Trucking Week
Present project at National Trucking Week (May). Gather feedback from broader industry audience.
Closing Message
Thank you for your partnership in this national initiative. The insights you bring, the operators you connect us with, and the feedback you provide are not peripheral — they are central to DRIVE S(AI)FE's success. We are building AI that genuinely serves the heavy vehicle industry, and that work is only possible with your voice at the table.
Questions, feedback, or ideas? Reach out to the project team anytime. We will see you in the next meeting.
Thanks for your time, and see you at the next one!
