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In-Cab HMI &

Driver Experience

CONFIDENTIALITY NOTICE

Due to confidentiality & the nature of projects currently in development, I'm unable to share specific data, internal deliverables and proprietary findings from this project. All images are sourced from Volvo Group's public library. The process, methods, and outcomes described here reflect my genuine contributions.

What the work was & where it landed

Evaluate next-generation in-cab HMI systems across Volvo Trucks and Mack Trucks, covering infotainment displays, instrument clusters, ADAS interfaces, physical controls, and sleeper cab systems across different configurations. My contribution also included UI text and localisation review for the North American market, evaluating interface strings for clarity and driver context.

The work spanned the full research lifecycle: study design, execution, synthesis, requirements writing, and implemented feature review. Findings fed directly into active development cycles alongside UI designers, engineers, and product teams across ICE, EV, and autonomous platforms, with cross-functional alignment across teams in Gothenburg, and Lyon.
ROLEUsability UX Research UX Strategy Prototyping
TEAMUI Designers Usability Leads Engineers Management
CLIENTVolvo Trucks Mack Trucks
TIMELINE2023-2025

Why truck HMI research is a different problem

Professional truck drivers operate in one of the most cognitively demanding HMI environments that exists. Every interaction may it be a climate adjustment, a navigation input, an ADAS alert happens while managing a 40-tonne vehicle, under time pressure, and conditions where errors carry real consequences.
Unlike passenger vehicles, commercial trucks serve as both workspace and, for long-haul drivers, a living space. This extends the design challenge beyond the driving task including how drivers rest, manage their environment, and navigate systems during non-driving hours in the sleeper cab.

The expert compensator problem. Years behind the wheel mean professional drivers develop workarounds for friction they no longer consciously register. Self-reported usability is systematically optimistic. A driver saying "that's not a problem" is often exactly the signal worth investigating further.

The rhythm of research

Research ran on an agile cadence synced to development sprints. Each cycle was scoped to what the team needed to decide next, which meant findings had to be useful immediately, not after a full synthesis phase. A representative cycle looked like this:
01
Scope Alignment
What decisions does the development team need to make in the next sprint? Which HMI areas are live in the current prototype?
02
Prototyping, Questionnaire & Piloting
Prototypes were built and refined ahead of sessions including some light coding work to ensure the test environment reflected the interactions being evaluated. Questionnaires were designed around realistic driving contexts. Eye-tracking calibration and pilot sessions ran before each study to pressure-test timing and flag gaps before participants arrived.
03
Participant Sessions
Smart Eye gaze data was recorded in parallel with think-aloud structured task scenarios. Setup included moderator and note-taker to allow full attention to participant behaviour without losing data.
04
Synthesis
Critical observations were flagged immediately after sessions before the team moved on. Gaze data cross-referenced with verbal transcripts before the next session day, so patterns could emerge across participants rather than being lost in session-by-session noise.
05
Findings brief
A structured report covering each task with severity ratings categorised against usability standards was presented. Issues not meeting the threshold were flagged. it was presented with a summary slide and suggestions to stakeholders involved
06
Back into development
UX requirements were written based on findings, covering both current and future development cycles. Suggestions were implemented within technical and project constraints, and where possible, validated again in subsequent studies. Implemented features were tested against usability standards once built.

What were the research methods

Research methods were selected not as a standard toolkit but based on what the research study needed to answer, the timeline, and the constraints of this environment. The study often combined different methods.

SIMULATOR & ON-ROAD EVALUATION

Moderated sessions with professional drivers across lab, simulator, and real vehicle environments using think-aloud protocols and structured task scenarios. Recruited across target driver profiles for each study. Different environments offered different tradeoffs between ecological validity and experimental control.

EYE-TRACKING STUDIES

Configured and ran Smart Eye setups across on-road and simulator environments. Gaze data overlaid with verbal feedback often revealed where drivers looked didn't match what they reported.

EXPERT AND HEURISTIC REVIEW

Evaluated prototypes and built features based on usability principles covering force-feedback, lighting, sound design, and physical control ergonomics.

COMPETITIVE BENCHMARKING

Reviewed competitors across the commercial vehicle space HMI like layout approaches, physical control strategies, ADAS interface patterns to understand the solution and where differentiation was meaningful.

What was evaluated

HMI & DISPLAYS


  • Infotainment interactions

  • Instrument cluster & DID

  • Navigation & route input

  • ADAS visibility & alerts

  • Lighting & dimming behaviour

  • Content & language

  • Symbol comprehension

CONTROLS & HARDWARE


  • Physical control ergonomics

  • Force-feedback & haptics

  • Climate control systems

  • Auditory feedback & sound

  • Switchgear & stalk interactions

  • Icon legibility

  • Reach to dash components

    eg: wireless charger

PLATFORMS & CONTEXTS


  • Diesel/ICE variants

  • Electric (EV) platforms

  • Autonomous variants

  • Driving task context

  • Sleeper cab & rest periods

Analysis & synthesis

Going beyond self-report was the only way to surface real friction. Eye-tracking data overlaid with verbal feedback revealed blind spots that interviews alone would have missed and a consistent pattern emerged: experienced drivers habitually compensate, while verbal reports stay positive.
Scroll to view chat

Driver - 10 year experience with long-haul trucks

OBSERVATIONS


Driver made two attempts to find and change the radio station with a slight head dip on the second attempt. Indicating that the interaction required more glance time off the road which would've been better used to focus while driving.

EYE TRACKING ANALYSIS


2X longer glance time than the competitors while also not matching usability standards. This led us to flag an interaction the driver rated easy.

This is an example and doesn't represent real data

Analysis Process

01
Session recordings, eye-tracking exports, think-aloud transcripts, and questionnaire responses compiled and cross-referenced per study.
02
Qualitative data clustered thematically. Issues mapped across participants to identify frequency and whether they reflected isolated incidents or systemic problems.
03
Findings prioritised by impact on safety, task efficiency, and driver experience giving design and engineering teams a clear picture of what needed immediate attention.
04
Insights packaged into reports and presented to teams across Sweden, Lyon framed in design-actionable language, not raw data dumps.

Impact

Cognitive load & interaction efficiency
Usability issues and findings across multiple HMI systems informed further studies on cognitive load reduction and task efficiency contributing to fewer steps required to complete key interactions.
Iterative design cycle integration
Findings fed directly into active development cycles which were reviewed against usability principles throughout, keeping research and development moving together.
Auditory feedback system
In-cab sound design was finalised through a dedicated usability review led end-to-end from evaluation criteria through to sign-off with stakeholders and experts.
UX requirements
Translated research and expert evaluation outputs into written requirements — turning insights into constraints development teams could act on directly.
Cross-regional alignment
Supported UX alignment across Gothenburg and Lyon while helping teams converge on decisions and maintaining brand distinction between Volvo Trucks and Mack Trucks.
Research-to-requirements pipeline
Built a repeatable process for turning research findings into specifications that engineering teams could act on directly informing longer-term product roadmaps as well.

Reflection

Working on a platform used by professional drivers across continents made the stakes of getting things right very clear. Every interaction a climate gesture, a navigation prompt, an ADAS alert happens in a working environment where cognitive load is high and errors have real consequences.

The most valuable thing I took from this project was learning to treat familiarity as a signal, not reassurance. When experienced drivers said something "wasn't a problem," that was often exactly when it was worth looking more carefully. The best insights didn't come from what people told us they came from what we observed when they stopped explaining and just drove.

I also grew significantly in cross-functional and cross-cultural collaboration. Coordinating across teams in Sweden, Lyon, and India with different priorities, timelines, and design philosophies required as much communication skill as research skill.

Available for full-time roles.

Maybe it's with you.

Get in touch

vartakgrishma19@gmail.com