PACCAR ADvanced driver assistance system (ADAS) study

context

Role: UX Researcher

Tools: Google Sheets, FigJam, Excel

Timeline: Winter 2024 (3 months)

Overview

PACCAR was preparing to launch Fusion 3.0, the latest version of their Advanced Driver Assistance System (ADAS) for heavy duty trucks. While technical performance testing was underway, I was tasked with evaluating drivers’ experience with the ADAS system in real-world conditions. This study aimed to capture perceptions of driver trust, satisfaction, and usability pain points before the system’s official release to customers. The process involved designing a mixed methods study, qualitative and quantitative data analysis, and presenting findings in a digestible way to stakeholders.

Background

This research study was requested by the PACCAR Technical Center (PTC) Verification Team as part of their effort to assess the Fusion 3.0 ADAS system’s performance and acceptability before it officially launched in February 2025. The team was conducting several test drives with Fusion 3.0-equipped vehicles (namely Development Mileage Accumulation (DMA) tests and Global Powertrain Winter Tests) and needed a formal driver survey to be conducted in-person at the conclusion of these tests in order to capture driver feedback of the system.

The goal of the study focused on driver perception of performance and subjective experience, rather than technical measurements, because other studies were already underway that prioritized this information. The study was mixed-method in nature, capturing both qualitative and quantitative data in order to gain a comprehensive understanding of the drivers’ experiences. Feedback was gathered at the overall system level as well as an individual feature level. The features that made up the Fusion 3.0 system at the time included: 

  • Adaptive Cruise Control (ACC) (including Stop & Go capability)

  • Forward Distance Alerts (FDA)

  • Traffic Sign Recognition (TSR)

  • High Beam Assist (HBA)

  • Lane Departure Warning (LDW)

  • Highway Departure Braking (HDB)

  • Automated Emergency Braking (AEB)

Research methods

Participants: 6 drivers (2 contract drivers, 4 engineers), ages 31–62, with 8–34 years of trucking experience

Format: 1–1.5 hour in-person surveys & interviews at PACCAR Technical Center in Mount Vernon, WA

Data Collected:

  1. Quantitative: Likert-scale satisfaction, likelihood-to-use, and performance ratings

  2. Qualitative: Open-ended questions on positive, negative, and surprising experiences

Interview Guide Structure:

  1. Demographics + warm-up questions

  2. Evaluation of individual features

  3. Evaluation of overall system

  4. Closing reflections

Analysis:

  1. Qualitative: Thematic analysis in FigJam (digital sticky notes, iterative clustering)

  2. Quantitative: Averages calculated for satisfaction, likelihood, and performance

Quantitative PERFORMANCE RATING ResULTS

The performance ratings of each feature (with the exception of Highway Departure Braking and Automated Emergency Braking) were as follows:

Note: Not enough people experienced Highway Departure Braking or Automated Emergency Braking so it wasn’t possible to capture meaningful quantitative data around its performance.

overall RESULTS

According to the criteria of the 1-10 scale, Traffic Sign Recognition, High Beam Assist, and Lane Departure Warnings were the features that received passing performance ratings. Adaptive Cruise Control and Forward Distance Alerts did not. Not enough people experienced Highway Departure Braking and Automated Emergency Braking to be able to capture meaningful quantitative data around its performance. This is admittedly a limitation of the study, but one that cannot and should not be remedied; drivers are not encouraged to deliberately activate these features for obvious safety reasons, and most encounters tend to be false positives. For data around how these emergency features perform during a true positive scenario, a controlled validation test would be best, not a driver study. Finally, the overall system (Fusion 3.0) received passing performance ratings.

 

Participants had a lot to say about ACC in particular, and notably, it was cited as the thing that works best about the overall system and also the thing that needs the most improvement. Participants were impressed by its ability to maintain a set distance and adapt to the flow of traffic (what works best) but they also acknowledge that its braking functionality should be improved (what needs the most improvement). The specific braking behavior that needs to be improved are the lack of smoothness and audible pulsing noises coming from the foundation brakes. ACC is arguably one of the most significant, and complex, Fusion 3.0 features, so special attention to its performance should be paid in the future because it has the most potential for impacting the driver experience significantly.

 

One of the most significant themes that came up across features was audible noises. Drivers felt mildly annoyed at best, and distracted at worst, by the sheer frequency of sounds coming from the system. Some of these sounds were warnings and some of them were notifications of something faulting. The inconvenience from warnings came from the false positives that sometimes where prone to happen (ex. LDW in construction zones, ACC when vehicles are exiting the highway, FDA when overtaking another vehicle, and so on). Alone, false positives from a single feature are tolerable. But together, they can be a nuisance. It definitely can be argued that false positives are better than false negatives when it comes to an ADAS system, but one has to consider the effect of desensitization after a while, which indeed was mentioned by at least two drivers.

 

The inconvenience from fault notifications came almost exclusively from ACC. These manifest as a “bombardment” of notifications that the driver largely cannot do anything about, but is forced to hear, whether they are accurate or not. For example, sometimes drivers would get notifications about ACC faulting even if the system was still functional. But typically, the notifications happened during inclement weather when camera or radar function would be compromised. They would also happen during startup. And they typically happen multiple times during a key cycle. This is especially difficult for drivers when they cannot easily remedy the fault on a long-haul drive. In future versions of Fusion, special attention should be paid not only to preventing these faults in the first place but considering how often the audible notifications need to occur.

 

Overall, the system’s weaknesses (such as fault notifications, frequent audible noises) ultimately do not detract from its performance. Improving on them will make the driving experience more pleasant, but the Fusion 3.0 system is already a helpful enhancement to the overall driving experience, according to drivers.

Conclusions

The consensus from the study was that although there is room for improvement, the overall system has improved since earlier versions and is ultimately useful and road-ready.

A summary of the system’s strengths:

  • ACC’s ability to maintain a set distance from vehicles and adapt to the flow of traffic, including its response to vehicles cutting, has improved

  • TSR detects most traffic signs accurately

  • Visual warnings for FDA and LDW are satisfactory

  • HBA override controls mostly intuitive

A summary of the system’s weaknesses:

  • ACC foundation brakes could be smoother, audibly pulse

  • Engine braking power could be optimized, especially while driving downhill

  • Frequent, audible driver notifications when camera & radar fault is mildly annoying at best and distracting at worst

  • ACC picks up vehicles in exit lane

  • FDA and LDW audible warnings can be unpleasant and distracting

  • TSR regarded as useful, but not accurate enough to be relied on

  • High beams are quick to turn on and off, regarded as distracting

  • LDW not as effective in construction zones

  • FDA false positives while overtaking vehicles are common

recommendations

Collecting more data from non-engineer drivers or others who have experienced the system for longer than the duration of Winter Test would be the most meaningful next step. Engineer drivers know the system more intimately than customer drivers would and capturing non-expert feedback would add to the wealth of knowledge being collected. Additionally, getting more feedback from participants who experience the system for longer than the duration of the Winter Test would allow us insight into how the system performs over a longer period of time.

 A summary of the most impactful enhancements to the Fusion 3.0 system would be:

  • Refine alert systems

    • Tier audible warnings by severity

    • Reassess which faults require both audible + visual cues

  • Improve Adaptive Cruise Control

    • Smoother braking and engine braking optimization

    • Reduce redundant fault notifications

  • Enhance driver control

    • Allow opt-in/out settings for features like High Beam Assist

    • Consider dynamic following distance tied to turn signal usage

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