Master Thesis in Software Engineering: Adaptive Architecture for Human-AI Annotation Systems

Thesis Scope & Problem Statement

Volvo Autonomous Solutions develops systems for automated hauling operations in quarries and open pit
mines using Volvo trucks. Validating these systems requires high-quality ground-truth datasets. Our team
owns a camera-based annotation tool that produces bounding boxes used as a reference for validating
LiDAR- and radar-based moving-object perception, with camera perception entering the stack. Errors in
these reference annotations can compromise the validity of downstream evaluations.

A previous Master's thesis evaluated different architectures for combining AI-generated annotations wi
human verification. The results highlighted trade-offs between annotation correctness, proces
performance, maintainability, and human effort: no single static configuration performed best across
these dimensions. Participants also reported verifying less actively when AI suggestions appeared
plausible, highlighting a potential risk of automation bias.

This thesis project is intended for two Master's students working together.

The central focus of this Master's thesis is to design, implement, and evaluate a confidence-aware adapti
verification architecture within the existing annotation tool. Instead of applying the same review process 
every detection or scene, the system will use confidence signals to select an appropriate depth of hum
verification. This is a software engineering thesis focused on how to structure, integrate, test, and evalua
that adaptive behavior, with particular attention to maintainability, traceability, fallback behavior, and
effective human oversight. Existing AI models provide the application context; developing new percepti
models is not the primary objective.

Research Objectives & Tasks

The work may include:

• Design and integrate a modular architecture with explicit interfaces for confidence provider
  verification policies, decision logging, and annotator feedbac
• Implement confidence-provider components that expose calibrated or normalized signals, such 
  model confidence, model agreement, source model, and object class frequency, through a comm
  interface.
• Implement and compare interchangeable threshold-based and learned decision policies for routing
  cases to light review, full review, or a second review. Automatic acceptance will be considered only
  where permitted by safety policy, with low-confidence, out-of-distribution, and safety-critical cas
  routed to human review.
• Integrate attention cues into the annotation interface and record the signals, policy version, selected
  review mode, and human outcome for each routing decision.
• Develop automated tests for routing decisions, component interfaces, and fallback behavior.
• Benchmark the adaptive architecture against existing manual, sequential, and parallel workflo
  using annotation correctness, recall on safety-critical classes, error catch rates, task completion time,
  routing overhead, and human intervention.
• Evaluate maintainability and testability through change scenarios, such as adding a confiden
  provider or replacing a decision policy.
• Conduct a counterbalanced user study to investigate verification behavior, cognitive workload, tru
  calibration, and susceptibility to automation bias.

Depending on feasibility and measurable added value, the work may also explore out-of-distribution
detection or vision-language-model-derived scene complexity as additional confidence signals. The co
evaluation will use fixed policies during controlled experiments; online adaptation is an optional extension.

Who Are We?

You will join the Verification & Validation (V&V) Virtual Driver team at V.A.S., focused on ensuring t
integrity of the software stack for autonomous decision-making and control. The team brings together
developers with varied backgrounds and seniority, all centered around robust V&V practices. The
environment is friendly and helpful, with both in-office development and in-vehicle testing.

Who Are You?

You are a final-year Master's student in Software Engineering, Computer Science, or a related field, with
strong interest in software architecture, software quality, and building maintainable systems. You are
comfortable programming in Python and interested in extending and evaluating an existing software
platform in an industrial setting.

You should have a foundation in modular software design, interface design, automated testing, and version
control, together with an interest in empirical software engineering. You should be motivated to compare
design alternatives and connect architectural decisions with measurable effects on correctnes
performance, maintainability, and testability.

Familiarity with AI-assisted systems, machine learning, computer vision, or human-computer interaction is
advantageous, but prior specialization in machine learning or object detection is not required. An interest in
learning enough about model confidence and human verification to make sound software design decis
is important.

Why Join Us?

This thesis offers an opportunity to apply software engineering methods to a concrete industrial challeng
designing an adaptive human-AI workflow that is maintainable, testable, and auditable. You will build on 
existing annotation platform and prior thesis results, taking the work from architectural design through
implementation and empirical evaluation.

You will:

• Gain hands-on experience in architectural design, component integration, automated testing, and
  evaluation of an industrial software system.
• Investigate how confidence signals can guide human attention while preserving traceability a
  human oversight.
• Measure architectural trade-offs across annotation correctness, performance, maintainabilit
  testability, and human effort.
• Collaborate with engineers working on the validation of autonomous transport solutions.

You will gain practical experience in designing and evaluating human-AI systems in a safety-critical context.
We look forward to receiving your application letter, academic transcript, and CV in English.

Ready for the next move?

Are you excited to bring your skills and disruptive ideas to the table? We can’t wait to hear from you. Apply today!