About this role
Transport is at the core of modern society. Imagine using your expertise to shape sustainable transport and infrastructure solutions for the future. If you seek to make a difference on a global scale, working with next-gen technologies and the sharpest collaborative teams, then we could be a perfect match.
Background
The development and validation of Advanced Driver Assistance Systems (ADAS) and autonomous driving functions require large amounts of representative traffic data covering diverse traffic situations, environmental conditions, and road users. Collecting such data in the real world is costly and time-consuming, and certain critical or rare situations can be difficult to capture.
Virtual traffic environments and simulation-based testing offer opportunities to complement real-world testing by enabling repeatable experiments, controlled variation of traffic scenarios, and the generation of additional data for system validation.
Recent advances in sensor fusion, scene reconstruction, computer vision, and generative methods create new possibilities for reconstructing realistic traffic environments and generating parameterized traffic scenarios from real-world data.
In this thesis, we aim to explore these possibilities through two complementary research directions: reconstructing virtual traffic environments from multi-sensor reference data and generating parameterized traffic scenarios for ADAS validation. The specific focus can be adapted to the students’ interests and technical backgrounds.
Description of the Thesis Work
The thesis will investigate how real-world traffic data and simulation technologies can be used to support the validation of ADAS and autonomous driving functions. Depending on the selected direction, the work may focus on one of the following areas:
1. Virtual Traffic Environment Reconstruction
This direction focuses on reconstructing virtual traffic environments using multi-sensor reference data collected from trucks equipped with a reference sensor setup. The available data may include LiDAR point clouds, radar measurements, camera images, and IMU measurements.
The objective is to investigate how accurately the surrounding traffic environment can be reconstructed and how the reconstructed environment can support simulation and replay-based validation, including the replay of recorded sensor signals with software components in the loop.
Potential challenges include sensor synchronization, point-cloud density, occlusions, sensor coverage, and reconstruction fidelity.
2. Parameterized Traffic Scenario Generation for ADAS Validation
Parameterized Traffic Scenario Extraction for ADAS Validation
This direction focuses on extracting and interpreting traffic scenarios from real-world data and translating them into a formal scenario description language suitable for simulation-based validation. The objective is to investigate how recorded traffic situations can be identified, represented, and converted into structured, simulation-ready scenarios.
Potential areas of investigation include scenario extraction from recorded sensor data, interpretation of traffic participants and their interactions, mapping observed behavior to scenario parameters, and representing the resulting scenarios in a standardized scenario description language.
The two directions share the goal of improving simulation-based validation through more representative and flexible traffic environments. Depending on the selected approach, the work may also explore how reconstructed environments and generated scenarios can support synthetic data augmentation and more comprehensive testing workflows.
Potential Areas of Work
- Multi-sensor data processing and fusion
- 3D scene and environment reconstruction
- Virtual traffic environments and sensor simulation
- Parameterized traffic scenario generation
- Evaluation of reconstruction quality and scenario realism
- Synthetic data generation and augmentation
- Simulation-based ADAS validation, including false-positive and false-negative analysis
Suitable Background for the Student
We are looking for master’s students with an interest in autonomous driving, intelligent vehicles, and simulation-based validation. Relevant academic backgrounds include:
- Machine Learning and Deep Learning
- Computer Vision
- Robotics and Sensor Fusion
- Signal Processing and Sensor Modelling
Experience with Python, 3D data processing, simulation environments, or autonomous driving systems is beneficial.
Practical Information:
Thesis level: Master’s thesis
- Number of students: Two
- Location: Gothenburg, Sweden
- Language: English
- Starting date: January 19, 2027
- Application deadline: November 21, 2026
Are you interested in contributing to the development of next-generation validation methods for ADAS and autonomous driving? We look forward to hearing from you. Apply today!
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