Master thesis - Robust 4D Radar Perception for Autonomous Driving: Detect and Classify Multipath
Confirmed on the employer's own hiring board on Oct 25, 2026. First seen by Alion on Oct 2, 2026. Scania scores A on the Alion truth index.
30 credits - Robust 4D Radar Perception for Autonomous Driving: Detecting and Classifying Multipath Effects
Introduction
A Master's thesis is an excellent way to get closer to TRATON Group R&D and build relationships for the future.
Background
4D radar is becoming an increasingly important sensing modality in autonomous driving. New generations of radar offer better range and finer spatial resolution, making radar useful not only for geometric scene understanding, but also for motion reasoning through Doppler measurements.
At the same time, 4D radar data can contain significant multipath artifacts. These effects can produce false detections with incorrect position, incorrect Doppler, or both, often appearing as ghost objects. Such errors can degrade downstream perception and motion estimation if not handled robustly.
This creates an important research opportunity: developing methods that identify and suppress multipath-induced false positives while preserving true dynamic and static targets.
Objective
The objective of this thesis is to investigate methods for classifying and removing false 4D radar returns caused by multipath propagation in autonomous driving scenarios. The final research questions will be defined together with the student based on literature, available data, and project direction. Possible directions include:
Characterizing multipath failure modes in modern 4D radar data.
Designing models or feature pipelines to distinguish valid returns from ghosts.
Leveraging spatial, Doppler and temporal cues for robust filtering.
Comparing classical signal-processing and machine-learning approaches.
Evaluating the impact of filtering on downstream tasks such as odometry, motion estimation, or occupancy prediction.
The Project Offers
Work on a high-impact topic at the intersection of radar sensing and autonomous driving.
Access to real-world driving data and computational resources.
Close supervision and collaboration in an active research environment.
Opportunity to contribute methods that can improve robustness of future AD systems.
Potential for scientific publication depending on outcomes.
Who are we looking for?
We are looking for one or two motivated Master’s students in Computer Science, Electrical Engineering, Engineering Physics, Robotics, Applied Mathematics, or related fields.
Experience in one or more of the following is beneficial:
Machine learning or deep learning
Signal processing
Computer vision or multimodal perception
Probabilistic modeling and data analysis
Python and PyTorch
Autonomous driving, robotics, or sensor fusion
The planned thesis start is January 2027.
Number of students: 1
Start date for the thesis work: [To be agreed]
Estimated time required: 20 weeks, full time (30 credits)
Contact persons and supervisors
Ajinkya Khoche, [email protected]; Jonny Andersson, [email protected]
Hiring Manager: Magnus Granström, [email protected]
Application
Your application must include a CV, personal letter, and transcript of grades.
A background check might be conducted for this position. We are conducting interviews continuously and may close the recruitment earlier than the date specified.
References
Publication date:
1.10.2026 - 30.11.2026 (applications evaluated continuously)