Thesis Work: Safety Aware Decision Framework for Uncertain Perception of Large Autonomous Construction Vehicles

ABB
24 minutes ago
Posted date24 minutes ago
N/A
Minimum levelN/A
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This Position reports to:
Maintenance Manager
Details
Your role and responsibilities
How can uncertain multi-sensor perception data be transformed into reliable, deterministic safety actions for large autonomous construction vehicles operating in dynamic environments?
This thesis will be investigating safety aspects for autonomous dump trucks. The goal is to design and evaluate a safety framework using multi sensor data (LiDAR, radar, GPS and possibly camera, although its not required that every sensor is used).
The data from these sensors will be uncertain, due to noise, weather conditions and terrain. Its therefore important to weigh these inputs given their respective uncertainty in order to chose the 'safest' option, based on the vehicles current state and its surroundings.
By not using a fixed safety threshold but instead using a probabilistic framework the system will be able to respond better to uncertainties in its surroundings.
Qualifications for the role
More aboout us
Recruiting Manager Touko Jalkanen, +46 703 904 980, will answer your questions.
Positions are filled continuously. Please apply with your CV, academic transcripts, and a cover letter in English. We look forward to receiving your application!
A Future Opportunity
Please note that this position is part of our talent pipeline and not an active job opening at this time. By applying, you express your interest in future career opportunities with ABB.
We value people from different backgrounds. Could this be your story? Apply today or visit www.abb.com to learn more about us and see the impact of our work across the globe.
This Position reports to:
Maintenance Manager
Details
- Period: January - June 2026
- Number of credits: 30 ECTS
- Number of students for this thesis work: One
- Location: Kista
Your role and responsibilities
How can uncertain multi-sensor perception data be transformed into reliable, deterministic safety actions for large autonomous construction vehicles operating in dynamic environments?
This thesis will be investigating safety aspects for autonomous dump trucks. The goal is to design and evaluate a safety framework using multi sensor data (LiDAR, radar, GPS and possibly camera, although its not required that every sensor is used).
The data from these sensors will be uncertain, due to noise, weather conditions and terrain. Its therefore important to weigh these inputs given their respective uncertainty in order to chose the 'safest' option, based on the vehicles current state and its surroundings.
By not using a fixed safety threshold but instead using a probabilistic framework the system will be able to respond better to uncertainties in its surroundings.
Qualifications for the role
- Master's thesis student with a relevant background (e.g automation, mechatronics, electric, mobile machines, computer science)
- Interested in problem-solving and bringing curiosity and analytical thinking to your work
- Comfortable communicating in English and Swedish (both written and spoken)
- Collaborative and passionate about teamwork
- Good programming skills
More aboout us
Recruiting Manager Touko Jalkanen, +46 703 904 980, will answer your questions.
Positions are filled continuously. Please apply with your CV, academic transcripts, and a cover letter in English. We look forward to receiving your application!
A Future Opportunity
Please note that this position is part of our talent pipeline and not an active job opening at this time. By applying, you express your interest in future career opportunities with ABB.
We value people from different backgrounds. Could this be your story? Apply today or visit www.abb.com to learn more about us and see the impact of our work across the globe.
JOB SUMMARY
Thesis Work: Safety Aware Decision Framework for Uncertain Perception of Large Autonomous Construction Vehicles

ABB
Stockholm
24 minutes ago
N/A
Part-time
Thesis Work: Safety Aware Decision Framework for Uncertain Perception of Large Autonomous Construction Vehicles