Thesis Student - Quantitative Modelling and Assessment of Multi-Sensor Redundancy

Introduction

A thesis project at TRATON provides a unique opportunity to gain practical experience, expand your professional network, and contribute to the future of sustainable transportation. Many of our current employees began their careers at Traton by completing their degree projects within the organization.

TRATON Group includes leading commercial vehicle brands such as Scania, MAN, Volkswagen Truck & Bus, and International. Together, we are committed to driving the transformation toward sustainable transport solutions. As a thesis student, you will work closely with experienced engineers and specialists, contributing to real-world product development while gaining valuable industry experience.

Background

Modern autonomous vehicles rely on multiple sensor technologies, such as cameras, radar, lidar and GNSS, to provide reliable information about the vehicle and its surroundings. These sensor technologies have different strengths and weaknesses, and their performance can vary significantly depending on environmental conditions, infrastructure and operating scenarios.

Quantitative methods for assessing sensor redundancy could provide a more objective understanding of the capabilities and limitations of multi-sensor configurations. By considering sensor performance, functional overlap, statistical dependencies and uncertainty, it may be possible to develop metrics that describe the degree redundancy available under different operating conditions.

Job Description

Phase 1: Establish a theoretical framework for quantitative assessment of redundancy in multi-sensor systems. The work should start with a literature review of redundancy metrics, reliability and availability modelling, sensor diversity, statistical dependencies and methods for evaluating overlapping sensor capabilities. Investigate how individual sensor performance characteristics can be combined into meaningful system-level redundancy metrics. Python or similar tools may be used.

Phase 2:Develop and evaluate mathematical methods for quantifying redundancy in representative multi-sensor configurations. The methods should consider differences in sensor capabilities, overlapping measurement functions and correlated performance degradation. Investigate how redundancy metrics vary with sensor performance and environmental conditions, and evaluate the influence of uncertainty and modelling assumptions. Sensitivity analyses and representative simulation scenarios should be used to assess the applicability and limitations of the proposed methods. Where possible, the results may be compared against experimental data or existing sensor performance models.

Additional comments:The thesis should focus on developing objective and reusable mathematical methods for assessing redundancy in multi-sensor systems, rather than implementing a complete autonomous driving system. The scope and selection of sensor technologies will be determined together with the supervisors. The work may use existing sensor performance models or representative simulated data as inputs. The methods should be applicable to autonomous driving and ADAS systems, as well as other applications involving heterogeneous sensor configurations.

Education / Program / Qualifications

We are looking for a curious, motivated, and hands-on student with a strong interest in embedded systems, multiple sensor technologies, electrical architecture, and sustainable transportation.

Preferred Qualifications

Interest in sensor technology, embedded systems and automated driving systems.

Fluency in English, both written and spoken.

Education:

Students from engineering with emphasis in robotics, signal processing, control, automation, autonomous systems, computer science, electrical engineering, mechatronics, mathematics, applied physics or similar.

Thesis Start Date

• February 2027

The exact start date and project timeline will be agreed upon between TRATON, the selected candidate, and the academic institution.

Contact Information

For questions regarding the thesis project, please contact:

• Mikael Salmén, Senior Development Engineer - TGRAAP1

mikael.salmen@scania.com

• Laís Ramos, Manager - TGRAAP1

lais.carolina.ramos@scania.com

Application

Your application must include:

• CV
• Personal letter
• Transcript of grades

We welcome applicants from all backgrounds and believe that diverse perspectives contribute to better innovation, stronger teams, and improved solutions.

Please note that a background check may be conducted for this position. Interviews will be held continuously, and the recruitment process may close before the stated application deadline.

Join us in shaping the future of battery technology and sustainable transportation. We look forward to receiving your application.

Requisition ID: 34684

Number of Openings: 1.0

Part-time / Full-time: Full-time

Permanent / Temporary: Temporary

Country / Region: SE

Location(s):
Södertälje, SE, 151 38

Required Travel: 0%

Workplace: Hybrid