Thesis: Multimodal Spatiotemporal Intelligence for Fleet-Level Intelligence

Company description: Part of Volvo Group, Volvo Autonomous Solutions accelerates the development, commercialization and sales of autonomous transport solutions, focusing on defined segments for the on- and off-road space. The combination of strong tech expertise and skilled customer solutions creates innovative transport offers never seen before. We are constantly pushing our own skills and ability to drive change in a traditional industry to meet a growing customer demand. We are now looking for innovative, committed individuals to join us in our endeavor to create customer solutions that enhance safety, flexibility and productivity. Job description:What you will do At Volvo Autonomous Solutions, we are at the forefront of innovation, designing the autonomous transport solutions of tomorrow. As part of our global and diverse team, you will have the opportunity to work on an industrial research challenge that contributes to the future of safe, efficient and sustainable transport. We are looking for a master’s thesis student to investigate how shared information from multiple vehicles can be used to detect changing site conditions and support operational decision-making across a fleet. The thesis project Conditions observed by one vehicle may affect vehicles that subsequently approach the same area. For example, changes in road conditions, traffic situations or operational environments may have an impact on vehicle safety, routing and mission execution. The goal of this thesis is to investigate whether a shared spatiotemporal representation of sensor observations, vehicle signals, maps and operational events can be used to maintain a dynamic model of site conditions. The representation should also be reusable for applications such as ODD monitoring, road-condition assessment, mission assistance, traffic coordination, incident investigation and maintenance analysis. The project will include developing and comparing timestamp-based, single-modality and multimodal methods for maintaining a dynamic site-condition model. You will also investigate how the model can provide evidence-grounded recommendations, such as warning another vehicle, identifying an affected route or proposing a review of speed, routing or traffic-control settings. The work will include: Developing and comparing different approaches for modelling changing site conditions over time. Combining information from vehicle sensors, CAN signals, maps and operational events. Demonstrating evidence-grounded recommendations for fleet-level decision support. Measuring detection quality, warning lead time, false alarms and robustness to missing sensors. Evaluating how well the approach generalises across vehicles and operating conditions. Analysing the results and documenting your findings in a scientific report. Resources and research environment You will have access to multi-vehicle camera, CAN and operational data, as well as position data, site maps and reviewed events or condition changes. GPU compute resources and multimodal models will be available for developing and evaluating your approach. Additional sensor data from LiDAR and RADAR may also be available, depending on the selected scope of the project. You will work closely with experienced engineers and researchers in a collaborative AI and autonomy team, gaining insight into fleet-level intelligence, autonomous systems and the development of operational decision-support solutions. Who are you? You are enrolled in a master’s programme in computer science, data science, machine learning, electrical engineering, robotics or a related field. You have solid Python programming skills and hands-on experience with a deep-learning framework such as PyTorch or TensorFlow. We are looking for someone who is interested in autonomous systems, machine perception, spatiotemporal modelling and working with real-world sensor and vehicle data. You should also be: Independent, structured and curious. Analytical and motivated to solve challenging technical problems. Comfortable working with multimodal data, machine-learning experiments and evaluation metrics. Interested in fleet-level intelligence and operational decision support. Proficient in written and spoken English. What’s in it for you? This thesis gives you the opportunity to work on a real industrial research problem with access to unique multimodal vehicle data and the tools needed to conduct advanced experiments. You will receive close technical supervision from experts in AI and autonomous systems, as well as access to GPU resources and multimodal models. You will also become part of a dynamic and inclusive work environment where new ideas are encouraged and celebrated. How to apply To apply, send your CV, grade transcript and a short motivation explaining your interest in the project and relevant experience. Start and scope can be adjusted together with the supervisor. We value your data privacy and therefore do not accept applications via mail. Who we are and what we believe in We are committed to shaping the future landscape of efficient, safe, and sustainable transport solutions. Fulfilling our mission creates countless career opportunities for talents across the group’s leading brands and entities.