30 hp - Adaptive Hierarchical Inference with Foundation models for Commercial-Vehicle Edge-Cloud Systems Introduction Thesis work is an excellent way to get closer to Scania and build relationships for the future. Many of today's employees began their Scania career with their degree project. Background TRATON GROUP is one of the world’s leading commercial vehicle manufacturers. Its brands include Scania, MAN, International and Volkswagen Truck & Bus. The group offers light commercial vehicles, trucks and buses, supported by financing, charging and digital logistics services. Through its global operations, production sites, and sales and service networks, TRATON has access to diverse vehicle platforms, real-world data and fleet environments. This provides a strong basis for developing and validating solutions for more sustainable and efficient transportation. Within TRATON’s Cloud and Embedded Platform department, we develop solutions for connected vehicles and IoT platforms. This work supports TRATON’s growing focus on communication, digital services and smart transport. Advanced data analysis is an important part of these developments. Modern commercial vehicles generate diverse data from vehicle telemetry, time-series sensors, cameras, radar, LiDAR and operational logs. High-performance units and embedded platforms make it possible to process much of this data close to the vehicle, reducing latency, network dependence and unnecessary data transfer. Foundation models could support predictive maintenance, anomaly detection, diagnostic assistance, multimodal perception and reasoning. However, the most capable models require cloud-scale computing resources, while onboard systems face strict limits on computation, memory, energy and communication. This research therefore focuses on hierarchical inference. A compact model performs real-time analysis onboard, while a larger cloud-based model is used only when the local prediction is uncertain and the latency and resource conditions allow it. ObjectiveThe objective is to design and evaluate an uncertainty-aware, adaptive inference cascade across sensing or embedded devices, a vehicle high-performance unit HPU, and cloud resources. The focus of the work will be on efficient inference, confidence estimation, and orchestration rather than on development of foundation models. The problem definition of the thesis is: How can calibrated uncertainty and vehicle operating conditions be used to decide, per input, whether a compact onboard model should answer locally or defer to a larger model, while meeting task-quality and latency requirements and limiting communication and resource use? Together with the supervisors, the student will select one representative commercial-vehicle use case, such as predictive maintenance or anomaly detection, visual diagnostic assistance, time-series reasoning, or multimodal situational awareness. To keep the 20-week scope feasible, the thesis should prioritize one use case, o
