30 hp - Efficient World Representations for End-to-End Autonomous Driving Introduction A Master's thesis is an excellent way to get closer to TRATON Group R&D and build relationships for the future. In this thesis, you will contribute to EGoPT, a new industrial research project on World Models for Autonomous Driving. Background Modern autonomous vehicles generate large amounts of sensor data from cameras, lidar, radar, and vehicle-state signals. Processing all this information at full resolution and over long temporal histories can exceed the computational budget available for real-time planning. Compressing it too aggressively, however, may remove information that is important for planning or safety. The VINNOVA-FFI EGoPT project investigates how compact, task-aligned representations of multimodal sensor data can support real-time trajectory planning for autonomous heavy-duty vehicles. Current end-to-end driving methods and evaluation tools are mainly developed for passenger cars, while trucks introduce additional constraints related to vehicle size, articulation, load-dependent dynamics, braking distance, and computational resources. The thesis will primarily use public datasets, open-source models, simulation, and planning benchmarks and emerging truck-focused resources. Objective The thesis will investigate an initial research question within EGoPT. Possible directions include: Efficient spatial or temporal representations: compress sensor observations or scene history while preserving information needed for planning. Adaptive representations: allocate a limited token or compute budget to the cameras, regions, or information most relevant to the current driving situation. Safety-aware compression: evaluate whether compact representations preserve safety-critical information. Heavy-duty vehicle generalization and evaluation: adapt learned planners to different vehicle configurations, or evaluate and extend public benchmarks with articulated or configuration-dependent constraints. Job description During the thesis period, you will: Review relevant literature and help define a focused research question. Set up or reproduce an open-source autonomous-driving model, simulator, or benchmark. Implement and evaluate a method, benchmark extension, or experimental framework. Analyze relevant trade-offs in planning performance, safety, generalization, latency, memory, or computational cost. Document the work, present the results, and write the final thesis report. The expected outcome is a reproducible model baseline, evaluation method, or experimental framework that can support future research within EGoPT. Education/program/focus You are pursuing a Master's degree in computer science, machine learning, robotics, engineering physics, electrical engineering, or a related technical field. A suitable candidate should have strong programming skills, preferably in Python and PyTorch; knowledge of machine learning and deep learning; an interest in autonomous
