Msc Thesis Student - Reinforcement Learning for adapting a VLA model for Autonomous Trucks

Transport is at the core of modern society. Imagine using your expertise to shape sustainable transport and infrastructure solutions for the future. If you seek to make a difference on a global scale, working with next-gen technologies and the sharpest collaborative teams, then we could be a perfect match. 

Master Thesis Proposal: Closed-Loop Reinforcement Learning for adapting a Vision-Language-Action model for Autonomous Truck Driving 

Alpamayo is a Vision-Language-Action model trained primarily for passenger vehicle driving. Heavy-duty vehicles introduce different vehicle dynamics and tactical driving requirements. This thesis aims to adapt a passenger-car-trained Alpamayo model to specific requirements for a 40-tonne, 18–32m truck combination using closed-loop RL post-training. All post-training runs on a 128 GB unified-memory (UMA) computer.  

The reward function for RL targets the behaviours where cars and long combination vehicles differ from most. Heavy-duty vehicle drivers negotiate a mandatory exit – which requires several consecutive lane changes within a fixed distance – by signalling early and reducing speed as the exit approaches. These principles, together with off-tracking and larger required time gaps, shape the reward function. The tuned policy is benchmarked against the untuned passenger car baseline. 

The main objectives of this thesis are: 

• Articulated closed-loop setup: Replace the car-like dynamics in AlpaSim with a tractor–semitrailer model and build exit-ramp scenarios with dense traffic.
• Reward design (car to truck gap): Shape an RL reward function for safe completion of multiple lane changes to the exit within the available distance, using the early-signaling and speed reduction near exit.
• RL post-training & evaluation: Post-train the 10B policy with AlpaGym on 128 GB UMA and compare closed-loop KPIs (exit success rate, safety margins, comfort) against the untuned car baseline. 

The work will be carried out at Trucks Technology and Industrial (TTI), Göteborg, and is recommended for two students with a strong background in machine learning (preferably reinforcement learning) and Python. 

Thesis team: this is one of three linked master theses – (1) using the car-trained models as-is by prompting, (2) closed-loop reinforcement-learning post-training, and (3) in-vehicle deployment – forming a single thesis team that meets regularly and shares simulation infrastructure and results. 

Contact persons: 

Deepthi Pathare – Volvo TTI

mail: deepthi.pathare@volvo.com 

Stefan Börjesson  – Volvo TTI

mail: stefan.a.borjesson@volvo.com 

Morteza Haghir Chehreghani – Chalmers 

mail: morteza.chehreghani@chalmers.com 

Duration - Jan 2027 to Jun 2027

If this sounds interesting, please send your application before 25th October 2026 

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