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cs.RO2026
From Code to Road: A Vehicle-in-the-Loop and Digital Twin-Based Framework for Central Car Server Testing in Autonomous Driving
Chengdong Wu, Sven Kirchner, Nils Purschke +9
Simulation is one of the most essential parts in the development stage of automotive software. However, purely virtual simulations often struggle to accurately capture all real-wor…
cs.RO2025
DepthVision: Enabling Robust Vision-Language Models with GAN-Based LiDAR-to-RGB Synthesis for Autonomous Driving
Sven Kirchner, Nils Purschke, Ross Greer +1
Ensuring reliable autonomous operation when visual input is degraded remains a key challenge in intelligent vehicles and robotics. We present DepthVision, a multimodal framework th…
cs.RO2025
Autonomous Vehicle Lateral Control Using Deep Reinforcement Learning with MPC-PID Demonstration
Chengdong Wu, Sven Kirchner, Nils Purschke +1
The controller is one of the most important modules in the autonomous driving pipeline, ensuring the vehicle reaches its desired position. In this work, a reinforcement learning ba…