6 papers · 1 filter
LLM-Empowered Event-Chain Driven Code Generation for ADAS in SDV systems
Nenad Petrovic, Norbert Kroth, Axel Torschmied +9
This paper presents an event-chain-driven, LLM-empowered workflow for generating validated, automotive code from natural-language requirements. A Retrieval-Augmented Generation (RA…
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…
Survey of GenAI for Automotive Software Development: From Requirements to Executable Code
Nenad Petrovic, Vahid Zolfaghari, Andre Schamschurko +10
Adoption of state-of-art Generative Artificial Intelligence (GenAI) aims to revolutionize many industrial areas by reducing the amount of human intervention needed and effort for h…
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…
Generating Automotive Code: Large Language Models for Software Development and Verification in Safety-Critical Systems
Sven Kirchner, Alois C. Knoll
Developing safety-critical automotive software presents significant challenges due to increasing system complexity and strict regulatory demands. This paper proposes a novel framew…
AUTOFRAME -- A Software-driven Integration Framework for Automotive Systems
Sven Kirchner, Nils Purschke, Chengdong Wu +3
The evolution of automotive technologies towards more integrated and sophisticated systems requires a shift from traditional distributed architectures to centralized vehicle archit…