artificial intelligence
Our Direction
We are strengthening our Artificial Intelligence and Machine Learning capabilities to enhance engineering intelligence, validation efficiency, and in-vehicle decision systems, aligned with software-defined vehicles and next-generation intelligent platforms.
Core AI / ML Foundations
- Focus: Explainable, robust, and automotive-grade intelligence for diagnostics, monitoring, and decision logic.
Technical competencies
- Supervised Learning: Random Forest, Adaptive Random Forest (concept-drift aware)
- Reinforcement Learning: Proximal Policy Optimization (PPO) for policy-based decision making
- Statistical Monitoring: EWMA, CUSUM for anomaly and change detection
- Deep Learning (research & prototyping): ANN, RNN, LSTM for temporal and sequential data modeling
Engineering value:
- Reliable fault detection and behavioral analysis
- Adaptive decision logic under dynamic conditions
- AI models suitable for real-time and safety-aware environments
Language & Agentic AI Systems
- Focus: Knowledge-driven automation and autonomous reasoning for engineering and in-vehicle intelligence.
Technical competencies
- Language & Text AI: LLM-based code and document synthesis, domain-grounded reasoning using RAG
- Agentic AI: Goal-driven, tool-using AI systems (LangChain-based), multi-agent collaboration, human-in-the- loop control
- Multimodal Intelligence: Text, code, and structured data understanding and generation
Engineering value:
- Accelerated development and validation workflows, Context-aware reasoning and decision support
- Foundation for adaptive infotainment, diagnostics, and HMI systems