Security for Autonomous Systems and Vehicles
Research on security challenges, vulnerability discovery, and safety enhancement for autonomous systems and vehicles powered by LLMs
Security for Autonomous Systems and Vehicles
Research on Large Language Models in Security for Autonomous Systems and Vehicles
Enhancing Autonomous Vehicle Security
LLM-Powered Detection of Critical Driving Vulnerabilities
Making Self-Driving Cars Safer in Edge Cases
Vision-Language Models for Handling Rare but Critical Driving Scenarios
Smart Collision Avoidance for Autonomous Vehicles
Integrating LLMs for Ethical and Context-Aware Decision-Making
Predicting Human Behavior with AI
Using Multimodal LLMs for Context-Aware Human Behavior Prediction
Safe AI-Generated Code for Autonomous Driving
Enhancing LLM-based code generation through simulation verification
LetsPi: Safer Trajectory Planning
Physics-informed and knowledge-driven LLMs for autonomous navigation safety
MovSAM: Single-Image Moving Object Detection
Novel approach for detecting motion from static images
Smarter Vehicle Classification with AI
Using Vision-Language Models to Enhance LiDAR-based Safety Systems
LangTraj: Revolutionizing Autonomous Vehicle Testing
Natural language control for realistic traffic simulations
