Autonomous Vehicle
Autonomous vehicles (AVs) aim to achieve safe and efficient self-driven navigation, primarily focusing on robust perception, decision-making, and control in complex and unpredictable environments. Current research emphasizes improving perception through advanced sensor fusion (e.g., LiDAR, cameras, radar) and data processing techniques like deep learning and computer vision, coupled with sophisticated planning algorithms (e.g., Markov Decision Processes, behavior trees, and game theory) for safe and efficient trajectory generation. This field is significant for its potential to revolutionize transportation, enhancing safety, efficiency, and accessibility, while also driving advancements in artificial intelligence, robotics, and control systems.
Papers
Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI
Muhammet Anil Yagiz, Pedram MohajerAnsari, Mert D. Pese, Polat Goktas
Dual-AEB: Synergizing Rule-Based and Multimodal Large Language Models for Effective Emergency Braking
Wei Zhang, Pengfei Li, Junli Wang, Bingchuan Sun, Qihao Jin, Guangjun Bao, Shibo Rui, Yang Yu, Wenchao Ding, Peng Li, Yilun Chen
OPTIMA: Optimized Policy for Intelligent Multi-Agent Systems Enables Coordination-Aware Autonomous Vehicles
Rui Du, Kai Zhao, Jinlong Hou, Qiang Zhang, Peter Zhang
Overcoming Autoware-Ubuntu Incompatibility in Autonomous Driving Systems-Equipped Vehicles: Lessons Learned
Dada Zhang, Md Ruman Islam, Pei-Chi Huang, Chun-Hsing Ho
Adver-City: Open-Source Multi-Modal Dataset for Collaborative Perception Under Adverse Weather Conditions
Mateus Karvat, Sidney Givigi
Advancements in Road Lane Mapping: Comparative Fine-Tuning Analysis of Deep Learning-based Semantic Segmentation Methods Using Aerial Imagery
Xuanchen (Willow)Liu, Shuxin Qiao, Kyle Gao, Hongjie He, Michael A. Chapman, Linlin Xu, Jonathan Li