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
Case Study: Runtime Safety Verification of Neural Network Controlled System
Frank Yang, Sinong Simon Zhan, Yixuan Wang, Chao Huang, Qi Zhu
Multilevel Graph Reinforcement Learning for Consistent Cognitive Decision-making in Heterogeneous Mixed Autonomy
Xin Gao, Zhaoyang Ma, Xueyuan Li, Xiaoqiang Meng, Zirui Li
AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Merging
Senkang Hu, Zhengru Fang, Zihan Fang, Yiqin Deng, Xianhao Chen, Yuguang Fang, Sam Kwong
DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba
Chengran Yuan, Zhanqi Zhang, Jiawei Sun, Shuo Sun, Zefan Huang, Christina Dao Wen Lee, Dongen Li, Yuhang Han, Anthony Wong, Keng Peng Tee, Marcelo H. Ang
Communication-Aware Consistent Edge Selection for Mobile Users and Autonomous Vehicles
Nazish Tahir, Ramviyas Parasuraman, Haijian Sun
Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priors
Kunkun Hao, Yonggang Luo, Wen Cui, Yuqiao Bai, Jucheng Yang, Songyang Yan, Yuxi Pan, Zijiang Yang
Cross-cultural analysis of pedestrian group behaviour influence on crossing decisions in interactions with autonomous vehicles
Sergio Martín Serrano, Óscar Méndez Blanco, Stewart Worrall, Miguel Ángel Sotelo, David Fernández-Llorca
Areas of Improvement for Autonomous Vehicles: A Machine Learning Analysis of Disengagement Reports
Tyler Ward
InScope: A New Real-world 3D Infrastructure-side Collaborative Perception Dataset for Open Traffic Scenarios
Xiaofei Zhang, Yining Li, Jinping Wang, Xiangyi Qin, Ying Shen, Zhengping Fan, Xiaojun Tan
MSMA: Multi-agent Trajectory Prediction in Connected and Autonomous Vehicle Environment with Multi-source Data Integration
Xi Chen, Rahul Bhadani, Zhanbo Sun, Larry Head
SimpleLLM4AD: An End-to-End Vision-Language Model with Graph Visual Question Answering for Autonomous Driving
Peiru Zheng, Yun Zhao, Zhan Gong, Hong Zhu, Shaohua Wu