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
A Universal Multi-Vehicle Cooperative Decision-Making Approach in Structured Roads by Mixed-Integer Potential Game
Chengzhen Meng, Zhenmin Huang, Jun Ma
Efficient Motion Prediction: A Lightweight & Accurate Trajectory Prediction Model With Fast Training and Inference Speed
Alexander Prutsch, Horst Bischof, Horst Possegger
Intention-based and Risk-Aware Trajectory Prediction for Autonomous Driving in Complex Traffic Scenarios
Wen Wei, Jiankun Wang
A Computer Vision Approach for Autonomous Cars to Drive Safe at Construction Zone
Abu Shad Ahammed, Md Shahi Amran Hossain, Roman Obermaisser
Dynamic Game-Theoretical Decision-Making Framework for Vehicle-Pedestrian Interaction with Human Bounded Rationality
Meiting Dang, Dezong Zhao, Yafei Wang, Chongfeng Wei
Social impact of CAVs -- coexistence of machines and humans in the context of route choice
Grzegorz Jamróz, Ahmet Onur Akman, Anastasia Psarou, Zoltán Györgi Varga, Rafał Kucharski
VCAT: Vulnerability-aware and Curiosity-driven Adversarial Training for Enhancing Autonomous Vehicle Robustness
Xuan Cai, Zhiyong Cui, Xuesong Bai, Ruimin Ke, Zhenshu Ma, Haiyang Yu, Yilong Ren
Safety Verification and Navigation for Autonomous Vehicles based on Signal Temporal Logic Constraints
Aditya Parameshwaran, Yue Wang
Realistic Extreme Behavior Generation for Improved AV Testing
Robert Dyro, Matthew Foutter, Ruolin Li, Luigi Di Lillo, Edward Schmerling, Xilin Zhou, Marco Pavone
Safe and Real-Time Consistent Planning for Autonomous Vehicles in Partially Observed Environments via Parallel Consensus Optimization
Lei Zheng, Rui Yang, Minzhe Zheng, Michael Yu Wang, Jun Ma
Maneuver Decision-Making with Trajectory Streams Prediction for Autonomous Vehicles
Mais Jamal, Aleksandr Panov
Motion Forecasting via Model-Based Risk Minimization
Aron Distelzweig, Eitan Kosman, Andreas Look, Faris Janjoš, Denesh K. Manivannan, Abhinav Valada
A Comprehensive Survey of PID and Pure Pursuit Control Algorithms for Autonomous Vehicle Navigation
Harshit Jain, Priyal Babel
Risk-Aware Autonomous Driving for Linear Temporal Logic Specifications
Shuhao Qi, Zengjie Zhang, Zhiyong Sun, Sofie Haesaert
Traffic Scene Generation from Natural Language Description for Autonomous Vehicles with Large Language Model
Bo-Kai Ruan, Hao-Tang Tsui, Yung-Hui Li, Hong-Han Shuai