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
PLM-Net: Perception Latency Mitigation Network for Vision-Based Lateral Control of Autonomous Vehicles
Aws Khalil, Jaerock Kwon
Velocity Driven Vision: Asynchronous Sensor Fusion Birds Eye View Models for Autonomous Vehicles
Seamie Hayes, Sushil Sharma, Ciarán Eising
MCTS Based Dispatch of Autonomous Vehicles under Operational Constraints for Continuous Transportation
Milan Tomy, Konstantin M. Seiler, Andrew J. Hill
TRAVERSE: Traffic-Responsive Autonomous Vehicle Experience & Rare-event Simulation for Enhanced safety
Sandeep Thalapanane, Sandip Sharan Senthil Kumar, Guru Nandhan Appiya Dilipkumar Peethambari, Sourang SriHari, Laura Zheng, Julio Poveda, Ming C. Lin
Communication-Aware Reinforcement Learning for Cooperative Adaptive Cruise Control
Sicong Jiang, Seongjin Choi, Lijun Sun
Exploring Camera Encoder Designs for Autonomous Driving Perception
Barath Lakshmanan, Joshua Chen, Shiyi Lan, Maying Shen, Zhiding Yu, Jose M. Alvarez
Accelerating Online Mapping and Behavior Prediction via Direct BEV Feature Attention
Xunjiang Gu, Guanyu Song, Igor Gilitschenski, Marco Pavone, Boris Ivanovic
Robust Meta-Learning of Vehicle Yaw Rate Dynamics via Conditional Neural Processes
Lars Ullrich, Andreas Völz, Knut Graichen