Autonomous Driving
Autonomous driving research aims to develop vehicles capable of navigating and operating without human intervention, prioritizing safety and efficiency. Current efforts heavily focus on improving perception (using techniques like 3D Gaussian splatting and Bird's-Eye-View representations), prediction (leveraging diffusion models, transformers, and Bayesian games to handle uncertainty), and planning (employing reinforcement learning, large language models, and hierarchical approaches for decision-making). These advancements are crucial for enhancing the reliability and safety of autonomous vehicles, with significant implications for transportation systems and the broader AI community.
Papers
Incorporating Recurrent Reinforcement Learning into Model Predictive Control for Adaptive Control in Autonomous Driving
Yuan Zhang, Joschka Boedecker, Chuxuan Li, Guyue Zhou
FLYOVER: A Model-Driven Method to Generate Diverse Highway Interchanges for Autonomous Vehicle Testing
Yuan Zhou, Gengjie Lin, Yun Tang, Kairui Yang, Wei Jing, Ping Zhang, Junbo Chen, Liang Gong, Yang Liu
Street-View Image Generation from a Bird's-Eye View Layout
Alexander Swerdlow, Runsheng Xu, Bolei Zhou
Optical Flow for Autonomous Driving: Applications, Challenges and Improvements
Shihao Shen, Louis Kerofsky, Senthil Yogamani
Failure Detection for Motion Prediction of Autonomous Driving: An Uncertainty Perspective
Wenbo Shao, Yanchao Xu, Liang Peng, Jun Li, Hong Wang
How Does Traffic Environment Quantitatively Affect the Autonomous Driving Prediction?
Wenbo Shao, Yanchao Xu, Jun Li, Chen Lv, Weida Wang, Hong Wang