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
VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
Shaoyu Chen, Bo Jiang, Hao Gao, Bencheng Liao, Qing Xu, Qian Zhang, Chang Huang, Wenyu Liu, Xinggang Wang
Pre-trained Transformer-Enabled Strategies with Human-Guided Fine-Tuning for End-to-end Navigation of Autonomous Vehicles
Dong Hu, Chao Huang, Jingda Wu, Hongbo Gao
DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models
Xiaoyu Tian, Junru Gu, Bailin Li, Yicheng Liu, Yang Wang, Zhiyong Zhao, Kun Zhan, Peng Jia, Xianpeng Lang, Hang Zhao
Modified RRT* for Path Planning in Autonomous Driving
Sugirtha T, Pranav S, Nitin Benjamin Dasiah, Sridevi M
Exploiting T-norms for Deep Learning in Autonomous Driving
Mihaela Cătălina Stoian, Eleonora Giunchiglia, Thomas Lukasiewicz
CARLA-Autoware-Bridge: Facilitating Autonomous Driving Research with a Unified Framework for Simulation and Module Development
Gemb Kaljavesi, Tobias Kerbl, Tobias Betz, Kirill Mitkovskii, Frank Diermeyer
PC-NeRF: Parent-Child Neural Radiance Fields Using Sparse LiDAR Frames in Autonomous Driving Environments
Xiuzhong Hu, Guangming Xiong, Zheng Zang, Peng Jia, Yuxuan Han, Junyi Ma
Design and Realization of a Benchmarking Testbed for Evaluating Autonomous Platooning Algorithms
Michael Shaham, Risha Ranjan, Engin Kirda, Taskin Padir