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
Improving Agent Behaviors with RL Fine-tuning for Autonomous Driving
Zhenghao Peng, Wenjie Luo, Yiren Lu, Tianyi Shen, Cole Gulino, Ari Seff, Justin Fu
DiffSSC: Semantic LiDAR Scan Completion using Denoising Diffusion Probabilistic Models
Helin Cao, Sven Behnke
DualAD: Dual-Layer Planning for Reasoning in Autonomous Driving
Dingrui Wang, Marc Kaufeld, Johannes Betz
Modular Autonomous Vehicle in Heterogeneous Traffic Flow: Modeling, Simulation, and Implication
Lanhang Ye, Toshiyuki Yamamoto
Hierarchical End-to-End Autonomous Driving: Integrating BEV Perception with Deep Reinforcement Learning
Siyi Lu, Lei He, Shengbo Eben Li, Yugong Luo, Jianqiang Wang, Keqiang Li
MemFusionMap: Working Memory Fusion for Online Vectorized HD Map Construction
Jingyu Song, Xudong Chen, Liupei Lu, Jie Li, Katherine A. Skinner
Transient Adversarial 3D Projection Attacks on Object Detection in Autonomous Driving
Ce Zhou, Qiben Yan, Sijia Liu
Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models
Alexander Popov, Alperen Degirmenci, David Wehr, Shashank Hegde, Ryan Oldja, Alexey Kamenev, Bertrand Douillard, David Nistér, Urs Muller, Ruchi Bhargava, Stan Birchfield, Nikolai Smolyanskiy
FSF-Net: Enhance 4D Occupancy Forecasting with Coarse BEV Scene Flow for Autonomous Driving
Erxin Guo, Pei An, You Yang, Qiong Liu, An-An Liu
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
Learning Multiple Probabilistic Decisions from Latent World Model in Autonomous Driving
Lingyu Xiao, Jiang-Jiang Liu, Sen Yang, Xiaofan Li, Xiaoqing Ye, Wankou Yang, Jingdong Wang
Curb Your Attention: Causal Attention Gating for Robust Trajectory Prediction in Autonomous Driving
Ehsan Ahmadi, Ray Mercurius, Soheil Alizadeh, Kasra Rezaee, Amir Rasouli
Controllable Traffic Simulation through LLM-Guided Hierarchical Chain-of-Thought Reasoning
Zhiyuan Liu, Leheng Li, Yuning Wang, Haotian Lin, Zhizhe Liu, Lei He, Jianqiang Wang
Online Adaptation of Learned Vehicle Dynamics Model with Meta-Learning Approach
Yuki Tsuchiya, Thomas Balch, Paul Drews, Guy Rosman
MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving
Xiyang Wang, Shouzheng Qi, Jieyou Zhao, Hangning Zhou, Siyu Zhang, Guoan Wang, Kai Tu, Songlin Guo, Jianbo Zhao, Jian Li, Mu Yang
An Adverse Weather-Immune Scheme with Unfolded Regularization and Foundation Model Knowledge Distillation for Street Scene Understanding
Wei-Bin Kou, Guangxu Zhu, Rongguang Ye, Shuai Wang, Qingfeng Lin, Ming Tang, Yik-Chung Wu
Will Large Language Models be a Panacea to Autonomous Driving?
Yuxuan Zhua, Shiyi Wang, Wenqing Zhong, Nianchen Shen, Yunqi Li, Siqi Wang, Zhiheng Li, Cathy Wu, Zhengbing He, Li Li
Integrated Decision Making and Trajectory Planning for Autonomous Driving Under Multimodal Uncertainties: A Bayesian Game Approach
Zhenmin Huang, Tong Li, Shaojie Shen, Jun Ma