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
Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding
Mohammed Elhenawy, Huthaifa I. Ashqar, Andry Rakotonirainy, Taqwa I. Alhadidi, Ahmed Jaber, Mohammad Abu Tami
The global consensus on the risk management of autonomous driving
Sebastian Krügel, Matthias Uhl
Domain-Incremental Semantic Segmentation for Autonomous Driving under Adverse Driving Conditions
Shishir Muralidhara, René Schuster, Didier Stricker
CorrDiff: Adaptive Delay-aware Detector with Temporal Cue Inputs for Real-time Object Detection
Xiang Zhang, Chenchen Fu, Yufei Cui, Lan Yi, Yuyang Sun, Weiwei Wu, Xue Liu
DriVLM: Domain Adaptation of Vision-Language Models in Autonomous Driving
Xuran Zheng, Chang D. Yoo
LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models
Zengqi Peng, Yubin Wang, Xu Han, Lei Zheng, Jun Ma
CuRLA: Curriculum Learning Based Deep Reinforcement Learning for Autonomous Driving
Bhargava Uppuluri, Anjel Patel, Neil Mehta, Sridhar Kamath, Pratyush Chakraborty
AD-L-JEPA: Self-Supervised Spatial World Models with Joint Embedding Predictive Architecture for Autonomous Driving with LiDAR Data
Haoran Zhu, Zhenyuan Dong, Kristi Topollai, Anna Choromanska
Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions
Doaa Mahmud, Hadeel Hajmohamed, Shamma Almentheri, Shamma Alqaydi, Lameya Aldhaheri, Ruhul Amin Khalil, Nasir Saeed
H-MBA: Hierarchical MamBa Adaptation for Multi-Modal Video Understanding in Autonomous Driving
Siran Chen, Yuxiao Luo, Yue Ma, Yu Qiao, Yali Wang
Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives
Shaoyuan Xie, Lingdong Kong, Yuhao Dong, Chonghao Sima, Wenwei Zhang, Qi Alfred Chen, Ziwei Liu, Liang Pan
An LSTM-based Test Selection Method for Self-Driving Cars
Ali Güllü, Faiz Ali Shah, Dietmar Pfahl
Hybrid Machine Learning Model with a Constrained Action Space for Trajectory Prediction
Alexander Fertig, Lakshman Balasubramanian, Michael Botsch
SenseRAG: Constructing Environmental Knowledge Bases with Proactive Querying for LLM-Based Autonomous Driving
Xuewen Luo, Fan Ding, Fengze Yang, Yang Zhou, Junnyong Loo, Hwa Hui Tew, Chenxi Liu
Evaluating Scenario-based Decision-making for Interactive Autonomous Driving Using Rational Criteria: A Survey
Zhen Tian, Zhihao Lin, Dezong Zhao, Wenjing Zhao, David Flynn, Shuja Ansari, Chongfeng Wei
Enhancing Large Vision Model in Street Scene Semantic Understanding through Leveraging Posterior Optimization Trajectory
Wei-Bin Kou, Qingfeng Lin, Ming Tang, Shuai Wang, Rongguang Ye, Guangxu Zhu, Yik-Chung Wu