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
Neural Rendering based Urban Scene Reconstruction for Autonomous Driving
Shihao Shen, Louis Kerofsky, Varun Ravi Kumar, Senthil Yogamani
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following
Brian Yang, Huangyuan Su, Nikolaos Gkanatsios, Tsung-Wei Ke, Ayush Jain, Jeff Schneider, Katerina Fragkiadaki
Driving Everywhere with Large Language Model Policy Adaptation
Boyi Li, Yue Wang, Jiageng Mao, Boris Ivanovic, Sushant Veer, Karen Leung, Marco Pavone
Editable Scene Simulation for Autonomous Driving via Collaborative LLM-Agents
Yuxi Wei, Zi Wang, Yifan Lu, Chenxin Xu, Changxing Liu, Hao Zhao, Siheng Chen, Yanfeng Wang
Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review
Anton Kuznietsov, Balint Gyevnar, Cheng Wang, Steven Peters, Stefano V. Albrecht
Investigating Driving Interactions: A Robust Multi-Agent Simulation Framework for Autonomous Vehicles
Marc Kaufeld, Rainer Trauth, Johannes Betz
Compressing Deep Reinforcement Learning Networks with a Dynamic Structured Pruning Method for Autonomous Driving
Wensheng Su, Zhenni Li, Minrui Xu, Jiawen Kang, Dusit Niyato, Shengli Xie
Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environments
Haicheng Liao, Shangqian Liu, Yongkang Li, Zhenning Li, Chengyue Wang, Yunjian Li, Shengbo Eben Li, Chengzhong Xu
Informed Reinforcement Learning for Situation-Aware Traffic Rule Exceptions
Daniel Bogdoll, Jing Qin, Moritz Nekolla, Ahmed Abouelazm, Tim Joseph, J. Marius Zöllner
OASim: an Open and Adaptive Simulator based on Neural Rendering for Autonomous Driving
Guohang Yan, Jiahao Pi, Jianfei Guo, Zhaotong Luo, Min Dou, Nianchen Deng, Qiusheng Huang, Daocheng Fu, Licheng Wen, Pinlong Cai, Xing Gao, Xinyu Cai, Bo Zhang, Xuemeng Yang, Yeqi Bai, Hongbin Zhou, Botian Shi
Efficient and Interpretable Traffic Destination Prediction using Explainable Boosting Machines
Yasin Yousif, Jörg Müller
Improving Robustness of LiDAR-Camera Fusion Model against Weather Corruption from Fusion Strategy Perspective
Yihao Huang, Kaiyuan Yu, Qing Guo, Felix Juefei-Xu, Xiaojun Jia, Tianlin Li, Geguang Pu, Yang Liu
Evaluating the Robustness of Off-Road Autonomous Driving Segmentation against Adversarial Attacks: A Dataset-Centric analysis
Pankaj Deoli, Rohit Kumar, Axel Vierling, Karsten Berns
S-NeRF++: Autonomous Driving Simulation via Neural Reconstruction and Generation
Yurui Chen, Junge Zhang, Ziyang Xie, Wenye Li, Feihu Zhang, Jiachen Lu, Li Zhang
Multimodal-Enhanced Objectness Learner for Corner Case Detection in Autonomous Driving
Lixing Xiao, Ruixiao Shi, Xiaoyang Tang, Yi Zhou
Simulation-based Analysis of a Novel Loop-based Road Topology for Autonomous Vehicles
Stefan Ramdhan, Winnie Trandinh, Sathurshan Arulmohan, Xiayong Hu, Spencer Deevy, Victor Bandur, Vera Pantelic, Mark Lawford, Alan Wassyng
A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Driving
Rainer Trauth, Alexander Hobmeier, Johannes Betz
FRENETIX: A High-Performance and Modular Motion Planning Framework for Autonomous Driving
Rainer Trauth, Korbinian Moller, Gerald Wuersching, Johannes Betz