# Paper Collection We list key challenges from a wide span of candidate concerns, as well as trending methodologies. - [Survey](#survey) - [Language / VLM for Driving](#language--vlm-for-driving) - [Review for VLM in Driving](#review-for-vlm-in-driving) - [Papers for VLM in Driving](#papers-for-vlm-in-driving) - [World Model & Model-based RL](#world-model--model-based-rl) - [Multi-sensor Fusion](#multi-sensor-fusion) - [Multi-task Learning](#multi-task-learning) - [Interpretability](#interpretability) - [Review for Interpretability](#review-for-interpretability) - [Attention Visualization](#attention-visualization) - [Interpretable Tasks](#interpretable-tasks) - [Cost Learning](#cost-learning) - [Linguistic Explainability](#linguistic-explainability) - [Uncertainty Modeling](#uncertainty-modeling) - [Counterfactual Explanations and Causal Inference](#counterfactual-explanations-and-causal-inference) - [Visual Abstraction / Representation Learning](#visual-abstraction--representation-learning) - [Policy Distillation](#policy-distillation) - [Causal Confusion](#causal-confusion) - [Robustness](#robustness) - [Long-tailed Distribution](#long-tailed-distribution) - [Covariate Shift](#covariate-shift) - [Domain Adaptation](#domain-adaptation) - [Affordance Learning](#affordance-learning) - [BEV](#bev) - [Transformer](#transformer) - [V2V Cooperative](#v2v-cooperative) - [Distributed RL](#distributed-rl) - [Data-driven Simulation](#data-driven-simulation) - [Parameter Initialization](#parameter-initialization) - [Traffic Simulation](#traffic-simulation) - [Sensor Simulation](#sensor-simulation) ## Survey - End-to-End Autonomous Driving: Challenges and Frontiers [[TPAMI2024]](https://arxiv.org/abs/2306.16927) - Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey [[TIV2023]](https://ieeexplore.ieee.org/abstract/document/10258330) - Rethinking Integration of Prediction and Planning in Deep Learning-Based Automated Driving Systems: A Review [[arXiv2023]](https://arxiv.org/abs/2308.05731) - End-to-end Autonomous Driving using Deep Learning: A Systematic Review [[arXiv2023]](https://arxiv.org/abs/2311.18636) - Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives [[TIV2023]](https://arxiv.org/abs/2303.09824) - Imitation Learning: Progress, Taxonomies and Challenges [[TNNLS2022]](https://arxiv.org/abs/2106.12177) - A Review of End-to-End Autonomous Driving in Urban Environments [[Access2022]](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9832636) - A Survey on Imitation Learning Techniques for End-to-End Autonomous Vehicles [[TITS2022]](https://arxiv.org/abs/2101.01993) - Deep Reinforcement Learning for Autonomous Driving: A Survey [[TITS2021]](https://arxiv.org/abs/2002.00444) - A Survey of Deep RL and IL for Autonomous Driving Policy Learning [[TITS2021]](https://arxiv.org/abs/2101.01993) - A Survey of End-to-End Driving: Architectures and Training Methods [[TNNLS2020]](https://arxiv.org/abs/2003.06404) - Learning to Drive by Imitation: An Overview of Deep Behavior Cloning Methods [[TIV2020]](https://ieeexplore.ieee.org/abstract/document/9117169) - Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art [[book]](https://arxiv.org/abs/1704.05519)

(back to top)

## Language / VLM for Driving ### Review for VLM in Driving - Vision Language Models in Autonomous Driving: A Survey and Outlook [[TIV2024]](https://arxiv.org/abs/2310.14414)[[Code]](https://github.com/ge25nab/Awesome-VLM-AD-ITS) - A Survey on Multimodal Large Language Models for Autonomous Driving [[WACVWorkshop2024]](https://openaccess.thecvf.com/content/WACV2024W/LLVM-AD/html/Cui_A_Survey_on_Multimodal_Large_Language_Models_for_Autonomous_Driving_WACVW_2024_paper.html) - Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities [[arXiv2024]](https://arxiv.org/abs/2401.08045)[[Code]](https://github.com/zhanghm1995/Forge_VFM4AD) - LLM4Drive: A Survey of Large Language Models for Autonomous Driving [[arXiv2023]](https://arxiv.org/abs/2311.01043) ### Papers for VLM in Driving - DriveLM: Driving with Graph Visual Question Answering [[ECCV2024]](https://arxiv.org/abs/2312.14150)[[Code]](https://github.com/OpenDriveLab/DriveLM) - Reason2Drive: Towards Interpretable and Chain-based Reasoning for Autonomous Driving [[ECCV2024]](https://arxiv.org/abs/2312.03661)[[Code]](https://github.com/fudan-zvg/reason2drive) - Asynchronous Large Language Model Enhanced Planner for Autonomous Driving [[ECCV2024]](https://arxiv.org/abs/2406.14556)[[Code]](https://github.com/memberRE/AsyncDriver) - LMDrive: Closed-Loop End-to-End Driving with Large Language Models [[CVPR2024]](https://arxiv.org/abs/2312.07488)[[Code]](https://github.com/opendilab/LMDrive) - Driving Everywhere with Large Language Model Policy Adaptation [[CVPR2024]](https://arxiv.org/abs/2402.05932)[[Code]](https://github.com/Boyiliee/LLaDA-AV) - VLP: Vision Language Planning for Autonomous Driving [[CVPR2024]](https://arxiv.org/abs/2401.05577) - A Language Agent for Autonomous Driving [[COLM2024]](https://arxiv.org/abs/2311.10813)[[Code]](https://github.com/USC-GVL/Agent-Driver) - DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model [[RAL2024]](https://arxiv.org/abs/2310.01412) - Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving [[ICRA2024]](https://browse.arxiv.org/abs/2310.01957)[[Code]](https://github.com/wayveai/Driving-with-LLMs) - Prompting Multi-Modal Tokens to Enhance End-to-End Autonomous Driving Imitation Learning with LLMs [[ICRA2024]](https://arxiv.org/abs/2404.04869) - DriVLMe: Enhancing LLM-based Autonomous Driving Agents with Embodied and Social Experiences [[IROS2024]](https://arxiv.org/abs/2406.03008) - Pix2Planning: End-to-End Planning by Vision-language Model for Autonomous Driving on Carla Simulator [[IV2024]](https://ieeexplore.ieee.org/abstract/document/10588479) - LangProp: A code optimization framework using Large Language Models applied to driving [[ICLRWorkshop2024]](https://arxiv.org/abs/2401.10314)[[Code]](https://github.com/shuishida/LangProp) - SimpleLLM4AD: An End-to-End Vision-Language Model with Graph Visual Question Answering for Autonomous Driving [[arXiv2024]](https://arxiv.org/abs/2407.21293) - An LLM-enhanced Multi-objective Evolutionary Search for Autonomous Driving Test Scenario Generation [[arXiv2024]](https://arxiv.org/abs/2406.10857) - OmniDrive: A Holistic LLM-Agent Framework for Autonomous Driving with 3D Perception, Reasoning and Planning [[arXiv2024]](https://arxiv.org/abs/2405.01533)[[Code]](https://github.com/NVlabs/OmniDrive) - Continuously Learning, Adapting, and Improving: A Dual-Process Approach to Autonomous Driving [[arXiv2024]](https://arxiv.org/abs/2405.15324)[[Code]](https://github.com/PJLab-ADG/LeapAD) - Is a 3D-Tokenized LLM the Key to Reliable Autonomous Driving? [[arXiv2024]](https://arxiv.org/abs/2405.18361) - DriveCoT: Integrating Chain-of-Thought Reasoning with End-to-End Driving [[arXiv2024]](https://arxiv.org/abs/2403.16996) - RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model [[arXiv2024]](https://arxiv.org/abs/2402.10828) - DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models [[arXiv2024]](https://arxiv.org/abs/2402.12289) - Hybrid Reasoning Based on Large Language Models for Autonomous Car Driving [[arXiv2024]](https://arxiv.org/abs/2402.13602) - DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving [[arXiv2024]](https://arxiv.org/abs/2401.03641) - LingoQA: Video Question Answering for Autonomous Driving [[arXiv2023]](https://arxiv.org/abs/2312.14115)[[Code]](https://github.com/wayveai/LingoQA/) - Dolphins: Multimodal Language Model for Driving [[arXiv2023]](https://arxiv.org/abs/2312.00438)[[Code]](https://github.com/SaFoLab-WISC/Dolphins) - GPT-Driver: Learning to Drive with GPT [[arXiv2023]](https://arxiv.org/abs/2310.01415) - Language Prompt for Autonomous Driving [[arXiv2023]](https://arxiv.org/abs/2309.04379)[[Code]](https://github.com/wudongming97/Prompt4Driving) - DOROTHIE: Spoken Dialogue for Handling Unexpected Situations in Interactive Autonomous Driving Agents [[EMNLP2022(Findings)]](https://arxiv.org/abs/2210.12511)[[Code]](https://github.com/sled-group/DOROTHIE) - LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action [[CoRL2022]](https://proceedings.mlr.press/v205/shah23b.html) - Ground then Navigate: Language-guided Navigation in Dynamic Scenes [[arXiv2022]](https://arxiv.org/abs/2209.11972) - Generating Landmark Navigation Instructions from Maps as a Graph-to-Text Problem [[ACL2021]](https://arxiv.org/abs/2012.15329) - Advisable Learning for Self-Driving Vehicles by Internalizing Observation-to-Action Rules [[CVPR2020]](https://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Advisable_Learning_for_Self-Driving_Vehicles_by_Internalizing_Observation-to-Action_Rules_CVPR_2020_paper.html) - Conditional Driving from Natural Language Instructions [[CoRL2019]](https://arxiv.org/abs/1910.07615) - Grounding Human-to-Vehicle Advice for Self-driving Vehicles [[CVPR2019]](https://arxiv.org/abs/1911.06978)[[Dataset]](https://usa.honda-ri.com/had) - Talk to the Vehicle: Language Conditioned Autonomous Navigation of Self Driving Cars [[IROS2019]](https://ieeexplore.ieee.org/abstract/document/8967929) - Talk2Car: Taking Control of Your Self-Driving Car [[EMNLP2019]](https://arxiv.org/abs/1909.10838) - TOUCHDOWN: Natural Language Navigation and Spatial Reasoning in Visual Street Environments [[CVPR2019]](https://openaccess.thecvf.com/content_CVPR_2019/html/Chen_TOUCHDOWN_Natural_Language_Navigation_and_Spatial_Reasoning_in_Visual_Street_CVPR_2019_paper.html) - Learning to Navigate in Cities Without a Map [[NeurIPS2018]](https://proceedings.neurips.cc/paper_files/paper/2018/hash/e034fb6b66aacc1d48f445ddfb08da98-Abstract.html)[[Code]](https://github.com/deepmind/streetlearn)

(back to top)

## World Model & Model-based RL - Think2Drive: Efficient Reinforcement Learning by Thinking in Latent World Model for Quasi-Realistic Autonomous Driving (in CARLA-v2) [[ECCV2024]](https://arxiv.org/abs/2402.16720) - WoVoGen: World Volume-aware Diffusion for Controllable Multi-camera Driving Scene Generation [[ECCV2024]](https://arxiv.org/abs/2312.02934)[[Code]](https://github.com/fudan-zvg/WoVoGen) - OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving [[ECCV2024]](https://arxiv.org/abs/2311.16038)[[Code]](https://github.com/wzzheng/OccWorld) - Visual Point Cloud Forecasting enables Scalable Autonomous Driving [[CVPR2024]](https://arxiv.org/abs/2312.17655)[[Code]](https://github.com/OpenDriveLab/ViDAR) - GenAD: Generalized Predictive Model for Autonomous Driving [[CVPR2024]](https://arxiv.org/abs/2403.09630)[[Code]](https://github.com/OpenDriveLab/DriveAGI) - DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving [[CVPR2024]](https://arxiv.org/abs/2405.04390) - Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability [[arXiv2024]](https://arxiv.org/abs/2405.17398)[[Code]](https://github.com/OpenDriveLab/Vista) - Enhancing End-to-End Autonomous Driving with Latent World Model [[arXiv2024]](https://arxiv.org/abs/2406.08481)[[Code]](https://github.com/BraveGroup/LAW) - BEVWorld: A Multimodal World Model for Autonomous Driving via Unified BEV Latent Space [[arXiv2024]](https://arxiv.org/abs/2407.05679)[[Code]](https://github.com/zympsyche/BevWorld) - Unleashing Generalization of End-to-End Autonomous Driving with Controllable Long Video Generation [[arXiv2024]](https://arxiv.org/abs/2406.01349)[[Code]](https://github.com/westlake-autolab/Delphi) - DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation [[arXiv2024]](https://arxiv.org/abs/2403.06845)[[Code]](https://github.com/f1yfisher/DriveDreamer2) - CarDreamer: Open-Source Learning Platform for World Model based Autonomous Driving [[arXiv2024]](https://arxiv.org/abs/2405.09111)[[Code]](https://github.com/ucd-dare/CarDreamer) - GAIA-1: A Generative World Model for Autonomous Driving [[arXiv2023]](https://arxiv.org/abs/2309.17080) - ADriver-I: A General World Model for Autonomous Driving [[arXiv2023]](https://arxiv.org/abs/2311.13549) - DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving [[arXiv2023]](https://arxiv.org/abs/2309.09777)[[Code]](https://github.com/JeffWang987/DriveDreamer) - Uncertainty-Aware Model-Based Offline Reinforcement Learning for Automated Driving [[RAL2023]](https://ieeexplore.ieee.org/document/10015868) - Model-Based Imitation Learning for Urban Driving [[NeurIPS2022)]](https://arxiv.org/abs/2210.07729)[[Code]](https://github.com/wayveai/mile.git) - Iso-Dream: Isolating and Leveraging Noncontrollable Visual Dynamics in World Models [[NeurIPS2022]](http://arxiv.org/pdf/2205.13817v3)[[Code]](https://github.com/panmt/Iso-Dream.git) - Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model [[NeurIPSWorkshop2022]](https://arxiv.org/abs/2210.04017) - Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning [[ICML2022]](https://arxiv.org/abs/2207.10295) - Interpretable End-to-End Urban Autonomous Driving With Latent Deep Reinforcement Learning [[TITS2022]](https://arxiv.org/abs/2001.08726)[[Code]](https://github.com/cjy1992/interp-e2e-driving.git) - Learning To Drive From a World on Rails [[ICCV2021]](http://arxiv.org/pdf/2105.00636v3)[[Code]](https://github.com/dotchen/WorldOnRails.git) - Uncertainty-Aware Model-Based Reinforcement Learning: Methodology and Application in Autonomous Driving [[IV2022]](https://ieeexplore.ieee.org/abstract/document/9802913) - UMBRELLA: Uncertainty-Aware Model-Based Offline Reinforcement Learning Leveraging Planning [[NeurIPSWorkshop2021]](https://arxiv.org/pdf/2111.11097.pdf) - Deductive Reinforcement Learning for Visual Autonomous Urban Driving Navigation [[TNNLS2021]](https://ieeexplore.ieee.org/document/9537641) - Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic [[ICLR2019]](https://arxiv.org/abs/1901.02705)

(back to top)

## Multi-sensor Fusion - DualAT: Dual Attention Transformer for End-to-End Autonomous Driving [[ICRA2024]](https://ieeexplore.ieee.org/abstract/document/10610334) - DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba [[arXiv2024]](https://arxiv.org/abs/2408.03601)[[Code]](https://github.com/Chengran-Yuan/DRAMA) - MaskFuser: Masked Fusion of Joint Multi-Modal Tokenization for End-to-End Autonomous Driving [[arXiv2024]](https://arxiv.org/abs/2405.07573) - M2DA: Multi-Modal Fusion Transformer Incorporating Driver Attention for Autonomous Driving [[arXiv2024]](https://arxiv.org/abs/2403.12552) - Utilizing Navigation Paths to Generate Target Points for Enhanced End-to-End Autonomous Driving Planning [[arXiv2024]](https://arxiv.org/abs/2406.08349) - Hidden Biases of End-to-End Driving Models [[ICCV2023]](https://arxiv.org/abs/2306.07957)[[Code]](https://github.com/autonomousvision/carla_garage) - Learning to Drive Anywhere [[CoRL2023]](https://arxiv.org/abs/2309.12295) - Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving [[CVPR2023]](https://arxiv.org/abs/2305.06242)[[Code]](https://github.com/OpenDriveLab/ThinkTwice) - ReasonNet: End-to-End Driving with Temporal and Global Reasoning [[CVPR2023]](https://arxiv.org/abs/2305.10507) - Scaling Vision-Based End-to-End Autonomous Driving with Multi-View Attention Learning [[IROS2023]](https://ieeexplore.ieee.org/abstract/document/10341506) - FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving [[arXiv2023]](https://arxiv.org/abs/2308.01006) - Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model [[NeurIPSWorkshop2022]](https://arxiv.org/abs/2210.04017) - End-to-end Autonomous Driving with Semantic Depth Cloud Mapping and Multi-agent [[IV2022]](https://arxiv.org/abs/2204.05513) - MMFN: Multi-Modal-Fusion-Net for End-to-End Driving [[IROS2022]](https://arxiv.org/abs/2207.00186)[[Code]](https://github.com/Kin-Zhang/mmfn) - Interpretable End-to-End Urban Autonomous Driving With Latent Deep Reinforcement Learning [[TITS2022]](https://arxiv.org/abs/2001.08726)[[Code]](https://github.com/cjy1992/interp-e2e-driving.git) - Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer [[CoRL2022]](https://arxiv.org/abs/2207.14024)[[Code]](https://github.com/opendilab/InterFuser) - Learning from All Vehicles [[CVPR2022]](http://arxiv.org/pdf/1709.04622v4)[[Code]](https://github.com/dotchen/LAV.git) - TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving [[TPAMI2022]](https://arxiv.org/abs/2205.15997)[[Code]](https://github.com/autonomousvision/transfuser.git) - Multi-Modal Fusion Transformer for End-to-End Autonomous Driving [[CVPR2021]](https://arxiv.org/abs/2104.09224)[[Code]](https://github.com/autonomousvision/transfuser.git) - Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement Learning [[arXiv2021]](https://arxiv.org/abs/2109.08473) - Multi-modal Sensor Fusion-based Deep Neural Network for End-to-end Autonomous Driving with Scene Understanding [[IEEESJ2020]](https://arxiv.org/abs/2005.09202) - Probabilistic End-to-End Vehicle Navigation in Complex Dynamic Environments With Multimodal Sensor Fusion [[RAL2020]](https://arxiv.org/abs/2005.01935) - Multimodal End-to-End Autonomous Driving [[TITS2020]](https://ieeexplore.ieee.org/abstract/document/9165167) - End-To-End Interpretable Neural Motion Planner [[CVPR2019]](https://openaccess.thecvf.com/content_CVPR_2019/html/Zeng_End-To-End_Interpretable_Neural_Motion_Planner_CVPR_2019_paper.html) - Does Computer Vision Matter for Action? [[ScienceRobotics2019]](https://www.science.org/doi/abs/10.1126/scirobotics.aaw6661) - End-To-End Multi-Modal Sensors Fusion System For Urban Automated Driving [[NeurIPSWorkshop2018]](https://openreview.net/forum?id=Byx4Xkqjcm) - MultiNet: Multi-Modal Multi-Task Learning for Autonomous Driving [[WACV2019]](https://arxiv.org/abs/1709.05581) - LiDAR-Video Driving Dataset: Learning Driving Policies Effectively [[CVPR2018]](https://openaccess.thecvf.com/content_cvpr_2018/html/Chen_LiDAR-Video_Driving_Dataset_CVPR_2018_paper.html)

(back to top)

## Multi-task Learning - PARA-Drive: Parallelized Architecture for Real-time Autonomous Driving [[CVPR2024]](https://openaccess.thecvf.com/content/CVPR2024/html/Weng_PARA-Drive_Parallelized_Architecture_for_Real-time_Autonomous_Driving_CVPR_2024_paper.html) - Planning-oriented Autonomous Driving [[CVPR2023]](https://arxiv.org/abs/2212.10156)[[Code]](https://github.com/OpenDriveLab/UniAD) - Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving [[CVPR2023]](https://arxiv.org/abs/2305.06242)[[Code]](https://github.com/OpenDriveLab/ThinkTwice) - Coaching a Teachable Student [[CVPR2023]](https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Coaching_a_Teachable_Student_CVPR_2023_paper.html) - ReasonNet: End-to-End Driving with Temporal and Global Reasoning [[CVPR2023]](https://arxiv.org/abs/2305.10507) - Hidden Biases of End-to-End Driving Models [[ICCV2023]](https://arxiv.org/abs/2306.07957)[[Code]](https://github.com/autonomousvision/carla_garage) - VAD: Vectorized Scene Representation for Efficient Autonomous Driving [[ICCV2023]](https://arxiv.org/abs/2303.12077)[[Code]](https://github.com/hustvl/VAD) - TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving [[TPAMI2022]](https://arxiv.org/abs/2205.15997)[[Code]](https://github.com/autonomousvision/transfuser.git) - Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline [[NeurIPS2022]](https://arxiv.org/abs/2206.08129) [[Code]](https://github.com/OpenDriveLab/TCP) - Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer [[CoRL2022]](https://arxiv.org/abs/2207.14024)[[Code]](https://github.com/opendilab/InterFuser) - Learning from All Vehicles [[CVPR2022]](http://arxiv.org/pdf/1709.04622v4)[[Code]](https://github.com/dotchen/LAV.git) - Multi-Task Learning With Attention for End-to-End Autonomous Driving [[CVPRWorkshop2021]](https://arxiv.org/abs/2104.10753) - NEAT: Neural Attention Fields for End-to-End Autonomous Driving [[ICCV2021]](https://arxiv.org/abs/2109.04456)[[Code]](https://github.com/autonomousvision/neat.git) - SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning [[CoRL2020]](https://arxiv.org/abs/1912.02973)[[Code]](https://github.com/twsq/sam-driving.git) - Urban Driving with Conditional Imitation Learning [[ICRA2020]](http://arxiv.org/pdf/1912.00177v2) - Multi-modal Sensor Fusion-based Deep Neural Network for End-to-end Autonomous Driving with Scene Understanding [[IEEESJ2020]](https://arxiv.org/abs/2005.09202) - Multi-task Learning with Future States for Vision-based Autonomous Driving [[ACCV2020]](https://openaccess.thecvf.com/content/ACCV2020/papers/Kim_Multi-task_Learning_with_Future_States_for_Vision-based_Autonomous_Driving_ACCV_2020_paper.pdf) - Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks [[AAAI2019]](http://arxiv.org/pdf/1811.02759v1)[[Code]](https://github.com/cardwing/Codes-for-Steering-Control.git) - MultiNet: Multi-Modal Multi-Task Learning for Autonomous Driving [[WACV2019]](https://arxiv.org/abs/1709.05581) - Intentnet: Learning to Predict Intention from Raw Sensor Data [[CoRL2018]](https://arxiv.org/abs/2101.07907) - Rethinking Self-driving: Multi-task Knowledge for Better Generalization and Accident Explanation Ability [[arXiv2018]](https://arxiv.org/abs/1809.11100)[[Code]](https://github.com/jackspp/rethinking-self-driving.git) - Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision [[ICVGIP2018]](https://arxiv.org/abs/1808.10393) - End-to-end Learning of Driving Models from Large-scale Video Datasets [[CVPR2017]](https://arxiv.org/abs/1612.01079)[[Code]](https://github.com/gy20073/BDD_Driving_Model.git)

(back to top)

## Interpretability ### Review for Interpretability - Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review [[arXiv2024]](https://arxiv.org/abs/2402.10086) - Explainability of Deep Vision-based Autonomous Driving Systems: Review and challenges [[IJCV2022]](https://arxiv.org/abs/2101.05307) ### Attention Visualization - Guiding Attention in End-to-End Driving Models [[IV2024]](https://arxiv.org/abs/2405.00242) - Scaling Self-Supervised End-to-End Driving with Multi-View Attention Learning [[arxiv2023]](https://arxiv.org/abs/2302.03198) - PlanT: Explainable Planning Transformers via Object-Level Representations [[CoRL2022]](https://arxiv.org/abs/2210.14222)[[Code]](https://github.com/autonomousvision/plant) - MMFN: Multi-Modal-Fusion-Net for End-to-End Driving [[IROS2022]](https://arxiv.org/abs/2207.00186)[[Code]](https://github.com/Kin-Zhang/mmfn) - TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving [[TPAMI2022]](https://arxiv.org/abs/2205.15997)[[Code]](https://github.com/autonomousvision/transfuser.git) - Multi-Modal Fusion Transformer for End-to-End Autonomous Driving [[CVPR2021]](https://arxiv.org/abs/2104.09224)[[Code]](https://github.com/autonomousvision/transfuser.git) - Multi-Task Learning With Attention for End-to-End Autonomous Driving [[CVPRWorkshop2021]](https://arxiv.org/abs/2104.10753) - NEAT: Neural Attention Fields for End-to-End Autonomous Driving [[ICCV2021]](https://arxiv.org/abs/2109.04456)[[Code]](https://github.com/autonomousvision/neat.git) - Explaining Autonomous Driving by Learning End-to-End Visual Attention [[CVPRWorkshop2020]](https://openaccess.thecvf.com/content_CVPRW_2020/html/w20/Cultrera_Explaining_Autonomous_Driving_by_Learning_End-to-End_Visual_Attention_CVPRW_2020_paper.html) - Visual Explanation by Attention Branch Network for End-to-end Learning-based Self-driving [[IV2019]](https://ieeexplore.ieee.org/abstract/document/8813900) - Deep Object-Centric Policies for Autonomous Driving [[ICRA2019]](https://ieeexplore.ieee.org/abstract/document/8794224) - Textual Explanations for Self-Driving Vehicles [[ECCV2018]](https://openaccess.thecvf.com/content_ECCV_2018/html/Jinkyu_Kim_Textual_Explanations_for_ECCV_2018_paper.html)[[Code]](https://github.com/JinkyuKimUCB/explainable-deep-driving) - Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision [[ICVGIP2018]](https://arxiv.org/abs/1808.10393) - Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention [[ICCV2017]](https://openaccess.thecvf.com/content_iccv_2017/html/Kim_Interpretable_Learning_for_ICCV_2017_paper.html)

(back to top)

### Interpretable Tasks - Planning-oriented Autonomous Driving [[CVPR2023]](https://arxiv.org/abs/2212.10156)[[Code]](https://github.com/OpenDriveLab/UniAD) - Hidden Biases of End-to-End Driving Models [[ICCV2023]](https://arxiv.org/abs/2306.07957)[[Code]](https://github.com/autonomousvision/carla_garage) - VAD: Vectorized Scene Representation for Efficient Autonomous Driving [[ICCV2023]](https://arxiv.org/abs/2303.12077)[[Code]](https://github.com/hustvl/VAD) - Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer [[CoRL2022]](https://arxiv.org/abs/2207.14024)[[Code]](https://github.com/opendilab/InterFuser) - TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving [[TPAMI2022]](https://arxiv.org/abs/2205.15997)[[Code]](https://github.com/autonomousvision/transfuser.git) - Learning from All Vehicles [[CVPR2022]](http://arxiv.org/pdf/1709.04622v4)[[Code]](https://github.com/dotchen/LAV.git) - Ground then Navigate: Language-guided Navigation in Dynamic Scenes [[arXiv2022]](https://arxiv.org/abs/2209.11972) - NEAT: Neural Attention Fields for End-to-End Autonomous Driving [[ICCV2021]](https://arxiv.org/abs/2109.04456)[[Code]](https://github.com/autonomousvision/neat.git) - Multi-Task Learning With Attention for End-to-End Autonomous Driving [[CVPRWorkshop2021]](https://arxiv.org/abs/2104.10753) - Urban Driving with Conditional Imitation Learning [[ICRA2020]](http://arxiv.org/pdf/1912.00177v2) - Using Eye Gaze to Enhance Generalization of Imitation Networks to Unseen Environments [[TNNLS2020]](https://www.ram-lab.com/papers/2020/liu2020tnnls.pdf) - Multi-modal Sensor Fusion-based Deep Neural Network for End-to-end Autonomous Driving with Scene Understanding [[IEEESJ2020]](https://arxiv.org/abs/2005.09202) - Rethinking Self-driving: Multi-task Knowledge for Better Generalization and Accident Explanation Ability [[arXiv2018]](https://arxiv.org/abs/1809.11100)[[Code]](https://github.com/jackspp/rethinking-self-driving.git) - Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision [[ICVGIP2018]](https://arxiv.org/abs/1808.10393) - End-to-end Learning of Driving Models from Large-scale Video Datasets [[CVPR2017]](https://arxiv.org/abs/1612.01079)[[Code]](https://github.com/gy20073/BDD_Driving_Model.git)

(back to top)

### Cost Learning - QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving [[ICRA2024]](https://arxiv.org/abs/2404.01486) - ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning [[ECCV2022]](https://arxiv.org/abs/2207.07601)[[Code]](https://github.com/OpenDriveLab/ST-P3) - Differentiable Raycasting for Self-Supervised Occupancy Forecasting [[ECCV2022]](https://arxiv.org/abs/2210.01917)[[Code]](https://github.com/tarashakhurana/emergent-occ-forecasting.git) - MP3: A Unified Model To Map, Perceive, Predict and Plan [[CVPR2021]](https://arxiv.org/abs/2101.06806) - Safe Local Motion Planning With Self-Supervised Freespace Forecasting [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/html/Hu_Safe_Local_Motion_Planning_With_Self-Supervised_Freespace_Forecasting_CVPR_2021_paper.html) - LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving [[ICCV2021]](https://arxiv.org/abs/2101.06547) - DSDNet: Deep Structured Self-driving Network [[ECCV2020]](https://arxiv.org/abs/2008.06041) - Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations [[ECCV2020]](https://arxiv.org/abs/2008.05930) - End-To-End Interpretable Neural Motion Planner [[CVPR2019]](https://openaccess.thecvf.com/content_CVPR_2019/html/Zeng_End-To-End_Interpretable_Neural_Motion_Planner_CVPR_2019_paper.html)

(back to top)

### Linguistic Explainability - ADAPT: Action-aware Driving Caption Transformer [[ICRA2023]](https://arxiv.org/abs/2302.00673)[[Code]](https://github.com/jxbbb/ADAPT) - Driving Behavior Explanation with Multi-level Fusion [[PR2022]](https://www.sciencedirect.com/science/article/abs/pii/S0031320321005975)[[Code]](https://github.com/valeoai/BEEF) - Explainable Object-Induced Action Decision for Autonomous Vehicles [[CVPR2020]](https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Explainable_Object-Induced_Action_Decision_for_Autonomous_Vehicles_CVPR_2020_paper.html) - Textual Explanations for Self-Driving Vehicles [[ECCV2018]](https://openaccess.thecvf.com/content_ECCV_2018/html/Jinkyu_Kim_Textual_Explanations_for_ECCV_2018_paper.html)[[Code]](https://github.com/JinkyuKimUCB/explainable-deep-driving)

(back to top)

### Uncertainty Modeling - UAP-BEV: Uncertainty Aware Planning using Bird's Eye View generated from Surround Monocular Images [[CASE2023]](https://arxiv.org/abs/2306.04939)[[Code]](https://github.com/Vikr-182/UAP-BEV) - Probabilistic End-to-End Vehicle Navigation in Complex Dynamic Environments With Multimodal Sensor Fusion [[RAL2020]](https://arxiv.org/abs/2005.01935) - Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? [[ICML2020]](https://arxiv.org/abs/2006.14911)[[Code]](https://github.com/OATML/oatomobile.git) - VTGNet: A Vision-Based Trajectory Generation Network for Autonomous Vehicles in Urban Environments [[TIV2020]](https://arxiv.org/abs/2004.12591)[[Code]](https://github.com/caipeide/VTGNet.git) - Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective [[IROS2019]](https://ieeexplore.ieee.org/abstract/document/8968307) - Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control [[arXiv2018]](https://arxiv.org/abs/1811.06817)

(back to top)

### Counterfactual Explanations and Causal Inference - OCTET: Object-aware Counterfactual Explanation [[CVPR2023]](https://arxiv.org/abs/2211.12380)[[Code]](https://github.com/valeoai/OCTET.git) - STEEX: Steering Counterfactual Explanations with Semantics [[ECCV2022]](https://arxiv.org/abs/2111.09094)[[Code]](https://github.com/valeoai/STEEX.git) - Who Make Drivers Stop? Towards Driver-centric Risk Assessment: Risk Object Identification via Causal Inference [[IROS2020]](https://arxiv.org/abs/2003.02425)

(back to top)

## Visual Abstraction / Representation Learning - An Examination of Offline-Trained Encoders in Vision-Based Deep Reinforcement Learning for Autonomous Driving [[CASE2024]](https://ieeexplore.ieee.org/document/10711548) - Visual Point Cloud Forecasting enables Scalable Autonomous Driving [[CVPR2024]](https://arxiv.org/abs/2312.17655)[[Code]](https://github.com/OpenDriveLab/ViDAR) - DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving [[CVPR2024]](https://arxiv.org/abs/2405.04390) - End-to-End Autonomous Driving without Costly Modularization and 3D Manual Annotation [[arXiv2024]](https://arxiv.org/abs/2406.17680) - Scene as Occupancy [[ICCV2023]](https://arxiv.org/abs/2306.02851)[[Code]](https://github.com/OpenDriveLab/OccNet) - DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving [[ICCV2023]](https://arxiv.org/abs/2308.00398)[[Code]](https://github.com/OpenDriveLab/DriveAdapter) - Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling [[ICLR2023]](https://openreview.net/forum?id=X5SUR7g2vVw)[[Code]](https://github.com/OpenDriveLab/PPGeo) - An End-to-End Autonomous Driving Pre-trained Transformer Model for Multi-Behavior-Optimal Trajectory Generation [[ITSC2023]](https://ieeexplore.ieee.org/abstract/document/10421847) - Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning [[NeurIPS2022]](https://openreview.net/forum?id=FQtku8rkp3) - Task-Induced Representation Learning [[ICLR2022]](https://arxiv.org/abs/2204.11827)[[Code]](https://github.com/clvrai/tarp) - Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities [[ICLR2022]](https://arxiv.org/abs/2212.13088)[[Code]](https://github.com/jianda-chen/AMBS.git) - Learning to Drive by Watching YouTube Videos: Action-Conditioned Contrastive Policy Pretraining [[ECCV2022]](https://arxiv.org/abs/2204.02393)[[Code]](https://github.com/metadriverse/ACO) - Segmented Encoding for Sim2Real of RL-based End-to-End Autonomous Driving [[IV2022]](https://ieeexplore.ieee.org/abstract/document/9827374) - GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Driving [[arXiv2021]](https://arxiv.org/abs/2111.08575) - Latent Attention Augmentation for Robust Autonomous Driving Policies [[IROS2021]](https://ieeexplore.ieee.org/abstract/document/9636449) - Multi-Task Long-Range Urban Driving Based on Hierarchical Planning and Reinforcement Learning [[ITSC2021]](https://ieeexplore.ieee.org/abstract/document/9564705) - Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement Learning [[arXiv2021]](https://arxiv.org/abs/2109.08473) - A Versatile and Efficient Reinforcement Learning Framework for Autonomous Driving [[arxiv2021]](https://arxiv.org/abs/2110.11573) - Deductive Reinforcement Learning for Visual Autonomous Urban Driving Navigation [[TNNLS2021]](https://ieeexplore.ieee.org/document/9537641) - End-to-End Model-Free Reinforcement Learning for Urban Driving Using Implicit Affordances [[CVPR2020]](https://openaccess.thecvf.com/content_CVPR_2020/html/Toromanoff_End-to-End_Model-Free_Reinforcement_Learning_for_Urban_Driving_Using_Implicit_Affordances_CVPR_2020_paper.html) - Toward Deep Reinforcement Learning without a Simulator: An Autonomous Steering Example [[AAAI2018]](https://ojs.aaai.org/index.php/AAAI/article/view/11490)

(back to top)

## Policy Distillation - Feedback-Guided Autonomous Driving [[CVPR2024]](https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Feedback-Guided_Autonomous_Driving_CVPR_2024_paper.html) - On the Road to Portability: Compressing End-to-End Motion Planner for Autonomous Driving [[CVPR2024]](https://arxiv.org/abs/2403.01238) - Knowledge Distillation from Single-Task Teachers to Multi-Task Student for End-to-End Autonomous Driving [[AAAI2024]](https://ojs.aaai.org/index.php/AAAI/article/view/30388)[[Code]](https://github.com/pagand/e2etransfuser) - Multi-Task Adaptive Gating Network for Trajectory Distilled Control Prediction [[RAL2024]](https://ieeexplore.ieee.org/abstract/document/10493137) - DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving [[ICCV2023]](https://arxiv.org/abs/2308.00398)[[Code]](https://github.com/OpenDriveLab/DriveAdapter) - Coaching a Teachable Student [[CVPR2023]](https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Coaching_a_Teachable_Student_CVPR_2023_paper.html) - Detrive: Imitation Learning with Transformer Detection for End-to-End Autonomous Driving [[DISA2023]](https://arxiv.org/abs/2310.14224) - Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline [[NeurIPS2022]](https://arxiv.org/abs/2206.08129)[[Code]](https://github.com/OpenDriveLab/TCP) - Learning from All Vehicles [[CVPR2022]](http://arxiv.org/pdf/1709.04622v4)[[Code]](https://github.com/dotchen/LAV.git) - End-to-End Urban Driving by Imitating a Reinforcement Learning Coach [[ICCV2021]](https://arxiv.org/abs/2108.08265)[[Code]](https://github.com/zhejz/carla-roach.git) - Learning To Drive From a World on Rails [[ICCV2021]](http://arxiv.org/pdf/2105.00636v3)[[Code]](https://github.com/dotchen/WorldOnRails.git) - Learning by Cheating [[CoRL2020]](http://arxiv.org/pdf/2107.00123v1)[[Code]](https://github.com/dotchen/LearningByCheating.git) - SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning [[CoRL2020]](https://arxiv.org/abs/1912.02973)[[Code]](https://github.com/twsq/sam-driving.git) - Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks [[AAAI2019]](http://arxiv.org/pdf/1811.02759v1)[[Code]](https://github.com/cardwing/Codes-for-Steering-Control.git)

(back to top)

## Causal Confusion - Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving? [[CVPR2024]](https://arxiv.org/abs/2312.03031)[[Code]](https://github.com/NVlabs/BEV-Planner) - Exploring the Causality of End-to-End Autonomous Driving [[arXiv2024]](https://arxiv.org/abs/2407.06546)[[Code]](https://github.com/bdvisl/DriveInsight) - DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving [[ICCV2023]](https://arxiv.org/abs/2308.00398)[[Code]](https://github.com/OpenDriveLab/DriveAdapter) - Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes [[arxiv2023]](https://arxiv.org/abs/2305.10430) - Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving [[arXiv2023]](https://arxiv.org/abs/2311.10747) - Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming [[ICML2022]](https://arxiv.org/abs/2206.10816) - Resolving Copycat Problems in Visual Imitation Learning via Residual Action Prediction [[ECCV2022]](https://link.springer.com/chapter/10.1007/978-3-031-19842-7_23) - Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning [[NeurIPS2021]](https://arxiv.org/abs/2110.14118)[[Code]](https://github.com/alinlab/oreo.git) - Keyframe-Focused Visual Imitation Learning [[ICML2021]](https://arxiv.org/abs/2106.06452)[[Code]](https://github.com/AlvinWen428/keyframe-focused-imitation-learning) - Fighting Copycat Agents in Behavioral Cloning from Observation Histories [[NeurIPS2020]](http://arxiv.org/pdf/2010.14876v1) - Shortcut Learning in Deep Neural Networks [[NatureMachineIntelligence2020]](https://www.nature.com/articles/s42256-020-00257-z) - Causal Confusion in Imitation Learning [[NeurIPS2019]](https://proceedings.neurips.cc/paper/2019/hash/947018640bf36a2bb609d3557a285329-Abstract.html) - ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst [[RSS2019]](https://arxiv.org/abs/1812.03079) - Exploring the Limitations of Behavior Cloning for Autonomous Driving [[ICCV2019]](https://arxiv.org/abs/1904.08980)[[Code]](https://github.com/felipecode/coiltraine.git) - Off-Road Obstacle Avoidance through End-to-End Learning [[NeurIPS2005]](https://proceedings.neurips.cc/paper/2005/hash/fdf1bc5669e8ff5ba45d02fded729feb-Abstract.html)

(back to top)

## Robustness ### Long-tailed Distribution - An LLM-enhanced Multi-objective Evolutionary Search for Autonomous Driving Test Scenario Generation [[arXiv2024]](https://arxiv.org/abs/2406.10857) - Unleashing Generalization of End-to-End Autonomous Driving with Controllable Long Video Generation [[arXiv2024]](https://arxiv.org/abs/2406.01349)[[Code]](https://github.com/westlake-autolab/Delphi) - CAT: Closed-loop Adversarial Training for Safe End-to-End Driving [[CoRL2023]](https://openreview.net/forum?id=VtJqMs9ig20) - Adversarial Driving: Attacking End-to-End Autonomous Driving [[IV2023]](https://arxiv.org/abs/2103.09151)[[Code]](https://github.com/wuhanstudio/adversarial-driving.git) - KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients [[ECCV2022]](https://arxiv.org/abs/2204.13683)[[Code]](https://github.com/autonomousvision/transfuser.git) - AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/html/Wang_AdvSim_Generating_Safety-Critical_Scenarios_for_Self-Driving_Vehicles_CVPR_2021_paper.html) - TrafficSim: Learning To Simulate Realistic Multi-Agent Behaviors [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/html/Suo_TrafficSim_Learning_To_Simulate_Realistic_Multi-Agent_Behaviors_CVPR_2021_paper.html) - Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation [[RAL2021]](https://arxiv.org/abs/2009.08311) - Learning by Cheating [[CoRL2020]](http://arxiv.org/pdf/2107.00123v1)[[Code]](https://github.com/dotchen/LearningByCheating.git) - Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method [[IROS2020]](https://arxiv.org/abs/2003.01197) - Enhanced Transfer Learning for Autonomous Driving with Systematic Accident Simulation [[IROS2020]](https://arxiv.org/abs/2007.12148) - Improving the Generalization of End-to-End Driving through Procedural Generation [[arXiv2020]](https://arxiv.org/abs/2012.13681)[[Code]](https://github.com/decisionforce/pgdrive.git) - Generating Adversarial Driving Scenarios in High-Fidelity Simulators [[ICRA2019]](https://ieeexplore.ieee.org/abstract/document/8793740) - Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation [[NeurIPS2018]](https://proceedings.neurips.cc/paper/2018/hash/653c579e3f9ba5c03f2f2f8cf4512b39-Abstract.html) - Microscopic Traffic Simulation using SUMO [[ITSC2018]](https://ieeexplore.ieee.org/abstract/document/8569938)

(back to top)

### Covariate Shift - Exploring Data Aggregation in Policy Learning for Vision-Based Urban Autonomous Driving [[CVPR2020]](https://openaccess.thecvf.com/content_CVPR_2020/html/Prakash_Exploring_Data_Aggregation_in_Policy_Learning_for_Vision-Based_Urban_Autonomous_CVPR_2020_paper.html) - Learning by Cheating [[CoRL2020]](http://arxiv.org/pdf/2107.00123v1)[[Code]](https://github.com/dotchen/LearningByCheating.git) - Agile Autonomous Driving using End-to-End Deep Imitation Learning [[RSS2018]](https://arxiv.org/abs/1709.07174) - Query-Efficient Imitation Learning for End-to-End Simulated Driving [[AAAI2017]](https://ojs.aaai.org/index.php/AAAI/article/view/10857) - Meta learning Framework for Automated Driving [[arXiv2017]](http://arxiv.org/pdf/1706.04038v1) - A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning [[AISTATS2011]](http://proceedings.mlr.press/v15/ross11a)

(back to top)

### Domain Adaptation - Uncertainty-Guided Never-Ending Learning to Drive [[CVPR2024]](https://openaccess.thecvf.com/content/CVPR2024/html/Lai_Uncertainty-Guided_Never-Ending_Learning_to_Drive_CVPR_2024_paper.html)[[Code]](https://github.com/h2xlab/InfDriver) - A Comparison of Imitation Learning Pipelines for Autonomous Driving on the Effect of Change in Ego-vehicle [[IV2024]](https://ieeexplore.ieee.org/abstract/document/10588638) - Balanced Training for the End-to-End Autonomous Driving Model Based on Kernel Density Estimation [[IV2024]](https://ieeexplore.ieee.org/abstract/document/10588649) - ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving [[arXiv2024]](https://arxiv.org/abs/2403.02877) - DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving [[ICCV2023]](https://arxiv.org/abs/2308.00398)[[Code]](https://github.com/OpenDriveLab/DriveAdapter) - Learning to Drive Anywhere [[CoRL2023]](https://arxiv.org/abs/2309.12295) - SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation [[CVPR2022]](https://arxiv.org/abs/2206.08367)[[Code]](https://github.com/SysCV/shift-dev) - Learning Interactive Driving Policies via Data-driven Simulation [[ICRA2022]](https://ieeexplore.ieee.org/abstract/document/9812407) - Segmented Encoding for Sim2Real of RL-based End-to-End Autonomous Driving [[IV2022]](https://ieeexplore.ieee.org/abstract/document/9827374) - Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation [[AAAI2021]](https://ojs.aaai.org/index.php/AAAI/article/view/17251)[[Code]](https://github.com/KarlXing/LUSR.git) - A Versatile and Efficient Reinforcement Learning Framework for Autonomous Driving [[arxiv2021]](https://arxiv.org/abs/2110.11573) - Enhanced Transfer Learning for Autonomous Driving with Systematic Accident Simulation [[IROS2020]](https://arxiv.org/abs/2007.12148) - Simulation-Based Reinforcement Learning for Real-World Autonomous Driving [[ICRA2020]](https://ieeexplore.ieee.org/abstract/document/9196730)[[Code]](https://github.com/deepsense-ai/carla-birdeye-view.git) - Learning to Drive from Simulation without Real World Labels [[ICRA2019]](https://arxiv.org/abs/1812.03823) - Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective [[IROS2019]](https://ieeexplore.ieee.org/abstract/document/8968307) - Virtual to Real Reinforcement Learning for Autonomous Driving [[BMVC2017]](https://arxiv.org/abs/1704.03952)[[Code]](https://github.com/SullyChen/Autopilot-TensorFlow.git)

(back to top)

## Affordance Learning - Enhance Planning with Physics-informed Safety Controller for End-to-end Autonomous Driving [[arXiv2024]](https://arxiv.org/abs/2405.00316) - Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer [[CoRL2022]](https://arxiv.org/abs/2207.14024)[[Code]](https://github.com/opendilab/InterFuser) - Multi-Task Learning With Attention for End-to-End Autonomous Driving [[CVPRWorkshop2021]](https://arxiv.org/abs/2104.10753) - Driver Behavioral Cloning for Route Following in Autonomous Vehicles Using Task Knowledge Distillation [[TIV2022]](https://ieeexplore.ieee.org/abstract/document/9857598) - Policy-Based Reinforcement Learning for Training Autonomous Driving Agents in Urban Areas With Affordance Learning [[TITS2021]](https://ieeexplore.ieee.org/abstract/document/9599578) - Conditional Affordance Learning for Driving in Urban Environments [[CoRL2018]](https://proceedings.mlr.press/v87/sauer18a.html)[[Code]](https://github.com/xl-sr/CAL)

(back to top)

## BEV - Visual Point Cloud Forecasting enables Scalable Autonomous Driving [[CVPR2024]](https://arxiv.org/abs/2312.17655)[[Code]](https://github.com/OpenDriveLab/ViDAR) - DualAD: Disentangling the Dynamic and Static World for End-to-End Driving [[CVPR2024]](https://openaccess.thecvf.com/content/CVPR2024/html/Doll_DualAD_Disentangling_the_Dynamic_and_Static_World_for_End-to-End_Driving_CVPR_2024_paper.html) - ParkingE2E: Camera-based End-to-end Parking Network, from Images to Planning [[IROS2024]](https://arxiv.org/abs/2408.02061)[[Code]](https://github.com/qintonguav/ParkingE2E) - E2E Parking: Autonomous Parking by the End-to-end Neural Network on the CARLA Simulator [[IV2024]](https://ieeexplore.ieee.org/abstract/document/10588551)[[Code]](https://github.com/qintonguav/e2e-parking-carla) - BEVGPT: Generative Pre-trained Large Model for Autonomous Driving Prediction, Decision-Making, and Planning [[AAAI2024]](https://arxiv.org/abs/2310.10357) - PolarPoint-BEV: Bird-eye-view Perception in Polar Points for Explainable End-to-end Autonomous Driving [[TIV2024]](https://ieeexplore.ieee.org/abstract/document/10418570) - Hybrid-Prediction Integrated Planning for Autonomous Driving [[arXiv2024]](https://arxiv.org/abs/2402.02426)[[Code]](https://github.com/zhangyp15/GraphAD) - GraphAD: Interaction Scene Graph for End-to-end Autonomous Driving [[arXiv2024]](https://arxiv.org/abs/2403.19098)[[Code]](https://github.com/georgeliu233/HPP) - DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving [[ICCV2023]](https://arxiv.org/abs/2308.00398)[[Code]](https://github.com/OpenDriveLab/DriveAdapter) - Planning-oriented Autonomous Driving [[CVPR2023]](https://arxiv.org/abs/2212.10156)[[Code]](https://github.com/OpenDriveLab/UniAD) - Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving [[CVPR2023]](https://arxiv.org/abs/2305.06242)[[Code]](https://github.com/OpenDriveLab/ThinkTwice) - Coaching a Teachable Student [[CVPR2023]](https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Coaching_a_Teachable_Student_CVPR_2023_paper.html) - ReasonNet: End-to-End Driving with Temporal and Global Reasoning [[CVPR2023]](https://arxiv.org/abs/2305.10507) - VAD: Vectorized Scene Representation for Efficient Autonomous Driving [[ICCV2023]](https://arxiv.org/abs/2303.12077)[[Code]](https://github.com/hustvl/VAD) - FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving [[arXiv2023]](https://arxiv.org/abs/2308.01006) - UAP-BEV: Uncertainty Aware Planning using Bird's Eye View generated from Surround Monocular Images [[CASE2023]](https://arxiv.org/abs/2306.04939)[[Code]](https://github.com/Vikr-182/UAP-BEV) - Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model [[NeurIPSWorkshop2022]](https://arxiv.org/abs/2210.04017) - Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer [[CoRL2022]](https://arxiv.org/abs/2207.14024)[[Code]](https://github.com/opendilab/InterFuser) - Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning [[ICML2022]](https://arxiv.org/abs/2207.10295) - Learning Mixture of Domain-Specific Experts via Disentangled Factors for Autonomous Driving Authors [[AAAI2022]](https://ojs.aaai.org/index.php/AAAI/article/view/20000) - ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning [[ECCV2022]](https://arxiv.org/abs/2207.07601)[[Code]](https://github.com/OpenDriveLab/ST-P3) - TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving [[TPAMI2022]](https://arxiv.org/abs/2205.15997)[[Code]](https://github.com/autonomousvision/transfuser.git) - Learning from All Vehicles [[CVPR2022]](https://arxiv.org/abs/2203.11934)[[Code]](https://github.com/dotchen/LAV.git) - Deep Federated Learning for Autonomous Driving [[IV2022]](http://arxiv.org/pdf/2110.05754v2)[[Code]](https://github.com/aioz-ai/FADNet.git) - NEAT: Neural Attention Fields for End-to-End Autonomous Driving [[ICCV2021]](https://arxiv.org/abs/2109.04456)[[Code]](https://github.com/autonomousvision/neat.git) - ObserveNet Control: A Vision-Dynamics Learning Approach to Predictive Control in Autonomous Vehicles [[RAL2021]](https://arxiv.org/abs/2107.08690) - Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D [[ECCV2020]](https://arxiv.org/abs/2008.05711)[[Code]](https://github.com/nv-tlabs/lift-splat-shoot.git) - Driving Through Ghosts: Behavioral Cloning with False Positives [[IROS2020]](https://arxiv.org/abs/2008.12969)

(back to top)

## Transformer - PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving [[ECCV2024]](https://arxiv.org/abs/2311.08100)[[Code]](https://github.com/zlichen/PPAD) - DualAD: Disentangling the Dynamic and Static World for End-to-End Driving [[CVPR2024]](https://openaccess.thecvf.com/content/CVPR2024/html/Doll_DualAD_Disentangling_the_Dynamic_and_Static_World_for_End-to-End_Driving_CVPR_2024_paper.html) - Target-point Attention Transformer: A novel trajectory predict network for end-to-end autonomous driving [[IV2024]](https://ieeexplore.ieee.org/abstract/document/10588617) - Hybrid-Prediction Integrated Planning for Autonomous Driving [[arXiv2024]](https://arxiv.org/abs/2402.02426)[[Code]](https://github.com/georgeliu233/HPP) - SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving [[arXiv2024]](https://arxiv.org/abs/2404.06892) - VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning [[arXiv2024]](https://arxiv.org/abs/2402.13243) - DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba [[arXiv2024]](https://arxiv.org/abs/2408.03601)[[Code]](https://github.com/Chengran-Yuan/DRAMA) - LeGo-Drive: Language-enhanced Goal-oriented Closed-Loop End-to-End Autonomous Driving [[arXiv2024]](https://arxiv.org/abs/2403.20116)[[Code]](https://github.com/reachpranjal/lego-drive) - Planning-oriented Autonomous Driving [[CVPR2023]](https://arxiv.org/abs/2212.10156)[[Code]](https://github.com/OpenDriveLab/UniAD) - Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving [[CVPR2023]](https://arxiv.org/abs/2305.06242)[[Code]](https://github.com/OpenDriveLab/ThinkTwice) - ReasonNet: End-to-End Driving with Temporal and Global Reasoning [[CVPR2023]](https://arxiv.org/abs/2305.10507) - Hidden Biases of End-to-End Driving Models [[ICCV2023]](https://arxiv.org/abs/2306.07957)[[Code]](https://github.com/autonomousvision/carla_garage) - VAD: Vectorized Scene Representation for Efficient Autonomous Driving [[ICCV2023]](https://arxiv.org/abs/2303.12077)[[Code]](https://github.com/hustvl/VAD) - Detrive: Imitation Learning with Transformer Detection for End-to-End Autonomous Driving [[DISA2023]](https://arxiv.org/abs/2310.14224) - Ground then Navigate: Language-guided Navigation in Dynamic Scenes [[arXiv2022]](https://arxiv.org/abs/2209.11972) - Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer [[CoRL2022]](https://arxiv.org/abs/2207.14024)[[Code]](https://github.com/opendilab/InterFuser) - MMFN: Multi-Modal-Fusion-Net for End-to-End Driving [[IROS2022]](https://arxiv.org/abs/2207.00186)[[Code]](https://github.com/Kin-Zhang/mmfn.git) - TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving [[TPAMI2022]](https://arxiv.org/abs/2205.15997)[[Code]](https://github.com/autonomousvision/transfuser.git) - Human-AI Shared Control via Policy Dissection [[NeurIPS2022]](https://arxiv.org/abs/2206.00152)[[Code]](https://github.com/Mehooz/vision4leg.git) - COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles [[CVPR2022]](https://arxiv.org/abs/2205.02222)[[Code]](https://github.com/UT-Austin-RPL/Coopernaut.git) - CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-Based Autonomous Urban Driving [[AAAI2022]](https://arxiv.org/abs/2202.08557)[[Code]](https://github.com/BIT-MCS/Cadre.git) - Safe Driving via Expert Guided Policy Optimization [[CoRL2022]](http://arxiv.org/pdf/2110.06831v2)[[Code]](https://github.com/decisionforce/EGPO.git) - NEAT: Neural Attention Fields for End-to-End Autonomous Driving [[ICCV2021]](https://arxiv.org/abs/2109.04456)[[Code]](https://github.com/autonomousvision/neat.git) - Multi-Modal Fusion Transformer for End-to-End Autonomous Driving [[CVPR2021]](https://arxiv.org/abs/2104.09224)[[Code]](https://github.com/autonomousvision/transfuser.git)

(back to top)

## V2V Cooperative - ICOP: Image-based Cooperative Perception for End-to-End Autonomous Driving [[IV2024]](https://ieeexplore.ieee.org/abstract/document/10588825) - Towards Collaborative Autonomous Driving: Simulation Platform and End-to-End System [[arXiv2024]](https://arxiv.org/abs/2404.09496)[[Code]](https://github.com/CollaborativePerception/V2Xverse) - End-to-End Autonomous Driving through V2X Cooperation [[arXiv2024]](https://arxiv.org/abs/2404.00717)[[Code]](https://github.com/AIR-THU/UniV2X) - CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-Based Autonomous Urban Driving [[AAAI2022]](https://arxiv.org/abs/2202.08557)[[Code]](https://github.com/BIT-MCS/Cadre.git) - COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles [[CVPR2022]](https://arxiv.org/abs/2205.02222)[[Code]](https://github.com/UT-Austin-RPL/Coopernaut.git)

(back to top)

## Distributed RL - Safe Driving via Expert Guided Policy Optimization [[CoRL2022]](http://arxiv.org/pdf/2110.06831v2)[[Code]](https://github.com/decisionforce/EGPO.git) - GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Driving [[arXiv2021]](https://arxiv.org/abs/2111.08575) - End-to-End Model-Free Reinforcement Learning for Urban Driving Using Implicit Affordances [[CVPR2020]](https://openaccess.thecvf.com/content_CVPR_2020/html/Toromanoff_End-to-End_Model-Free_Reinforcement_Learning_for_Urban_Driving_Using_Implicit_Affordances_CVPR_2020_paper.html) - Batch Policy Learning under Constraints [[ICML2019]](http://arxiv.org/pdf/1903.08738v1)[[Code]](https://github.com/gwthomas/force.git)

(back to top)

## Data-driven Simulation ### Parameter Initialization - SLEDGE: Synthesizing Driving Environments with Generative Models and Rule-Based Traffic [[ECCV2024]](https://arxiv.org/abs/2403.17933)[[Code]](https://github.com/autonomousvision/sledge) - NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking [[arXiv2024]](https://arxiv.org/abs/2406.15349)[[Code]](https://github.com/autonomousvision/navsim) - TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios [[ICRA2023]](https://arxiv.org/abs/2210.06609)[[Code]](https://github.com/metadriverse/trafficgen) - KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients [[ECCV2022]](https://arxiv.org/abs/2204.13683)[[Code]](https://github.com/autonomousvision/transfuser.git) - AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/html/Wang_AdvSim_Generating_Safety-Critical_Scenarios_for_Self-Driving_Vehicles_CVPR_2021_paper.html) - SceneGen: Learning To Generate Realistic Traffic Scenes [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/html/Tan_SceneGen_Learning_To_Generate_Realistic_Traffic_Scenes_CVPR_2021_paper.html) - HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/html/Mi_HDMapGen_A_Hierarchical_Graph_Generative_Model_of_High_Definition_Maps_CVPR_2021_paper.html) - SimNet: Learning Reactive Self-driving Simulations from Real-world Observations [[ICRA2021]](https://arxiv.org/abs/2105.12332) - Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method [[IROS2020]](https://arxiv.org/abs/2003.01197)

(back to top)

### Traffic Simulation - Solving Motion Planning Tasks with a Scalable Generative Model [[ECCV2024]](https://arxiv.org/abs/2407.02797)[[Code]](https://github.com/HorizonRobotics/GUMP/) - SMART: Scalable Multi-agent Real-time Simulation via Next-token Prediction [[arXiv2024]](https://arxiv.org/abs/2405.15677) - Data-driven Traffic Simulation: A Comprehensive Review [[arXiv2023]](https://arxiv.org/abs/2310.15975) - Scenario Diffusion: Controllable Driving Scenario Generation With Diffusion [[NeurIPS2023]](https://arxiv.org/abs/2311.02738) - ScenarioNet: Open-Source Platform for Large-Scale Traffic Scenario Simulation and Modeling [[NeurIPSDataset2023]](https://arxiv.org/abs/2306.12241)[[Code]](https://github.com/metadriverse/scenarionet) - MixSim: A Hierarchical Framework for Mixed Reality Traffic Simulation [[CVPR2023]](https://openaccess.thecvf.com/content/CVPR2023/html/Suo_MixSim_A_Hierarchical_Framework_for_Mixed_Reality_Traffic_Simulation_CVPR_2023_paper.html) - Learning Realistic Traffic Agents in Closed-loop [[CoRL2023]](https://arxiv.org/abs/2311.01394) - TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction [[arXiv2023]](https://arxiv.org/abs/2303.04116) - Language Conditioned Traffic Generation [[arXiv2023]](https://arxiv.org/abs/2307.07947)[[Code]](https://github.com/Ariostgx/lctgen) - TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios [[ICRA2023]](https://arxiv.org/abs/2210.06609)[[Code]](https://github.com/metadriverse/trafficgen) - DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch [[arXiv2023]](https://arxiv.org/abs/2309.14685) - Guided Conditional Diffusion for Controllable Traffic Simulation [[arXiv2022]](https://arxiv.org/abs/2210.17366) - BITS: Bi-level Imitation for Traffic Simulation [[arXiv2022]](https://arxiv.org/abs/2208.12403) - TrafficSim: Learning To Simulate Realistic Multi-Agent Behaviors [[CVPR2021]](https://openaccess.thecvf.com/content/CVPR2021/html/Suo_TrafficSim_Learning_To_Simulate_Realistic_Multi-Agent_Behaviors_CVPR_2021_paper.html) - SimNet: Learning Reactive Self-driving Simulations from Real-world Observations [[ICRA2021]](https://arxiv.org/abs/2105.12332)

(back to top)

### Sensor Simulation - Street Gaussians: Modeling Dynamic Urban Scenes with Gaussian Splatting [[ECCV2024]](https://arxiv.org/abs/2401.01339)[[Code]](https://github.com/zju3dv/street_gaussians) - A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very Large Datasets [[SIGGRAPH2024]](https://arxiv.org/abs/2406.12080)[[Code]](https://github.com/graphdeco-inria/hierarchical-3d-gaussians) - NeuRAD: Neural Rendering for Autonomous Driving [[CVPR2024]](https://arxiv.org/abs/2311.15260)[[Code]](https://github.com/georghess/neurad-studio) - Multi-Level Neural Scene Graphs for Dynamic Urban Environments [[CVPR2024]](https://arxiv.org/abs/2404.00168)[[Code]](https://github.com/tobiasfshr/map4d) - Multiagent Multitraversal Multimodal Self-Driving: Open MARS Dataset [[CVPR2024]](https://arxiv.org/abs/2406.09383)[[Code]](https://github.com/ai4ce/MARS) - HUGS: Holistic Urban 3D Scene Understanding via Gaussian Splatting [[CVPR2024]](https://arxiv.org/abs/2403.12722)[[Code]](https://github.com/hyzhou404/HUGS) - DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes [[CVPR2024]](https://arxiv.org/abs/2312.07920)[[Code]](https://github.com/VDIGPKU/DrivingGaussian) - Editable Scene Simulation for Autonomous Driving via Collaborative LLM-Agents [[CVPR2024]](https://arxiv.org/abs/2402.05746)[[Code]](https://github.com/yifanlu0227/ChatSim) - LidaRF: Delving into Lidar for Neural Radiance Field on Street Scenes [[CVPR2024]](https://arxiv.org/abs/2405.00900) - LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis [[CVPR2024]](https://arxiv.org/abs/2404.02742)[[Code]](https://github.com/ispc-lab/LiDAR4D) - PaReNeRF: Toward Fast Large-scale Dynamic NeRF with Patch-based Reference [[CVPR2024]](https://openaccess.thecvf.com/content/CVPR2024/html/Tang_PaReNeRF_Toward_Fast_Large-scale_Dynamic_NeRF_with_Patch-based_Reference_CVPR_2024_paper.html) - Dynamic LiDAR Re-simulation using Compositional Neural Fields [[CVPR2024]](https://arxiv.org/abs/2312.05247)[[Code]](https://github.com/prs-eth/Dynamic-LiDAR-Resimulation) - Panacea: Panoramic and Controllable Video Generation for Autonomous Driving [[CVPR2024]](https://arxiv.org/abs/2311.16813)[[Code]](https://github.com/wenyuqing/panacea) - EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision [[ICLR2024]](https://arxiv.org/abs/2311.02077)[[Code]](https://github.com/NVlabs/EmerNeRF) - UC-NeRF: Neural Radiance Field for Under-Calibrated Multi-view Cameras in Autonomous Driving [[ICLR2024]](https://arxiv.org/abs/2311.16945)[[Code]](https://github.com/kcheng1021/UC-NeRF) - S3Gaussian: Self-Supervised Street Gaussians for Autonomous Driving [[arXiv2024]](https://arxiv.org/abs/2405.20323)[[Code]](https://github.com/nnanhuang/S3Gaussian/) - AutoSplat: Constrained Gaussian Splatting for Autonomous Driving Scene Reconstruction [[arXiv2024]](https://arxiv.org/abs/2407.02598) - Dynamic 3D Gaussian Fields for Urban Areas [[arXiv2024]](https://arxiv.org/abs/2406.03175)[[Code]](https://github.com/tobiasfshr/map4d) - MagicDrive3D: Controllable 3D Generation for Any-View Rendering in Street Scenes [[arXiv2024]](https://arxiv.org/abs/2405.14475)[[Code]](https://github.com/flymin/MagicDrive3D) - VDG: Vision-Only Dynamic Gaussian for Driving Simulation [[arXiv2024]](https://arxiv.org/abs/2406.18198)[[Code]](https://github.com/lifuguan/VDG_official) - HO-Gaussian: Hybrid Optimization of 3D Gaussian Splatting for Urban Scenes [[arXiv2024]](https://arxiv.org/abs/2403.20032) - SGD: Street View Synthesis with Gaussian Splatting and Diffusion Prior [[arXiv2024]](https://arxiv.org/abs/2403.20079) - LightSim: Neural Lighting Simulation for Urban Scenes [[NeurIPS2023]](https://arxiv.org/abs/2312.06654) - Real-Time Neural Rasterization for Large Scenes [[ICCV2023]](https://arxiv.org/abs/2311.05607) - UniSim: A Neural Closed-Loop Sensor Simulator [[CVPR2023]](https://openaccess.thecvf.com/content/CVPR2023/html/Yang_UniSim_A_Neural_Closed-Loop_Sensor_Simulator_CVPR_2023_paper.html) - Learning Compact Representations for LiDAR Completion and Generation [[CVPR2023]](https://openaccess.thecvf.com/content/CVPR2023/html/Xiong_Learning_Compact_Representations_for_LiDAR_Completion_and_Generation_CVPR_2023_paper.html) - Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulation [[CoRL2023]](https://arxiv.org/abs/2311.01446) - Reconstructing Objects in-the-wild for Realistic Sensor Simulation [[ICRA2023]](https://arxiv.org/abs/2311.05602) - Enhancing Photorealism Enhancement [[TPAMI2023]](https://arxiv.org/abs/2105.04619)[[Code]](https://github.com/isl-org/PhotorealismEnhancement) - UrbanGIRAFFE: Representing Urban Scenes as Compositional Generative Neural Feature Fields [[ICCV2023]](https://arxiv.org/abs/2303.14167)[[Code]](https://github.com/freemty/urbanGIRAFFE) - MARS: An Instance-aware, Modular and Realistic Simulator for Autonomous Driving [[CICAI2023]](https://arxiv.org/abs/2307.15058)[[Code]](https://github.com/OPEN-AIR-SUN/mars) - Mega-NERF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs [[CVPR2022]](https://openaccess.thecvf.com/content/CVPR2022/html/Turki_Mega-NERF_Scalable_Construction_of_Large-Scale_NeRFs_for_Virtual_Fly-Throughs_CVPR_2022_paper.html) - Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation [[CVPR2022]](https://openaccess.thecvf.com/content/CVPR2022/html/Kundu_Panoptic_Neural_Fields_A_Semantic_Object-Aware_Neural_Scene_Representation_CVPR_2022_paper.html) - CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation [[CoRL2022]](https://openreview.net/forum?id=Mp3Y5jd7rnW) - VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles [[ICRA2022]](https://arxiv.org/abs/2111.12083)[[Code]](https://github.com/vista-simulator/vista) - Learning Interactive Driving Policies via Data-driven Simulation [[ICRA2022]](https://arxiv.org/abs/2111.12137)[[Code]](https://github.com/vista-simulator/vista) - Learning Robust Control Policies for End-to-End Autonomous Driving From Data-Driven Simulation [[RAL2020]](https://ieeexplore.ieee.org/abstract/document/8957584)

(back to top)