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Paper Collection

We list key challenges from a wide span of candidate concerns, as well as trending methodologies.

Survey

  • End-to-End Autonomous Driving: Challenges and Frontiers [arXiv]

  • Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives [TIV2023]

  • Imitation Learning: Progress, Taxonomies and Challenges [TNNLS2022]

  • A Review of End-to-End Autonomous Driving in Urban Environments [Access2022]

  • A Survey on Imitation Learning Techniques for End-to-End Autonomous Vehicles [TITS2022]

  • Deep Reinforcement Learning for Autonomous Driving: A Survey [TITS2021]

  • A Survey of Deep RL and IL for Autonomous Driving Policy Learning [TITS2021]

  • A Survey of End-to-End Driving: Architectures and Training Methods [TNNLS2020]

  • Learning to Drive by Imitation: An Overview of Deep Behavior Cloning Methods [TIV2020]

  • Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art [book]

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Multi-sensor Fusion

  • Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving [CVPR2023][code]

  • ReasonNet: End-to-End Driving with Temporal and Global Reasoning [CVPR2023]

  • Hidden Biases of End-to-End Driving Models [arXiv2023][code]

  • Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model [NeurIPSWorkshop2022]

  • End-to-end Autonomous Driving with Semantic Depth Cloud Mapping and Multi-agent [IV2022]

  • MMFN: Multi-Modal-Fusion-Net for End-to-End Driving [IROS2022][code]

  • Interpretable End-to-End Urban Autonomous Driving With Latent Deep Reinforcement Learning [TITS2022][Code]

  • Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer [CoRL2022][Code]

  • Learning from All Vehicles [CVPR2022][Code]

  • TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving [TPAMI2022][Code]

  • Multi-Modal Fusion Transformer for End-to-End Autonomous Driving [CVPR2021][Code]

  • Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement Learning [arXiv2021]

  • Multi-modal Sensor Fusion-based Deep Neural Network for End-to-end Autonomous Driving with Scene Understanding [IEEESJ2020]

  • Probabilistic End-to-End Vehicle Navigation in Complex Dynamic Environments With Multimodal Sensor Fusion [RAL2020]

  • Multimodal End-to-End Autonomous Driving [TITS2020]

  • End-To-End Interpretable Neural Motion Planner [CVPR2019]

  • Does Computer Vision Matter for Action? [ScienceRobotics2019]

  • End-To-End Multi-Modal Sensors Fusion System For Urban Automated Driving [NeurIPSWorkshop2018]

  • MultiNet: Multi-Modal Multi-Task Learning for Autonomous Driving [WACV2019]

  • LiDAR-Video Driving Dataset: Learning Driving Policies Effectively [CVPR2018]

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Language-guided Driving

  • Ground then Navigate: Language-guided Navigation in Dynamic Scenes [arXiv2022]

  • LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action [CoRL2022]

  • Generating Landmark Navigation Instructions from Maps as a Graph-to-Text Problem [ACL2021]

  • Advisable Learning for Self-Driving Vehicles by Internalizing Observation-to-Action Rules [CVPR2020]

  • Conditional Driving from Natural Language Instructions [CoRL2019]

  • Grounding Human-to-Vehicle Advice for Self-driving Vehicles [CVPR2019][Dataset]

  • Talk to the Vehicle: Language Conditioned Autonomous Navigation of Self Driving Cars [IROS2019]

  • Talk2Car: Taking Control of Your Self-Driving Car [EMNLP2019]

  • TOUCHDOWN: Natural Language Navigation and Spatial Reasoning in Visual Street Environments [CVPR2019]

  • Learning to Navigate in Cities Without a Map [NeurIPS2018][Code]

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Multi-task Learning

  • Planning-oriented Autonomous Driving [CVPR2023][Code]

  • Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving [CVPR2023][code]

  • Coaching a Teachable Student [CVPR2023]

  • ReasonNet: End-to-End Driving with Temporal and Global Reasoning [CVPR2023]

  • Hidden Biases of End-to-End Driving Models [arXiv2023][code]

  • TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving [TPAMI2022][Code]

  • Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline [NeurIPS2022] [code]

  • Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer [CoRL2022][Code]

  • Learning from All Vehicles [CVPR2022][Code]

  • Multi-Task Learning With Attention for End-to-End Autonomous Driving [CVPRWorkshop2021]

  • NEAT: Neural Attention Fields for End-to-End Autonomous Driving [ICCV2021][Code]

  • SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning [CoRL2020][Code]

  • Urban Driving with Conditional Imitation Learning [ICRA2020]

  • Multi-modal Sensor Fusion-based Deep Neural Network for End-to-end Autonomous Driving with Scene Understanding [IEEESJ2020]

  • Multi-task Learning with Future States for Vision-based Autonomous Driving [ACCV2020]

  • Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks [AAAI2019][Code]

  • MultiNet: Multi-Modal Multi-Task Learning for Autonomous Driving [WACV2019]

  • Intentnet: Learning to Predict Intention from Raw Sensor Data [CoRL2018]

  • Rethinking Self-driving: Multi-task Knowledge for Better Generalization and Accident Explanation Ability [arXiv2018][Code]

  • Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision [ICVGIP2018]

  • End-to-end Learning of Driving Models from Large-scale Video Datasets [CVPR2017][Code]

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Interpretability

Attention Visualization

  • Scaling Self-Supervised End-to-End Driving with Multi-View Attention Learning [arxiv2023]

  • PlanT: Explainable Planning Transformers via Object-Level Representations [CoRL2022][Code]

  • MMFN: Multi-Modal-Fusion-Net for End-to-End Driving [IROS2022][code]

  • TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving [TPAMI2022][Code]

  • Multi-Modal Fusion Transformer for End-to-End Autonomous Driving [CVPR2021][Code]

  • Multi-Task Learning With Attention for End-to-End Autonomous Driving [CVPRWorkshop2021]

  • NEAT: Neural Attention Fields for End-to-End Autonomous Driving [ICCV2021][Code]

  • Explaining Autonomous Driving by Learning End-to-End Visual Attention [CVPRWorkshop2020]

  • Visual Explanation by Attention Branch Network for End-to-end Learning-based Self-driving [IV2019]

  • Deep Object-Centric Policies for Autonomous Driving [ICRA2019]

  • Textual Explanations for Self-Driving Vehicles [ECCV2018][Code]

  • Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision [ICVGIP2018]

  • Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention [ICCV2017]

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Interpretable Tasks

  • Planning-oriented Autonomous Driving [CVPR2023][Code]

  • Hidden Biases of End-to-End Driving Models [arXiv2023][code]

  • Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer [CoRL2022][Code]

  • TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving [TPAMI2022][Code]

  • Learning from All Vehicles [CVPR2022][Code]

  • Ground then Navigate: Language-guided Navigation in Dynamic Scenes [arXiv2022]

  • NEAT: Neural Attention Fields for End-to-End Autonomous Driving [ICCV2021][Code]

  • Multi-Task Learning With Attention for End-to-End Autonomous Driving [CVPRWorkshop2021]

  • Urban Driving with Conditional Imitation Learning [ICRA2020]

  • Using Eye Gaze to Enhance Generalization of Imitation Networks to Unseen Environments [TNNLS2020]

  • Multi-modal Sensor Fusion-based Deep Neural Network for End-to-end Autonomous Driving with Scene Understanding [IEEESJ2020]

  • Rethinking Self-driving: Multi-task Knowledge for Better Generalization and Accident Explanation Ability [arXiv2018][Code]

  • Learning End-to-end Autonomous Driving using Guided Auxiliary Supervision [ICVGIP2018]

  • End-to-end Learning of Driving Models from Large-scale Video Datasets [CVPR2017][Code]

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Cost Learning

  • ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning [ECCV2022][Code]

  • Differentiable Raycasting for Self-Supervised Occupancy Forecasting [ECCV2022][Code]

  • MP3: A Unified Model To Map, Perceive, Predict and Plan [CVPR2021]

  • Safe Local Motion Planning With Self-Supervised Freespace Forecasting [CVPR2021]

  • LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving [ICCV2021]

  • DSDNet: Deep Structured Self-driving Network [ECCV2020]

  • Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations [ECCV2020]

  • End-To-End Interpretable Neural Motion Planner [CVPR2019]

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Linguistic Explainability

  • ADAPT: Action-aware Driving Caption Transformer [ICRA2023][Code]

  • Driving Behavior Explanation with Multi-level Fusion [PR2022]

  • Explainable Object-Induced Action Decision for Autonomous Vehicles [CVPR2020]

  • Textual Explanations for Self-Driving Vehicles [ECCV2018][Code]

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Uncertainty Modeling

  • Probabilistic End-to-End Vehicle Navigation in Complex Dynamic Environments With Multimodal Sensor Fusion [RAL2020]

  • Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? [ICML2020][Code]

  • VTGNet: A Vision-Based Trajectory Generation Network for Autonomous Vehicles in Urban Environments [TIV2020][Code]

  • Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective [IROS2019]

  • Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control [arXiv2018]

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Visual Abstraction / Representation Learning

  • Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling [ICLR2023][Code]

  • Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning [NeurIPS2022]

  • Task-Induced Representation Learning [ICLR2022][Code]

  • Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities [ICLR2022][Code]

  • Learning to Drive by Watching YouTube Videos: Action-Conditioned Contrastive Policy Pretraining [ECCV2022][Code]

  • Segmented Encoding for Sim2Real of RL-based End-to-End Autonomous Driving [IV2022]

  • GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Driving [arXiv2021]

  • Latent Attention Augmentation for Robust Autonomous Driving Policies [IROS2021]

  • Multi-Task Long-Range Urban Driving Based on Hierarchical Planning and Reinforcement Learning [ITSC2021]

  • Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement Learning [arXiv2021]

  • A Versatile and Efficient Reinforcement Learning Framework for Autonomous Driving [arxiv2021]

  • Deductive Reinforcement Learning for Visual Autonomous Urban Driving Navigation [TNNLS2021]

  • End-to-End Model-Free Reinforcement Learning for Urban Driving Using Implicit Affordances [CVPR2020]

  • Toward Deep Reinforcement Learning without a Simulator: An Autonomous Steering Example [AAAI2018]

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Policy Distillation

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Causal Confusion

  • Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming [ICML2022]

  • Resolving Copycat Problems in Visual Imitation Learning via Residual Action Prediction [ECCV2022]

  • Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning [NeurIPS2021][Code]

  • Keyframe-Focused Visual Imitation Learning [ICML2021][Code]

  • Fighting Copycat Agents in Behavioral Cloning from Observation Histories [NeurIPS2020]

  • Shortcut Learning in Deep Neural Networks [NatureMachineIntelligence2020]

  • Causal Confusion in Imitation Learning [NeurIPS2019]

  • ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst [RSS2019]

  • Exploring the Limitations of Behavior Cloning for Autonomous Driving [ICCV2019][Code]

  • Off-Road Obstacle Avoidance through End-to-End Learning [NeurIPS2005]

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World Model & Model-based RL

  • Model-Based Imitation Learning for Urban Driving [NeurIPS2022)][Code]

  • Iso-Dream: Isolating and Leveraging Noncontrollable Visual Dynamics in World Models [NeurIPS2022][Code]

  • Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model [NeurIPSWorkshop2022]

  • Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning [ICML2022]

  • Interpretable End-to-End Urban Autonomous Driving With Latent Deep Reinforcement Learning [TITS2022][Code]

  • Learning To Drive From a World on Rails [ICCV2021][Code]

  • Uncertainty-Aware Model-Based Reinforcement Learning: Methodology and Application in Autonomous Driving [IV2022]

  • Deductive Reinforcement Learning for Visual Autonomous Urban Driving Navigation [TNNLS2021]

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Robustness

Long-tailed Distribution

  • Adversarial Driving: Attacking End-to-End Autonomous Driving [IV2023][Code]

  • KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients [ECCV2022][Code]

  • AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles [CVPR2021]

  • TrafficSim: Learning To Simulate Realistic Multi-Agent Behaviors [CVPR2021]

  • Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation [RAL2021]

  • Learning by Cheating [CoRL2020][Code]

  • Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method [IROS2020]

  • Enhanced Transfer Learning for Autonomous Driving with Systematic Accident Simulation [IROS2020]

  • Improving the Generalization of End-to-End Driving through Procedural Generation [arXiv2020][Code]

  • Generating Adversarial Driving Scenarios in High-Fidelity Simulators [ICRA2019]

  • Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation [NeurIPS2018]

  • Microscopic Traffic Simulation using SUMO [ITSC2018]

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Covariate Shift

  • Exploring Data Aggregation in Policy Learning for Vision-Based Urban Autonomous Driving [CVPR2020]

  • Learning by Cheating [CoRL2020][Code]

  • Agile Autonomous Driving using End-to-End Deep Imitation Learning [RSS2018]

  • Query-Efficient Imitation Learning for End-to-End Simulated Driving [AAAI2017]

  • Meta learning Framework for Automated Driving [arXiv2017]

  • A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning [AISTATS2011]

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Domain Adaptation

  • Learning Interactive Driving Policies via Data-driven Simulation [ICRA2022]

  • Segmented Encoding for Sim2Real of RL-based End-to-End Autonomous Driving [IV2022]

  • Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation [AAAI2021][Code]

  • A Versatile and Efficient Reinforcement Learning Framework for Autonomous Driving [arxiv2021]

  • Enhanced Transfer Learning for Autonomous Driving with Systematic Accident Simulation [IROS2020]

  • Simulation-Based Reinforcement Learning for Real-World Autonomous Driving [ICRA2020][Code]

  • Learning to Drive from Simulation without Real World Labels [ICRA2019]

  • Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective [IROS2019]

  • Virtual to Real Reinforcement Learning for Autonomous Driving [BMVC2017][Code]

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Affordance Learning

  • Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer [CoRL2022][Code]

  • Multi-Task Learning With Attention for End-to-End Autonomous Driving [CVPRWorkshop2021]

  • Driver Behavioral Cloning for Route Following in Autonomous Vehicles Using Task Knowledge Distillation [TIV2022]

  • Policy-Based Reinforcement Learning for Training Autonomous Driving Agents in Urban Areas With Affordance Learning [TITS2021]

  • Conditional Affordance Learning for Driving in Urban Environments [CoRL2018][code]

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BEV

  • Planning-oriented Autonomous Driving [CVPR2023][Code]

  • Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving [CVPR2023][code]

  • Coaching a Teachable Student [CVPR2023]

  • ReasonNet: End-to-End Driving with Temporal and Global Reasoning [CVPR2023]

  • Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model [NeurIPSWorkshop2022]

  • Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer [CoRL2022][Code]

  • Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning [ICML2022]

  • Learning Mixture of Domain-Specific Experts via Disentangled Factors for Autonomous Driving Authors [AAAI2022]

  • ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning [ECCV2022][Code]

  • TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving [TPAMI2022][Code]

  • Learning from All Vehicles [CVPR2022][Code]

  • Deep Federated Learning for Autonomous Driving [IV2022][Code]

  • NEAT: Neural Attention Fields for End-to-End Autonomous Driving [ICCV2021][Code]

  • ObserveNet Control: A Vision-Dynamics Learning Approach to Predictive Control in Autonomous Vehicles [RAL2021]

  • Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D [ECCV2020][Code]

  • Driving Through Ghosts: Behavioral Cloning with False Positives [IROS2020]

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Transformer

  • Planning-oriented Autonomous Driving [CVPR2023][Code]

  • Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving [CVPR2023][code]

  • ReasonNet: End-to-End Driving with Temporal and Global Reasoning [CVPR2023]

  • Hidden Biases of End-to-End Driving Models [arXiv2023][code]

  • Ground then Navigate: Language-guided Navigation in Dynamic Scenes [arXiv2022]

  • Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer [CoRL2022][Code]

  • MMFN: Multi-Modal-Fusion-Net for End-to-End Driving [IROS2022][Code]

  • TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving [TPAMI2022][Code]

  • Human-AI Shared Control via Policy Dissection [NeurIPS2022][Code]

  • COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles [CVPR2022][Code]

  • CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-Based Autonomous Urban Driving [AAAI2022][Code]

  • Safe Driving via Expert Guided Policy Optimization [CoRL2022][Code]

  • NEAT: Neural Attention Fields for End-to-End Autonomous Driving [ICCV2021][Code]

  • Multi-Modal Fusion Transformer for End-to-End Autonomous Driving [CVPR2021][Code]

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V2V Cooperative

  • CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-Based Autonomous Urban Driving [AAAI2022][Code]

  • COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles [CVPR2022][Code]

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Distributed RL

  • Safe Driving via Expert Guided Policy Optimization [CoRL2022][Code]

  • GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Driving [arXiv2021]

  • End-to-End Model-Free Reinforcement Learning for Urban Driving Using Implicit Affordances [CVPR2020]

  • Batch Policy Learning under Constraints [ICML2019][Code]

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Data-driven Simulation

Parameter Initialization

  • KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients [ECCV2022][Code]

  • TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios [arXiv2022][code]

  • AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles [CVPR2021]

  • SceneGen: Learning To Generate Realistic Traffic Scenes [CVPR2021]

  • HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps [CVPR2021]

  • SimNet: Learning Reactive Self-driving Simulations from Real-world Observations [ICRA2021]

  • Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method [IROS2020]

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Traffic Simulation

  • MixSim: A Hierarchical Framework for Mixed Reality Traffic Simulation [CVPR2023]

  • TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction [arXiv2023]

  • TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios [arXiv2022][code]

  • Guided Conditional Diffusion for Controllable Traffic Simulation [arXiv2022]

  • BITS: Bi-level Imitation for Traffic Simulation [arXiv2022]

  • TrafficSim: Learning To Simulate Realistic Multi-Agent Behaviors [CVPR2021]

  • SimNet: Learning Reactive Self-driving Simulations from Real-world Observations [ICRA2021]

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Sensor Simulation

  • UniSim: A Neural Closed-Loop Sensor Simulator [CVPR2023]

  • Learning Compact Representations for LiDAR Completion and Generation [CVPR2023]

  • Reconstructing Objects in-the-wild for Realistic Sensor Simulation [ICRA2023]

  • Enhancing Photorealism Enhancement [TPAMI2023][code]

  • UrbanGIRAFFE: Representing Urban Scenes as Compositional Generative Neural Feature Fields [arXiv2023]

  • Mega-NERF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs [CVPR2022]

  • Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation [CVPR2022]

  • CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation [CoRL2022]

  • VISTA 2.0: An Open, Data-driven Simulator for Multimodal Sensing and Policy Learning for Autonomous Vehicles [ICRA2022][code]

  • Learning Interactive Driving Policies via Data-driven Simulation [ICRA2022][code]

  • Learning Robust Control Policies for End-to-End Autonomous Driving From Data-Driven Simulation [RAL2020]

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