Data to 7 Oct 2026

Driving Policy Transfer via Modularity and Abstraction

arXiv 1804.09364 ↗First posted 25 Apr 2018cs.RO cs.CV cs.LGcited by 91

Not on the map. Why?

Cited by 28 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

End-to-end approaches to autonomous driving have high sample complexity and are difficult to scale to realistic urban driving. Simulation can help end-to-end driving systems by providing a cheap, safe, and diverse training environment. Yet training driving policies in simulation brings up the problem of transferring such policies to the real world. We present an approach to transferring driving policies from simulation to reality via modularity and abstraction. Our approach is inspired by classic driving systems and aims to combine the benefits of modular architectures and end-to-end deep learning approaches. The key idea is to encapsulate the driving policy such that it is not directly exposed to raw perceptual input or low-level vehicle dynamics. We evaluate the presented approach in simulated urban environments and in the real world. In particular, we transfer a driving policy trained in simulation to a 1/5-scale robotic truck that is deployed in a variety of conditions, with no finetuning, on two continents. The supplementary video can be viewed at https://youtu.be/BrMDJqI6H5U

Key facts. Cited by 91 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Apr 2018.

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Citing papers per year: 2019: 2; 2020: 1; 2021: 24; 2022: 17; 2023: 15; 2024: 15; 2025: 12; 2026: 5.

91 papers.

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2021End-to-end Interpretable Neural Motion Plannercited by 240
2021MP3: A Unified Model to Map, Perceive, Predict and Plancited by 211
2021End-to-End Urban Driving by Imitating a Reinforcement Learning Coachcited by 191
2021NEAT: Neural Attention Fields for End-to-End Autonomous Drivingcited by 174
2021AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehiclescited by 132
2021Learning to drive from a world on railscited by 114
2022PlanT: Explainable Planning Transformers via Object-Level Representationscited by 105
2022Navigating to Objects in the Real Worldcited by 104
2024Towards learning-based planning:The nuPlan benchmark for real-world autonomous drivingcited by 103
2021Learning High-Speed Flight in the Wildcited by 98
2021LookOut: Diverse Multi-Future Prediction and Planning for Self-Drivingcited by 96
2019Urban Driving with Conditional Imitation Learningcited by 84

38 more

2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
2023ADAPT: Action-aware Driving Caption Transformercited by 78
2022Zero Experience Required: Plug & Play Modular Transfer Learning for Semantic Visual Navigationcited by 66
2022When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learningcited by 64
2025Robust Autonomy Emerges from Self-Playcited by 64
2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 59
2023Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Modelscited by 59
2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
2021Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networkscited by 50
2022Hierarchical Model-Based Imitation Learning for Planning in Autonomous Drivingcited by 43
2024Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robotscited by 32
2021Learning a State Representation and Navigation in Cluttered and Dynamic Environmentscited by 30
2023The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Reviewcited by 29
2023GINA-3D: Learning to Generate Implicit Neural Assets in the Wildcited by 25
2021Learning by Watchingcited by 22
2025The Reality Gap in Robotics: Challenges, Solutions, and Best Practicescited by 22
2024Natural Language Can Help Bridge the Sim2Real Gapcited by 21
2025StyleDrive: Towards Driving-Style Aware Benchmarking of End-To-End Autonomous Drivingcited by 20
2021Deep Structured Reactive Planningcited by 18
2021Learned Visual Navigation for Under-Canopy Agricultural Robotscited by 18
2023Coaching a Teachable Studentcited by 17
2022SelfD: Self-Learning Large-Scale Driving Policies From the Webcited by 16
2024A Comprehensive Survey of Cross-Domain Policy Transfer for Embodied Agentscited by 16
2022Learning Robotic Navigation from Experience: Principles, Methods, and Recent Resultscited by 13
2023Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban Environmentscited by 11
2024QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Drivingcited by 11
2024Vision Transformers for End-to-End Vision-Based Quadrotor Obstacle Avoidancecited by 10
2023Towards Generalist Robots: A Promising Paradigm via Generative Simulationcited by 9
2023Learning to Drive Anywherecited by 9
2024xTED: Cross-Domain Adaptation via Diffusion-Based Trajectory Editingcited by 9
2020Driving Through Ghosts: Behavioral Cloning with False Positivescited by 8
2022DOROTHIE: Spoken Dialogue for Handling Unexpected Situations in Interactive Autonomous Driving Agentscited by 8
2023Scaling Vision-based End-to-End Driving with Multi-View Attention Learningcited by 8
2024CarFormer: Self-Driving with Learned Object-Centric Representationscited by 8
2021Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Imagescited by 6
2024Autonomous Driving Small-Scale Cars: A Survey of Recent Developmentcited by 6
2021Increasing the Efficiency of Policy Learning for Autonomous Vehicles by Multi-Task Representation Learningcited by 5
2021A Versatile and Efficient Reinforcement Learning Framework for Autonomous Drivingcited by 5

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