mlsanityData to 14 Oct 2026

Deep Object-Centric Policies for Autonomous Driving

arXiv 1811.05432 ↗First posted 13 Nov 2018cs.AI cs.CV cs.ROcited by 41

Not on the map. Why?

Abstract (arXiv)

While learning visuomotor skills in an end-to-end manner is appealing, deep neural networks are often uninterpretable and fail in surprising ways. For robotics tasks, such as autonomous driving, models that explicitly represent objects may be more robust to new scenes and provide intuitive visualizations. We describe a taxonomy of "object-centric" models which leverage both object instances and end-to-end learning. In the Grand Theft Auto V simulator, we show that object-centric models outperform object-agnostic methods in scenes with other vehicles and pedestrians, even with an imperfect detector. We also demonstrate that our architectures perform well on real-world environments by evaluating on the Berkeley DeepDrive Video dataset, where an object-centric model outperforms object-agnostic models in the low-data regimes.

Key facts. Cited by 41 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Nov 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2019: 1; 2020: 0; 2021: 11; 2022: 6; 2023: 10; 2024: 7; 2025: 2; 2026: 4.

41 papers.

Sort:

By area:

  1. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 490
  2. 2022VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priorscited by 136
  3. 2022DRAMA: Joint Risk Localization and Captioning in Drivingcited by 126
  4. 2022PlanT: Explainable Planning Transformers via Object-Level Representationscited by 105
  5. 2021Explainability of deep vision-based autonomous driving systems: Review and challengescited by 80
  6. 2022Concept Bottleneck Model with Additional Unsupervised Conceptscited by 79
  7. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 61
  8. 2023Learning Generalizable Manipulation Policies with Object-Centric 3D Representationscited by 61
  9. 2023HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Drivingcited by 50
  10. 2024Vision-based Manipulation from Single Human Video with Open-World Object Graphscited by 50
  11. 2019Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safetycited by 49
  12. 2025ControlVLA: Few-shot Object-centric Adaptation for Pre-trained Vision-Language-Action Modelscited by 43
29 more
  1. 2021Contrastive Domain Adaptationcited by 36
  2. 2024Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrationscited by 35
  3. 2022Grounding Visual Representations with Texts for Domain Generalizationcited by 30
  4. 2023The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Reviewcited by 29
  5. 2021Learning by Watchingcited by 22
  6. 2022Visuomotor Control in Multi-Object Scenes Using Object-Aware Representationscited by 21
  7. 2024Object-Centric Instruction Augmentation for Robotic Manipulationcited by 16
  8. 2024Composing Pre-Trained Object-Centric Representations for Robotics From "What" and "Where" Foundation Modelscited by 16
  9. 2024Language-Guided Object-Centric Diffusion Policy for Generalizable and Collision-Aware Robotic Manipulationcited by 14
  10. 2021Behavioral Research and Practical Models of Drivers' Attentioncited by 9
  11. 2021DROID: Driver-centric Risk Object Identificationcited by 9
  12. 2023Driving through the Concept Gridlock: Unraveling Explainability Bottlenecks in Automated Drivingcited by 9
  13. 2023Ego-Vehicle Action Recognition based on Semi-Supervised Contrastive Learningcited by 8
  14. 2024Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Drivingcited by 7
  15. 2026OCRA: Object-Centric Learning with 3D and Tactile Priors for Human-to-Robot Action Transfercited by 6
  16. 2021Instance-Aware Predictive Navigation in Multi-Agent Environmentscited by 5
  17. 2021Automatic Curricula via Expert Demonstrationscited by 5
  18. 2021Adversarial Mixture Density Networks: Learning to Drive Safely from Collision Datacited by 4
  19. 2023Learning Road Scene-level Representations via Semantic Region Predictioncited by 4
  20. 2024Improving Explainable Object-induced Model through Uncertainty for Automated Vehiclescited by 4
  21. 2025Interpretable Decision-Making for End-to-End Autonomous Drivingcited by 4
  22. 2023Generalizable Imitation Learning Through Pre-Trained Representationscited by 3
  23. 2021Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cagescited by 1
  24. 2023Depth-guided Free-space Segmentation for a Mobile Robotcited by 1
  25. 2026Towards Driver Behavior Understanding: Weakly-Supervised Risk Perception in Driving Scenescited by 1
  26. 2026An End-to-End Decision-Aware Multi-Scale Attention-Based Model for Explainable Autonomous Drivingcited by 1
  27. 2021n-hot: Efficient bit-level sparsity for powers-of-two neural network quantizationcited by 0
  28. 2023DRUformer: Enhancing the driving scene Important object detection with driving relationship self-understandingcited by 0
  29. 2026Beyond Fixed Thresholds and Domain-Specific Benchmarks for Explainable Multi-Task Classification in Autonomous Vehiclescited by 0