mlsanityData to 7 Oct 2026

Enhance Planning with Physics-informed Safety Controller for End-to-end Autonomous Driving

arXiv 2405.00316 ↗First posted 1 May 2024cs.RO cs.SY eess.SYcited by 4

On the map: End-to-end autonomous driving → End-to-end system architecture

Abstract (arXiv)

Recent years have seen a growing research interest in applications of Deep Neural Networks (DNN) on autonomous vehicle technology. The trend started with perception and prediction a few years ago and it is gradually being applied to motion planning tasks. Despite the performance of networks improve over time, DNN planners inherit the natural drawbacks of Deep Learning. Learning-based planners have limitations in achieving perfect accuracy on the training dataset and network performance can be affected by out-of-distribution problem. In this paper, we propose FusionAssurance, a novel trajectory-based end-to-end driving fusion framework which combines physics-informed control for safety assurance. By incorporating Potential Field into Model Predictive Control, FusionAssurance is capable of navigating through scenarios that are not included in the training dataset and scenarios where neural network fail to generalize. The effectiveness of the approach is demonstrated by extensive experiments under various scenarios on the CARLA benchmark.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving. Builds on 33 of its 39 references that are in the map. First posted on arXiv 1 May 2024.

Builds on

33 of 39 references.

Closest ancestors · 5

Same direction, newest first

  1. 2023Hidden Biases of End-to-End Driving Models13 Jun
  2. 2022Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline16 Jun
  3. 2022TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving31 May
  4. 2021Multi-Modal Fusion Transformer for End-to-End Autonomous Driving19 Apr
  5. 2021Learning Interpretable End-to-End Vision-Based Motion Planning for Autonomous Driving with Optical Flow Distillation18 Apr

Other references · 28

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2020End-to-End Object Detection with Transformerscited by 7,384
  4. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  5. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  6. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  7. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
  8. 2021ViLT: Vision-and-Language Transformer Without Convolution or Region Supervisioncited by 1,224
  9. 2017End-to-end Driving via Conditional Imitation Learningcited by 479
  10. 2019A Survey of Autonomous Driving: Common Practices and Emerging Technologiescited by 373
  11. 2019Learning by Cheatingcited by 361
  12. 2019Exploring the Limitations of Behavior Cloning for Autonomous Drivingcited by 343
16 more
  1. 2019Control Barrier Functions: Theory and Applicationscited by 332
  2. 2015DeepDriving: Learning Affordance for Direct Perception in Autonomous Drivingcited by 229
  3. 2022Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformercited by 211
  4. 2021End-to-End Urban Driving by Imitating a Reinforcement Learning Coachcited by 191
  5. 2022Model-Based Imitation Learning for Urban Drivingcited by 149
  6. 2022Learning from All Vehiclescited by 135
  7. 2020A Survey of End-to-End Driving: Architectures and Training Methodscited by 123
  8. 2021Learning to drive from a world on railscited by 114
  9. 2022Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Drivingcited by 71
  10. 2021GRI: General Reinforced Imitation and its Application to Vision-Based Autonomous Drivingcited by 59
  11. 2020Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationshipscited by 36
  12. 2020Perceive, Attend, and Drive: Learning Spatial Attention for Safe Self-Drivingcited by 24
  13. 2020Numerically Stable Dynamic Bicycle Model for Discrete-time Controlcited by 15
  14. 2023Towards Safety Assured End-to-End Vision-Based Control for Autonomous Racingcited by 4
  15. 2023Integrated Behavior Planning and Motion Control for Autonomous Vehicles with Traffic Rules Compliancecited by 2
  16. 2023Geometry-Aware Safety-Critical Local Reactive Controller for Robot Navigation in Unknown and Cluttered Environmentscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025A Survey on Vision-Language-Action Models for Autonomous Drivingcited by 72
  2. 2026MVAdapt: Zero-Shot Multi-Vehicle Adaptation for End-to-End Autonomous Drivingcited by 1
  3. 2025A Physics-informed End-to-End Occupancy Framework for Motion Planning of Autonomous Vehiclescited by 0
  4. 2026Closed-Loop Refinement and Execution for Learned Driving Plannerscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Combining Policy Gradient and Safety-Based Control for Autonomous Driving
  2. 2026Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment
  3. 2022Safe Real-World Autonomous Driving by Learning to Predict and Plan with a Mixture of Experts
  4. 2021SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies
  5. 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey
  6. 2023End-to-end Autonomous Driving using Deep Learning: A Systematic Review
  7. 2025Drive in Corridors: Enhancing the Safety of End-to-end Autonomous Driving via Corridor Learning and Planning
  8. 2025HyPlan: Hybrid Learning-Assisted Planning Under Uncertainty for Safe Autonomous Driving