mlsanityData to 7 Oct 2026

iCOIL: Scenario Aware Autonomous Parking Via Integrated Constrained Optimization and Imitation Learning

arXiv 2305.13663 ↗First posted 23 May 2023cs.ROcited by 0

On the map: Offline RL and imitation learning from data → Imitation and offline RL for driving

Abstract (arXiv)

Autonomous parking (AP) is an emering technique to navigate an intelligent vehicle to a parking space without any human intervention. Existing AP methods based on mathematical optimization or machine learning may lead to potential failures due to either excessive execution time or lack of generalization. To fill this gap, this paper proposes an integrated constrained optimization and imitation learning (iCOIL) approach to achieve efficient and reliable AP. The iCOIL method has two candidate working modes, i.e., CO and IL, and adopts a hybrid scenario analysis (HSA) model to determine the better mode under various scenarios. We implement and verify iCOIL on the Macao Car Racing Metaverse (MoCAM) platform. Results show that iCOIL properly adapts to different scenarios during the entire AP procedure, and achieves significantly larger success rates than other benchmarks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data. Builds on 10 of its 22 references that are in the map. First posted on arXiv 23 May 2023.

Builds on

10 of 22 references.

Closest ancestors · 2

Same direction, newest first

  1. 2020Deep Reinforcement Learning for Autonomous Driving: A Survey2 Feb
  2. 2019Urban Driving with Conditional Imitation Learning30 Nov

Other references · 8

  1. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  2. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  3. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,345
  4. 2018HG-DAgger: Interactive Imitation Learning with Human Expertscited by 245
  5. 2017Optimization-Based Collision Avoidancecited by 28
  6. 2022Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verificationcited by 14
  7. 2022RDA: An Accelerated Collision Free Motion Planner for Autonomous Navigation in Cluttered Environmentscited by 13
  8. 2020Peer-Assisted Robotic Learning: A Data-Driven Collaborative Learning Approach for Cloud Robotic Systemscited by 9

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025SEG-Parking: Towards Safe, Efficient, and Generalizable Autonomous Parking via End-to-End Offline Reinforcement Learning
  2. 2026ParkingWorld: End-to-End Autonomous Parking Reinforcement Learning from Corrective Experience in 3DGS Simulation ↗
  3. 2025MultiPark: Multimodal Parking Transformer with Next-Segment Prediction
  4. 2026From Semantic Decisions to Feasible Trajectories: Self-Evolving LLM-Guided Optimal Control for Narrow-Space Parking
  5. 2024ParkingE2E: Camera-based End-to-end Parking Network, from Images to Planning
  6. 2022Autonomous Vehicle Parking in Dynamic Environments: An Integrated System with Prediction and Motion Planning
  7. 2026Learning Reliable Parking Policies via Offline Reinforcement Learning with Quantized Action Representations
  8. 2023Towards Sim2Real Transfer of Autonomy Algorithms using AutoDRIVE Ecosystem