mlsanityData to 14 Oct 2026

Multi-Task Conditional Imitation Learning for Autonomous Navigation at Crowded Intersections

arXiv 2202.10124 ↗First posted 21 Feb 2022cs.RO cs.CVcited by 4

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

Abstract (arXiv)

In recent years, great efforts have been devoted to deep imitation learning for autonomous driving control, where raw sensory inputs are directly mapped to control actions. However, navigating through densely populated intersections remains a challenging task due to uncertainty caused by uncertain traffic participants. We focus on autonomous navigation at crowded intersections that require interaction with pedestrians. A multi-task conditional imitation learning framework is proposed to adapt both lateral and longitudinal control tasks for safe and efficient interaction. A new benchmark called IntersectNav is developed and human demonstrations are provided. Empirical results show that the proposed method can achieve a success rate gain of up to 30% compared to the state-of-the-art.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data. Builds on 26 of its 39 references that are in the map. First posted on arXiv 21 Feb 2022.

Builds on

26 of 39 references.

Closest ancestors · 2

Same direction, newest first

  1. 2021Multi-task Learning with Attention for End-to-end Autonomous Driving21 Apr
  2. 2018Conditional Affordance Learning for Driving in Urban Environments18 Jun

Other references · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
  4. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,358
  5. 2016End to End Learning for Self-Driving Carscited by 1,009
  6. 2017A Survey on Multi-Task Learningcited by 840
  7. 2017An Overview of Multi-Task Learning in Deep Neural Networkscited by 803
  8. 2017End-to-end Driving via Conditional Imitation Learningcited by 485
  9. 2014Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecturecited by 483
  10. 2019Learning by Cheatingcited by 383
  11. 2019Exploring the Limitations of Behavior Cloning for Autonomous Drivingcited by 346
  12. 2019Causal Confusion in Imitation Learningcited by 288
12 more
  1. 2015DeepDriving: Learning Affordance for Direct Perception in Autonomous Drivingcited by 231
  2. 2013OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networkscited by 224
  3. 2021End-to-End Urban Driving by Imitating a Reinforcement Learning Coachcited by 194
  4. 2016End-to-end Learning of Driving Models from Large-scale Video Datasetscited by 148
  5. 2019A Survey of Deep Learning Applications to Autonomous Vehicle Controlcited by 106
  6. 2018CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-drivingcited by 97
  7. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 61
  8. 2019Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environmentscited by 18
  9. 2021Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlookscited by 15
  10. 2015Understand Scene Categories by Objects: A Semantic Regularized Scene Classifier Using Convolutional Neural Networkscited by 14
  11. 2018Shared Multi-Task Imitation Learning for Indoor Self-Navigationcited by 5
  12. 2020Autonomous Driving at Intersections: A Critical-Turning-Point Approach for Left Turnscited by 4

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 1.

4 papers.

  1. 2022Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baselinecited by 248
  2. 2023Vision Language Models in Autonomous Driving: A Survey and Outlookcited by 126
  3. 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 114
  4. 2024Application of Multimodal Large Language Models in Autonomous Drivingcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2019Urban Driving with Conditional Imitation Learning
  2. 2021Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars
  3. 2025Learning to Drive by Imitating Surrounding Vehicles
  4. 2019Conditional Vehicle Trajectories Prediction in CARLA Urban Environment
  5. 2022SHAIL: Safety-Aware Hierarchical Adversarial Imitation Learning for Autonomous Driving in Urban Environments
  6. 2022Dynamic Conditional Imitation Learning for Autonomous Driving
  7. 2022Model-Based Imitation Learning for Urban Driving
  8. 2022Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios