mlsanityData to 14 Oct 2026

MPC-based Imitation Learning for Safe and Human-like Autonomous Driving

arXiv 2206.12348 ↗First posted 24 Jun 2022cs.RO cs.SY eess.SYcited by 1

On the map: End-to-end autonomous driving → Imitation-learning end-to-end planners

also in 1 more
Abstract (arXiv)

To ensure user acceptance of autonomous vehicles (AVs), control systems are being developed to mimic human drivers from demonstrations of desired driving behaviors. Imitation learning (IL) algorithms serve this purpose, but struggle to provide safety guarantees on the resulting closed-loop system trajectories. On the other hand, Model Predictive Control (MPC) can handle nonlinear systems with safety constraints, but realizing human-like driving with it requires extensive domain knowledge. This work suggests the use of a seamless combination of the two techniques to learn safe AV controllers from demonstrations of desired driving behaviors, by using MPC as a differentiable control layer within a hierarchical IL policy. With this strategy, IL is performed in closed-loop and end-to-end, through parameters in the MPC cost, model or constraints. Experimental results of this methodology are analyzed for the design of a lane keeping control system, learned via behavioral cloning from observations (BCO), given human demonstrations on a fixed-base driving simulator.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving. Builds on 13 of its 30 references that are in the map. First posted on arXiv 24 Jun 2022.

Builds on

13 of 30 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients27 Sep

Other references · 12

  1. 2018Behavioral Cloning from Observationcited by 492
  2. 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 490
  3. 2018ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worstcited by 397
  4. 2021TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviorscited by 193
  5. 2019A Divergence Minimization Perspective on Imitation Learning Methodscited by 190
  6. 2018Differentiable MPC for End-to-end Planning and Controlcited by 189
  7. 2015Learning Continuous Control Policies by Stochastic Value Gradientscited by 162
  8. 2019Urban Driving with Conditional Imitation Learningcited by 86
  9. 2019Data-driven Economic NMPC using Reinforcement Learningcited by 68
  10. 2019Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safetycited by 49
  11. 2020Combining system identification with reinforcement learning-based MPCcited by 11
  12. 2020PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Drivingcited by 11

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026C-CoT: Counterfactual Chain-of-Thought with Vision-Language Models for Safe Autonomous Drivingcited by 3

Similar, not cited

Close in wording, no citation either way

  1. 2022Evaluation of MPC-based Imitation Learning for Human-like Autonomous Driving
  2. 2021Safe Imitation Learning on Real-Life Highway Data for Human-like Autonomous Driving
  3. 2022Learning from Demonstrations of Critical Driving Behaviours Using Driver's Risk Field
  4. 2020Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation Learning
  5. 2025Safety-Aware Imitation Learning via MPC-Guided Disturbance Injection
  6. 2022Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios
  7. 2026Stability-Aware Imitation Learning from Model Predictive Control for Autonomous Vehicle Lateral Control: Exact Q-Loss and a Novel Training Procedure
  8. 2021A predictive safety filter for learning-based racing control