mlsanityData to 14 Oct 2026

Safety Reinforced Model Predictive Control (SRMPC): Improving MPC with Reinforcement Learning for Motion Planning in Autonomous Driving

arXiv 2512.03774 ↗First posted 3 Dec 2025cs.ROcited by 0

On the map: End-to-end autonomous driving → Reinforcement learning for driving

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Abstract (arXiv)

Model predictive control (MPC) is widely used for motion planning, particularly in autonomous driving. Real-time capability of the planner requires utilizing convex approximation of optimal control problems (OCPs) for the planner. However, such approximations confine the solution to a subspace, which might not contain the global optimum. To address this, we propose using safe reinforcement learning (SRL) to obtain a new and safe reference trajectory within MPC. By employing a learning-based approach, the MPC can explore solutions beyond the close neighborhood of the previous one, potentially finding global optima. We incorporate constrained reinforcement learning (CRL) to ensure safety in automated driving, using a handcrafted energy function-based safety index as the constraint objective to model safe and unsafe regions. Our approach utilizes a state-dependent Lagrangian multiplier, learned concurrently with the safe policy, to solve the CRL problem. Through experimentation in a highway scenario, we demonstrate the superiority of our approach over both MPC and SRL in terms of safety and performance measures.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 15 of its 41 references that are in the map. First posted on arXiv 3 Dec 2025.

Builds on

15 of 41 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning15 Jul

Other references · 14

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Differentiable MPC for End-to-end Planning and Controlcited by 189
  3. 2017OSQP: An Operator Splitting Solver for Quadratic Programscited by 161
  4. 2017On a Formal Model of Safe and Scalable Self-driving Carscited by 131
  5. 2021Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificatescited by 74
  6. 2021Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safetycited by 30
  7. 2021Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learningcited by 27
  8. 2020Model Predictive Contouring Control for Collision Avoidance in Unstructured Dynamic Environmentscited by 21
  9. 2019Safe Control Algorithms Using Energy Functions: A Unified Framework, Benchmark, and New Directionscited by 17
  10. 2019Learning-based Model Predictive Control for Safe Exploration and Reinforcement Learningcited by 16
  11. 2021Deep Learning Explicit Differentiable Predictive Control Laws for Buildingscited by 11
  12. 2019Safe Adaptation with Multiplicative Uncertainties Using Robust Safe Set Algorithmcited by 8
2 more
  1. 2018Linearized Gaussian Processes for Fast Data-driven Model Predictive Controlcited by 4
  2. 2021Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learningcited by 4

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  1. 2024Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving
  2. 2025Learning Safe Autonomous Driving Policies Using Predictive Safety Representations
  3. 2025Safe Reinforcement Learning with a Predictive Safety Filter for Motion Planning and Control: A Drifting Vehicle Example
  4. 2026Safe Reinforcement Learning using Ideas from Model Predictive Control
  5. 2025Safe model-based Reinforcement Learning via Model Predictive Control and Control Barrier Functions
  6. 2022SafeRL-Kit: Evaluating Efficient Reinforcement Learning Methods for Safe Autonomous Driving
  7. 2025Human-aligned Safe Reinforcement Learning for Highway On-Ramp Merging in Dense Traffic
  8. 2025Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving