# Learning the References of Online Model Predictive Control for Urban Self-Driving

arXiv: https://arxiv.org/abs/2308.15808 · HTML: https://mlsanity.com/paper/2308.15808/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 20 of its 26 references that are in the map.
- First posted on arXiv 30 Aug 2023.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control](https://mlsanity.com/paper/1811.01848/) (2018)
- [Cautious Model Predictive Control using Gaussian Process Regression](https://mlsanity.com/paper/1705.10702/) (2017)
- [Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning](https://mlsanity.com/paper/2008.07971/) (2020)
- [Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning](https://mlsanity.com/paper/2103.14666/) (2021)
- [Real-time Neural-MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms](https://mlsanity.com/paper/2203.07747/) (2022)
- [Data-Driven MPC for Quadrotors](https://mlsanity.com/paper/2102.05773/) (2021)
- [KNODE-MPC: A Knowledge-based Data-driven Predictive Control Framework for Aerial Robots](https://mlsanity.com/paper/2109.04821/) (2021)
- [Policy Search for Model Predictive Control with Application to Agile Drone Flight](https://mlsanity.com/paper/2112.03850/) (2021)
- [Practical Reinforcement Learning For MPC: Learning from sparse objectives in under an hour on a real robot](https://mlsanity.com/paper/2003.03200/) (2020)
- [Online Trajectory Generation with Distributed Model Predictive Control for Multi-Robot Motion Planning](https://mlsanity.com/paper/1909.05150/) (2019)
- [Learning High-Level Policies for Model Predictive Control](https://mlsanity.com/paper/2007.10284/) (2020)
- [A Two-Stage Optimization-based Motion Planner for Safe Urban Driving](https://mlsanity.com/paper/2002.02215/) (2020)
- [Multi-Modal Model Predictive Control through Batch Non-Holonomic Trajectory Optimization: Application to Highway Driving](https://mlsanity.com/paper/2109.10392/) (2021)
- [Chance-Aware Lane Change with High-Level Model Predictive Control Through Curriculum Reinforcement Learning](https://mlsanity.com/paper/2303.03723/) (2023)
- [Learning Agile Flight Maneuvers: Deep SE(3) Motion Planning and Control for Quadrotors](https://mlsanity.com/paper/2209.11097/) (2022)

## Built on by (most cited first, 2 of 2)

- [Learning to Plan, Planning to Learn: Adaptive Hierarchical RL-MPC for Sample-Efficient Decision Making](https://mlsanity.com/paper/2512.17091/) (2025)
- [Orchestrate, Generate, Reflect: A VLM-Based Multi-Agent Collaboration Framework for Automated Driving Policy Learning](https://mlsanity.com/paper/2509.17042/) (2025)

## Abstract (arXiv)

In this work, we propose a novel learning-based model predictive control (MPC) framework for motion planning and control of urban self-driving. In this framework, instantaneous references and cost functions of online MPC are learned from raw sensor data without relying on any oracle or predicted states of traffic. Moreover, driving safety conditions are latently encoded via the introduction of a learnable instantaneous reference vector. In particular, we implement a deep reinforcement learning (DRL) framework for policy search, where practical and lightweight raw observations are processed to reason about the traffic and provide the online MPC with instantaneous references. The proposed approach is validated in a high-fidelity simulator, where our development manifests remarkable adaptiveness to complex and dynamic traffic. Furthermore, sim-to-real deployments are also conducted to evaluate the generalizability of the proposed framework in various real-world applications. Also, we provide the open-source code and video demonstrations at the project website: https://latent-mpc.github.io/.