# Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligence

arXiv: https://arxiv.org/abs/2103.10290 · HTML: https://mlsanity.com/paper/2103.10290/

## Key facts

- Cited by 27 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 18 of its 54 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Placed in

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

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [A Convergence Theory for Deep Learning via Over-Parameterization](https://mlsanity.com/paper/1811.03962/) (2018)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016)
- [Deep Reinforcement Learning framework for Autonomous Driving](https://mlsanity.com/paper/1704.02532/) (2017)
- [Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications](https://mlsanity.com/paper/1812.11794/) (2018)
- [Model-free Deep Reinforcement Learning for Urban Autonomous Driving](https://mlsanity.com/paper/1904.09503/) (2019)
- [Deep Reinforcement Learning for Multi-objective Optimization](https://mlsanity.com/paper/1906.02386/) (2019)
- [Efficient representation and approximation of model predictive control laws via deep learning](https://mlsanity.com/paper/1806.10644/) (2018)
- [A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers](https://mlsanity.com/paper/1804.07871/) (2018)
- [Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learning](https://mlsanity.com/paper/1809.02926/) (2018)
- [Numerically Stable Dynamic Bicycle Model for Discrete-time Control](https://mlsanity.com/paper/2011.09612/) (2020)
- [Mixed Policy Gradient: off-policy reinforcement learning driven jointly by data and model](https://mlsanity.com/paper/2102.11513/) (2021)
- [Trajectory Generation by Chance Constrained Nonlinear MPC with Probabilistic Prediction](https://mlsanity.com/paper/2006.07907/) (2020)

## Built on by (most cited first, 27 of 27)

- [LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving](https://mlsanity.com/paper/2310.03026/) (2023)
- [Diffusion Actor-Critic with Entropy Regulator](https://mlsanity.com/paper/2405.15177/) (2024)
- [Reachability Constrained Reinforcement Learning](https://mlsanity.com/paper/2205.07536/) (2022)
- [Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model](https://mlsanity.com/paper/2210.04017/) (2022)
- [Towards Knowledge-driven Autonomous Driving](https://mlsanity.com/paper/2312.04316/) (2023)
- [SEPT: Towards Efficient Scene Representation Learning for Motion Prediction](https://mlsanity.com/paper/2309.15289/) (2023)
- [Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability Certificate](https://mlsanity.com/paper/2210.07553/) (2022)
- [Safe Reinforcement Learning with Dual Robustness](https://mlsanity.com/paper/2309.06835/) (2023)
- [Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation](https://mlsanity.com/paper/2311.16091/) (2023)
- [Self-learned Intelligence for Integrated Decision and Control of Automated Vehicles at Signalized Intersections](https://mlsanity.com/paper/2110.12359/) (2021)
- [Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey](https://mlsanity.com/paper/2404.00340/) (2024)
- [Feasible Policy Iteration for Safe Reinforcement Learning](https://mlsanity.com/paper/2304.08845/) (2023)
- [Robust Safe Reinforcement Learning under Adversarial Disturbances](https://mlsanity.com/paper/2310.07207/) (2023)
- [Optimization Landscape of Policy Gradient Methods for Discrete-time Static Output Feedback](https://mlsanity.com/paper/2310.19022/) (2023)
- [Conformal Symplectic Optimization for Stable Reinforcement Learning](https://mlsanity.com/paper/2412.02291/) (2024)
- [Improve Generalization of Driving Policy at Signalized Intersections with Adversarial Learning](https://mlsanity.com/paper/2204.04403/) (2022)
- [Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning](https://mlsanity.com/paper/2311.03756/) (2023)
- [Zeroth-Order Actor-Critic: An Evolutionary Framework for Sequential Decision Problems](https://mlsanity.com/paper/2201.12518/) (2022)
- [Policy Bifurcation in Safe Reinforcement Learning](https://mlsanity.com/paper/2403.12847/) (2024)
- [The Feasibility Theory of Constrained Reinforcement Learning: A Tutorial Study](https://mlsanity.com/paper/2404.10064/) (2024)
- [Q-learning-based Model-free Safety Filter](https://mlsanity.com/paper/2411.19809/) (2024)
- [A Driving Regime-Embedded Deep Learning Framework for Modeling Intra-Driver Heterogeneity in Multi-Scale Car-Following Dynamics](https://mlsanity.com/paper/2506.05902/) (2025)
- [Integrated Decision and Control for High-Level Automated Vehicles by Mixed Policy Gradient and Its Experiment Verification](https://mlsanity.com/paper/2210.10613/) (2022)
- [Bridging the Gap between Newton-Raphson Method and Regularized Policy Iteration](https://mlsanity.com/paper/2310.07211/) (2023)
- [Distributional Soft Actor-Critic with Harmonic Gradient for Safe and Efficient Autonomous Driving in Multi-lane Scenarios](https://mlsanity.com/paper/2505.13532/) (2025)
- [NeuralParker: A Reinforcement Learning Planner for Irregular Parking Environments](https://mlsanity.com/paper/2608.24485/) (2026)
- [Evaluation Is All You Need for Multi-Modal Autonomous Driving](https://mlsanity.com/paper/2609.30818/) (2026)

## Abstract (arXiv)

Decision and control are core functionalities of high-level automated vehicles. Current mainstream methods, such as functionality decomposition and end-to-end reinforcement learning (RL), either suffer high time complexity or poor interpretability and adaptability on real-world autonomous driving tasks. In this paper, we present an interpretable and computationally efficient framework called integrated decision and control (IDC) for automated vehicles, which decomposes the driving task into static path planning and dynamic optimal tracking that are structured hierarchically. First, the static path planning generates several candidate paths only considering static traffic elements. Then, the dynamic optimal tracking is designed to track the optimal path while considering the dynamic obstacles. To that end, we formulate a constrained optimal control problem (OCP) for each candidate path, optimize them separately and follow the one with the best tracking performance. To unload the heavy online computation, we propose a model-based reinforcement learning (RL) algorithm that can be served as an approximate constrained OCP solver. Specifically, the OCPs for all paths are considered together to construct a single complete RL problem and then solved offline in the form of value and policy networks, for real-time online path selecting and tracking respectively. We verify our framework in both simulations and the real world. Results show that compared with baseline methods IDC has an order of magnitude higher online computing efficiency, as well as better driving performance including traffic efficiency and safety. In addition, it yields great interpretability and adaptability among different driving tasks. The effectiveness of the proposed method is also demonstrated in real road tests with complicated traffic conditions.