# A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning

arXiv: https://arxiv.org/abs/1707.02515 · HTML: https://mlsanity.com/paper/1707.02515/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2017.

## Built on by (most cited first, 20 of 20)

- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Recursive Introspection: Teaching Language Model Agents How to Self-Improve](https://mlsanity.com/paper/2407.18219/) (2024)
- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety](https://mlsanity.com/paper/1903.00640/) (2019)
- [Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification](https://mlsanity.com/paper/2502.02133/) (2025)
- [A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning](https://mlsanity.com/paper/2101.06778/) (2021)
- [Discriminator-Guided Model-Based Offline Imitation Learning](https://mlsanity.com/paper/2207.00244/) (2022)
- [Bootstrapped Model Predictive Control](https://mlsanity.com/paper/2503.18871/) (2025)
- [PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving](https://mlsanity.com/paper/2011.00509/) (2020)
- [A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles](https://mlsanity.com/paper/2105.14218/) (2021)
- [Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning](https://mlsanity.com/paper/2310.18247/) (2023)
- [SHAIL: Safety-Aware Hierarchical Adversarial Imitation Learning for Autonomous Driving in Urban Environments](https://mlsanity.com/paper/2204.01922/) (2022)
- [Multi-lane Cruising Using Hierarchical Planning and Reinforcement Learning](https://mlsanity.com/paper/2110.00650/) (2021)
- [Enhancing Inverse Reinforcement Learning through Encoding Dynamic Information in Reward Shaping](https://mlsanity.com/paper/2410.03847/) (2024)
- [Efficient Motion Planning for Automated Lane Change based on Imitation Learning and Mixed-Integer Optimization](https://mlsanity.com/paper/1904.08784/) (2019)
- [MMCD: Multi-Modal Collaborative Decision-Making for Connected Autonomy with Knowledge Distillation](https://mlsanity.com/paper/2509.18198/) (2025)
- [Learning energy-efficient driving behaviors by imitating experts](https://mlsanity.com/paper/2208.12534/) (2022)
- [A Survey on Hybrid Motion Planning Methods for Automated Driving Systems](https://mlsanity.com/paper/2406.05575/) (2024)
- [Optimality Inductive Biases and Agnostic Guidelines for Offline Reinforcement Learning](https://mlsanity.com/paper/2107.01407/) (2021)
- [Off-dynamics Conditional Diffusion Planners](https://mlsanity.com/paper/2410.12238/) (2024)

## Abstract (arXiv)

For safe and efficient planning and control in autonomous driving, we need a driving policy which can achieve desirable driving quality in long-term horizon with guaranteed safety and feasibility. Optimization-based approaches, such as Model Predictive Control (MPC), can provide such optimal policies, but their computational complexity is generally unacceptable for real-time implementation. To address this problem, we propose a fast integrated planning and control framework that combines learning- and optimization-based approaches in a two-layer hierarchical structure. The first layer, defined as the "policy layer", is established by a neural network which learns the long-term optimal driving policy generated by MPC. The second layer, called the "execution layer", is a short-term optimization-based controller that tracks the reference trajecotries given by the "policy layer" with guaranteed short-term safety and feasibility. Moreover, with efficient and highly-representative features, a small-size neural network is sufficient in the "policy layer" to handle many complicated driving scenarios. This renders online imitation learning with Dataset Aggregation (DAgger) so that the performance of the "policy layer" can be improved rapidly and continuously online. Several exampled driving scenarios are demonstrated to verify the effectiveness and efficiency of the proposed framework.