# Synergizing Decision Making and Trajectory Planning Using Two-Stage Optimization for Autonomous Vehicles

arXiv: https://arxiv.org/abs/2411.18974 · HTML: https://mlsanity.com/paper/2411.18974/

## Key facts

- Cited by 1 papers 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 23 of its 54 references that are in the map.
- First posted on arXiv 28 Nov 2024.

## Placed in

- End-to-end autonomous driving / [Imitation-learning end-to-end planners](https://mlsanity.com/driving/imitation-learning-end-to-end-planners/)

## Builds on (most cited first, 23 of 23)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst](https://mlsanity.com/paper/1812.03079/) (2018)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016)
- [Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives](https://mlsanity.com/paper/2303.09824/) (2023)
- [Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys](https://mlsanity.com/paper/2303.17220/) (2023)
- [Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles](https://mlsanity.com/paper/1910.04586/) (2019)
- [Hierarchical Game-Theoretic Planning for Autonomous Vehicles](https://mlsanity.com/paper/1810.05766/) (2018)
- [Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach](https://mlsanity.com/paper/2005.11064/) (2020)
- [Search-based optimal motion planning for automated driving](https://mlsanity.com/paper/1803.04868/) (2018)
- [An Integrated Framework of Decision Making and Motion Planning for Autonomous Vehicles Considering Social Behaviors](https://mlsanity.com/paper/2005.11059/) (2020)
- [Alternating Direction Method of Multipliers for Constrained Iterative LQR in Autonomous Driving](https://mlsanity.com/paper/2011.00462/) (2020)
- [Cooperative Decision Making of Connected Automated Vehicles at Multi-lane Merging Zone: A Coalitional Game Approach](https://mlsanity.com/paper/2103.07887/) (2021)
- [Decentralized iLQR for Cooperative Trajectory Planning of Connected Autonomous Vehicles via Dual Consensus ADMM](https://mlsanity.com/paper/2301.04386/) (2023)
- [A Two-Stage Optimization-based Motion Planner for Safe Urban Driving](https://mlsanity.com/paper/2002.02215/) (2020)
- [Potential iLQR: A Potential-Minimizing Controller for Planning Multi-Agent Interactive Trajectories](https://mlsanity.com/paper/2107.04926/) (2021)
- [Receding Horizon Planning with Rule Hierarchies for Autonomous Vehicles](https://mlsanity.com/paper/2212.03323/) (2022)
- [Chance-Aware Lane Change with High-Level Model Predictive Control Through Curriculum Reinforcement Learning](https://mlsanity.com/paper/2303.03723/) (2023)
- [Improved Consensus ADMM for Cooperative Motion Planning of Large-Scale Connected Autonomous Vehicles with Limited Communication](https://mlsanity.com/paper/2401.09032/) (2024)
- [Infusing Reachability-Based Safety into Planning and Control for Multi-agent Interactions](https://mlsanity.com/paper/2008.00067/) (2020)
- [A Universal Cooperative Decision-Making Framework for Connected Autonomous Vehicles with Generic Road Topologies](https://mlsanity.com/paper/2401.04968/) (2024)

## Built on by (most cited first, 1 of 1)

- [Integrating Decision-Making Into Differentiable Optimization Guided Learning for End-to-End Planning of Autonomous Vehicles](https://mlsanity.com/paper/2412.01234/) (2024)

## Abstract (arXiv)

This paper introduces a local planner that synergizes the decision making and trajectory planning modules towards autonomous driving. The decision making and trajectory planning tasks are jointly formulated as a nonlinear programming problem with an integrated objective function. However, integrating the discrete decision variables into the continuous trajectory optimization leads to a mixed-integer programming (MIP) problem with inherent nonlinearity and nonconvexity. To address the challenge in solving the problem, the original problem is decomposed into two sub-stages, and a two-stage optimization (TSO) based approach is presented to ensure the coherence in outcomes for the two stages. The optimization problem in the first stage determines the optimal decision sequence that acts as an informed initialization. With the outputs from the first stage, the second stage necessitates the use of a high-fidelity vehicle model and strict enforcement of the collision avoidance constraints as part of the trajectory planning problem. We evaluate the effectiveness of our proposed planner across diverse multi-lane scenarios. The results demonstrate that the proposed planner simultaneously generates a sequence of optimal decisions and the corresponding trajectory that significantly improves driving performance in terms of driving safety and traveling efficiency as compared to alternative methods. Additionally, we implement the closed-loop simulation in CARLA, and the results showcase the effectiveness of the proposed planner to adapt to changing driving situations with high computational efficiency.