# An Integrated Framework of Decision Making and Motion Planning for Autonomous Vehicles Considering Social Behaviors

arXiv: https://arxiv.org/abs/2005.11059 · HTML: https://mlsanity.com/paper/2005.11059/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2020.

## Built on by (most cited first, 17 of 17)

- [Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Driving](https://mlsanity.com/paper/2207.10422/) (2022)
- [Hybrid-Prediction Integrated Planning for Autonomous Driving](https://mlsanity.com/paper/2402.02426/) (2024)
- [Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation](https://mlsanity.com/paper/2311.16091/) (2023)
- [Occupancy Prediction-Guided Neural Planner for Autonomous Driving](https://mlsanity.com/paper/2305.03303/) (2023)
- [Discretionary Lane-Change Decision and Control via Parameterized Soft Actor-Critic for Hybrid Action Space](https://mlsanity.com/paper/2402.15790/) (2024)
- [A Cooperation-Aware Lane Change Method for Autonomous Vehicles](https://mlsanity.com/paper/2201.10746/) (2022)
- [Agile Decision-Making and Safety-Critical Motion Planning for Emergency Autonomous Vehicles](https://mlsanity.com/paper/2409.08665/) (2024)
- [Adaptive Decision-Making for Autonomous Vehicles: A Learning-Enhanced Game-Theoretic Approach in Interactive Environments](https://mlsanity.com/paper/2402.11467/) (2024)
- [PreGSU-A Generalized Traffic Scene Understanding Model for Autonomous Driving based on Pre-trained Graph Attention Network](https://mlsanity.com/paper/2404.10263/) (2024)
- [Unified End-to-End V2X Cooperative Autonomous Driving](https://mlsanity.com/paper/2405.03971/) (2024)
- [A Conflicts-free, Speed-lossless KAN-based Reinforcement Learning Decision System for Interactive Driving in Roundabouts](https://mlsanity.com/paper/2408.08242/) (2024)
- [Synergizing Decision Making and Trajectory Planning Using Two-Stage Optimization for Autonomous Vehicles](https://mlsanity.com/paper/2411.18974/) (2024)
- [Markov Potential Game and Multi-Agent Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2603.19188/) (2026)
- [Measuring Sociality in Driving Interaction](https://mlsanity.com/paper/2306.13992/) (2023)
- [Safe Hybrid-Action Reinforcement Learning-Based Decision and Control for Discretionary Lane Change](https://mlsanity.com/paper/2403.00446/) (2024)
- [Towards Developing Socially Compliant Automated Vehicles: Advances, Expert Insights, and A Conceptual Framework](https://mlsanity.com/paper/2501.06089/) (2025)
- [LUNA-AD: Lightweight Uncertainty-Aware Language Model with Lifelong Learning for Autonomous Driving](https://mlsanity.com/paper/2606.08470/) (2026)

## Abstract (arXiv)

This paper presents a novel integrated approach to deal with the decision making and motion planning for lane-change maneuvers of autonomous vehicle (AV) considering social behaviors of surrounding traffic occupants. Reflected by driving styles and intentions of surrounding vehicles, the social behaviors are taken into consideration during the modelling process. Then, the Stackelberg Game theory is applied to solve the decision-making, which is formulated as a non-cooperative game problem. Besides, potential field is adopted in the motion planning model, which uses different potential functions to describe surrounding vehicles with different behaviors and road constrains. Then, Model Predictive Control (MPC) is utilized to predict the state and trajectory of the autonomous vehicle. Finally, the decision-making and motion planning is then integrated into a constrained multi-objective optimization problem. Three testing scenarios considering different social behaviors of surrounding vehicles are carried out to validate the performance of the proposed approach. Testing results show that the integrated approach is able to address different social interactions with other traffic participants, and make proper and safe decisions and planning for autonomous vehicles, demonstrating its feasibility and effectiveness.