Interactive Motion Planning
Codebase for adaptive interactive mixed-integer model predictive control (aiMPC): an optimal control-based interactive motion planning algorithm for autonomous vehicles.
- Real-time Software-and-Human-in-the-loop simulation in CARLA.
Test scenario illustration
Mandatory lane change scenario: a stopped truck on the right lane necessitates a lane change for the autonomous vehicle which needs to negotiate with a human-driven vehicle on the left lane.
![alt text](https://github.com/autonomous-viranjan/Interactive-Motion-Planning/blob/main/scenario.png)
- Blue vehicle is the ego vehicle and red vehicle is the human-driven neighboring vehicle (NV).
![shil](https://github.com/autonomous-viranjan/Interactive-Motion-Planning/assets/62226470/007e4ddd-783e-495a-a7bf-8f61333cc901)
- aiMPC estimates NV's cost online and adapts the MPC.
A case when ego merges ahead
https://github.com/autonomous-viranjan/Interactive-Motion-Planning/assets/62226470/80b97b3e-c7b9-4cf5-933c-67992a033649
A case when ego merges behind
https://github.com/autonomous-viranjan/Interactive-Motion-Planning/assets/62226470/f22663f6-780c-471e-94e3-66f41bb8012c
A case where the NV's nature changes
https://github.com/autonomous-viranjan/Interactive-Motion-Planning/assets/62226470/bacb1f65-077f-4fc9-8c1b-87f18a4aafa8
- αp and αa are the estimated NV cost weights.
Key dependencies:
- CARLA simulator
- Gurobi
- ROS Noetic
- MATLAB (data analysis)
Architecture
![shil-arch-1](https://github.com/autonomous-viranjan/Interactive-Motion-Planning/assets/62226470/7d8d609b-7c80-4473-9731-b24a91439f96)
- Cite as
@misc{bhattacharyya2023interactive,
title={Interactive Motion Planning for Autonomous Vehicles via Adaptive Interactive MPC},
author={Viranjan Bhattacharyya and Ardalan Vahidi},
year={2023},
eprint={2311.05810},
archivePrefix={arXiv},
primaryClass={eess.SY}
}