# Real-Time Constrained Trajectory Planning and Vehicle Control for Proactive Autonomous Driving With Road Users

## TL;DR

This paper introduces a real-time, proactive vehicle motion planning and control framework using Model Predictive Control that accounts for moving pedestrians, validated through simulations and experiments demonstrating stability and low computational delay.

## Contribution

It presents a novel MPC-based framework incorporating pedestrian motion prediction into vehicle control for autonomous driving.

## Key findings

- Controller remains stable under input delays
- Framework achieves low computational times
- Validated with real pedestrian data in simulations

## Abstract

For motion planning and control of autonomous vehicles to be proactive and safe, pedestrians' and other road users' motions must be considered. In this paper, we present a vehicle motion planning and control framework, based on Model Predictive Control, accounting for moving obstacles. Measured pedestrian states are fed into a prediction layer which translates each pedestrians' predicted motion into constraints for the MPC problem. Simulations and experimental validation were performed with simulated crossing pedestrians to show the performance of the framework. Experimental results show that the controller is stable even under significant input delays, while still maintaining very low computational times. In addition, real pedestrian data was used to further validate the developed framework in simulations.