Real-Time Constrained Trajectory Planning and Vehicle Control for Proactive Autonomous Driving with Road Users
Paper in proceeding, 2019

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.

road user avoidance

model predictive control

autonomous driving


Ivo Batkovic

Chalmers, Electrical Engineering, Systems and control

Mario Zanon

IMT School for Advanced Studies

Paolo Falcone

Chalmers, Electrical Engineering, Systems and control

Mohammad Ali

Zenuity AB

2019 18th European Control Conference, ECC 2019

978-3-907144-00-8 (ISBN)

18th European Control Conference (ECC)
Neapel, Italy,

Subject Categories

Control Engineering





More information

Latest update