Back to ASAM International Conference 2019

Simulation based Research of Optimal Longitudinal Control of Automated Vehicles with GLOSA Functionality

Dr. Steffen Kutter
Research group leader for highly automated driving and driving strategies
Technische Universität Dresden, Chair of Vehicle Mechatronics

co-author:
Dr. Stephan Uebel
Technische Universität Dresden

Besides increasing safety and driving comfort the automation of the longitudinal vehicle control task allows the online-calculation of velocity trajectories leading to increased energy efficiency. Considering vehicle dynamics as well as speed limits and road profiles, the increased energy efficiency is achieved by optimizing operating points and by minimizing the braking effort as well as stop times.

Especially in urban areas it is essential to consider the current and the predicted states of multiple traffic lights ahead for calculating energy optimized approaching trajectories. Modern traffic lights are equipped with transmitters that send information about their actual and upcoming system states. Additionally, traffic lights connected to a traffic control center can broadcast their future signal phases to vehicles many kilometers ahead. This information can be used to adapt the vehicle speed using optimal control algorithms. The overall process is referred to as green light optimal speed advisory (GLOSA). Due to the need for a continuous recalculation of the optimal control problem to compensate unpredictable disturbances a model predictive control approach (MPC) is used leading to high demands in computation efficiency. Therefore, this work presents a novel MPC approach reducing calculation time from months to seconds and its simulation based development.