Automotive Group

 

The Automotive Group deals with contol problems relevant for road traffic. In this context, both the control of individual vehicle components and the interaction of different road user are considered.

Contact

Photo of Tobias Quack

Name

Tobias Quack

Automotive Group Manager

Phone

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+49 241 80 27505

Email

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Networked Mobility

When talking about sustainable mobility, there exist four main challenges:

  • Road safety
  • traffic efficiency
  • energy efficiency
  • urban life quality.

The interconnection of road users and the infrastructure is a core element of all these challenges. For future mobility applications, an efficient information and communication technology is essential.

 
  Picture Networked mobility IRT

The main research focus of the institute of automatic control are applications resulting from new communication technologies between vehicles, pedestrians infrastructure and additional road users and objects. The focus lies on cooperative localization and environment perception and on cooperative driver assistance systems.

 
 

Vehicle Dynamics Control

Advanced driver assistance systems support the driver in his task of driving and can make partially autonomous driving maneuvers. These systems can help increase road safety significantly. Additionally, they can increase traveling comfort and reduce fuel consumption. Advanced driver assistance systems range from informatory systems like a collision warning to systems with an active intervention like a collision avoidance system or an adaptive cruise control system.

The institute of automatic control has its focus on model based control of the lateral and longitudinal dynamics of the vehicle and the processing of the relevant information with the help of sensor fusion algorithms. A special interest lies in system approaches using global navigation satellite systems like Galileo.

 
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Video for vehicle dynamics control - the vehicle evades autonomously
 

Advanced driver assistance systems support the driver in his task of driving and can make partially autonomous driving maneuvers. These systems can help increase road safety significantly. Additionally, they can increase traveling comfort and reduce fuel consumption. Advanced driver assistance systems range from informatory systems like a collision warning to systems with an active intervention like a collision avoidance system or an adaptive cruise control system.

The institute of automatic control has its focus on model based control of the lateral and longitudinal dynamics of the vehicle and the processing of the relevant information with the help of sensor fusion algorithms. A special interest lies in system approaches using global navigation satellite systems like Galileo.

 

Autonomous and Highly Automated Driving

Buggy IRT

The reason for a majority of road accidents are human errors. Therefore, road safety can be increased significantly, if the driver is supported in his tasks or even relieved completely from it. The driver can then concentrate on other tasks during driving.

The institute of automatic control studies autonomous and highly automated driving. The level of automatization reaches from individual driver assistance systems to a complete combined control of longitudinal and lateral dynamics of the vehicle. The research focus lies on localization and positioning both of the ego-vehicle and potential obstacles with the help of sensor fusion, vehicle dynamics control, the cooperation of different autonomous vehicles and maneuver planning.

 

Energy Management Strategies for Hybrid Electric Vehicles

The institute of automatic control conducts research related to the development of fuel efficient and charge sustaining optimization based energy management strategies for hybrid and plug-in hybrid electric vehicles. Apart from control algorithms designed for an a priori given driving cycle, the focus of research lies on the development of control strategies aimed for real-world driving cycles which are at the same time robust with respect to uncertainties in driver actions resulting from the oncoming traffic or individual driving behavior. A special interest is placed on robust and stochastic model predictive control which enables the aforementioned behavior of the hybrid powertrain. In addition, research is complemented by the development of driver models, profile prediction units for estimation of future driver demand with respect to the traffic infrastructure and flow. Finally, optimal estimators for the estimation of battery state of-charge is developed. The control concepts are evaluated in the scope of a Hardware in the Loop simulator for parallel hybrid electric vehicles.