Antikollisionssystem Proreta–Auf dem Weg zum unfallvermeidenden Fahrzeug

Bender, Eva; Darms, Michael; Schorn, Matthias; Stählin, Ulrich; Isermann, Rolf

doi:10.1007/bf03221883

Cited by 9 publications

(2 citation statements)

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“…This receding horizon concept is used in model predictive control [34]. Because of the nonlinear dynamic model (5) and the nonlinear cost functional (4), the theory of nonlinear model predictive control (NMPC) must be applied. The realtime implementation is a very current subject of research, where promising-sounding approaches such as [35] already exist.…”

Section: Combination Of Potential Field Methods and Optimal Control Thmentioning

confidence: 99%

“…The first project considered two vehicles moving in the same direction. If the driver of the rear vehicle does not react to the obstacle in the same lane, the system triggers an emergency braking and/or automatic steering in order to avoid the collision [5]. The second project was devoted to the prevention of overtaking accidents on rural roads.…”

Section: Introductionmentioning

confidence: 99%

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PRORETA 3: An Integrated Approach to Collision Avoidance and Vehicle Automation

Bauer¹,

Lotz²,

Pfromm³

et al. 2012

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The article describes first results of the research project PRORETA 3 that aims at the development of an integral driver assistance system for collision avoidance and automated vehicle guidance based on a modular system architecture. For this purpose, relevant information is extracted from a dense environment model and fed into a potential field-based trajectory planner that calculates reference signals for underlying vehicle controllers. In addition, the driver is supported by a human-machine interface. Zusammenfassung Der Beitrag beschreibt erste Ergebnisse des Forschungsprojektes PRORETA 3, das die Entwicklung eines integralen Fahrerassistenzsystems zur Kollisionsvermeidung und automatisierten Fahrzeugführung auf Basis einer modularen Systemarchitektur anstrebt. Hierzu werden relevante Informationen aus einem dichten Umfeldmodell extrahiert und in einem potentialfeldbasierten Trajektorienplaner verarbeitet, der Führungsgrößen für unterlagerte Fahrzeugregler generiert. Zusätzlich unterstützt eine Mensch-Maschine-Schnittstelle den Fahrer zielgerichtet bei der Fahrzeugführung.

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Section: Combination Of Potential Field Methods and Optimal Control Thmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%