Situation analysis for driver assistance systems at urban intersections

Intersections are one major accident location type. This paper presents a situation analysis that combines a driver intent prediction and probabilistic reachable sets to determine the criticality of a scene. The intent prediction algorithm is presented and its evaluation in a study in real world driving. It is explained how the reachable sets are calculated and the collision probability is defined. These approaches use detailed digital maps and a graph based environmental model to perceive these complex situations.

[1]  Christian Laugier,et al.  Exploiting map information for driver intention estimation at road intersections , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[2]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[3]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[4]  Lars C. Wolf,et al.  RoadGraph: High level sensor data fusion between objects and street network , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[5]  Gee Wah Ng,et al.  Learning Bayesian Network Parameters from Soft Data , 2009, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[6]  Matthias Althoff,et al.  Model-Based Probabilistic Collision Detection in Autonomous Driving , 2009, IEEE Transactions on Intelligent Transportation Systems.

[7]  W. Marsden.  I and J , 2012 .

[8]  Adnan Darwiche,et al.  Modeling and Reasoning with Bayesian Networks , 2009 .

[9]  Lars Petersson,et al.  Statistical Threat Assessment for General Road Scenes Using Monte Carlo Sampling , 2008, IEEE Transactions on Intelligent Transportation Systems.

[10]  Fredrik Gustafsson,et al.  Decision Making for Collision Avoidance Systems , 2002 .

[11]  John M. Noble,et al.  Bayesian Networks: An Introduction , 2009 .

[12]  Jianwei Zhang,et al.  Situation Analysis and Adaptive Risk Assessment for Intersection Safety Systems in Advanced Assisted Driving , 2009, AMS.

[13]  Kai Homeier,et al.  RoadGraph - Graph based environmental modelling and function independent situation analysis for driver assistance systems , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[14]  Simon Karrenberg.  Zur Erkennung unvermeidbarer Kollisionen von Kraftfahrzeugen mit Hilfe von Stellvertretertrajektorien , 2008 .

[15]  S Busch.  Entwicklung einer Bewertungsmethodik zur Prognose des Sicherheitsgewinns ausgewaehlter Fahrerassistenzsysteme , 2005 .

[16]  K. Schimmelpfennig,et al.  BEDEUTUNG DER QUERBESCHLEUNIGUNG IN DER UNFALLREKONSTRUKTION - SICHERHEITSGRENZE DES NORMALFAHRERS - , 1985 .

[17]  C. Bauer,et al.  Fahrerspezifische Analyse des Fahrverhaltens zur Parametrierung aktiver Sicherheitssysteme , 2010 .

[18]  Florin Gorunescu,et al.  Data Mining - Concepts, Models and Techniques , 2011, Intelligent Systems Reference Library.