We propose a Bayesian trajectory prediction and criticality assessment system that allows to reason about imminent collisions of a vehicle several seconds in advance. We first infer a distribution of high-level, abstract driving maneuvers such as lane changes, turns, road followings, etc. of all vehicles within the driving scene by modeling the domain in a Bayesian network with both causal and diagnostic evidences. This is followed by maneuver-based, long-term trajectory predictions, which themselves contain random components due to the immanent uncertainty of how drivers execute specific maneuvers. Taking all uncertain predictions of all maneuvers of every vehicle into account, the probability of the ego vehicle colliding at least once within a time span is evaluated via Monte-Carlo simulations and given as a function of the prediction horizon. This serves as the basis for calculating a novel criticality measure, the Time-To-Critical-Collision-Probability (TTCCP) -a generalization of the common Time-To-Collision (TTC) in arbitrary, uncertain, multi-object driving environments and valid for longer prediction horizons. The system is applicable from highly-structured to completely non-structured environments and additionally allows the prediction of vehicles not behaving according to a specific maneuver class.
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.
We propose a highly compact, generic representation of the driving environment, so-called Parametric Free Space (PFS) maps, specifically suitable for future Advanced Driver Assistance Systems (ADAS) and bring them into line with existing metric representations known from mobile robotics. PFS maps combine a closed contour description of arbitrarily shaped outer free space boundaries with a representation of inner free space boundaries, respectively objects, by geometric primitives. A real-time capable algorithm is presented that obtains the representation by building upon an intermediate occupancy grid map-based environment representation generated from an automotive radar and a stereo camera. The proposed representation preserves all relevant information contained in a grid map -thus remaining function-independentin a much more compact way, which is considered particularly important for the transmission via low data-rate automotive communication interfaces. Examples are shown in real traffic scenarios.
The dissertation approaches the questions i) how to represent the driving environment in an environment model, ii) how to obtain such a representation, and iii) how to predict the traffic scene for criticality assessment. Bayesian inference provides the common framework of all designed methods. First, Parametric Free Space (PFS) maps are introduced, which compactly represent the vehicle environment in form of relevant, drivable free space. They are obtained by a novel method for grid mapping and tracking in dynamic environments. In addition, a maneuver-based, long-term trajectory prediction and criticality assessment system is introduced and the application of all methods within the advanced driver assistance system PRORETA 3 is described.
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