A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Finite State Machines <s> This paper presents a general approach for recognition of driving maneuvers in advanced driver assistance systems (ADAS). Such systems often rely on the identification of driving maneuvers (overtaking, left turn at intersections, etc.) to improve the prediction of potential collisions or to trigger appropriate support for the driver. The proposed maneuver recognition approach combines a fuzzy rule base to model basic maneuver elements and probabilistic finite-state machines to capture all possible sequences of basic elements that constitute a driving maneuver. The proposed method is specifically tailored to ADAS requirements because of its low computational complexity, its flexibility and its straight-forward design based on easily comprehensible logical rules. In addition, we propose a suitable training method to optimize the fuzzy rule base. Our approach is evaluated on the recognition of turn maneuvers. Experiments on real data from a test vehicle demonstrate the feasibility of the proposed method. <s> BIB001 </s> A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms <s> Finite State Machines <s> This study proposes a probabilistic decision-making model for driving decisions. The decision-making process that is modeled stochastically is part of the Human Driver Model developed in an earlier study, in which perception, world-model and reflexive behavior are represented as separate modules. Finite-state machine design guidelines for decision-making models are provided to maximize state observability and resolution while maintaining a manageable size for state-machine. Two decision-making models useful for estimation and prediction of driver behavior are presented and one scenario-safety estimation application that uses the proposed decision-making model is given to illustrate the proposed methodology. <s> BIB002
In the driver manoeuvre recognition approach in BIB001 , a probabilistic FSM is used to completely capture the possible sequences of the core elements that represents a driving manoeuvre. The feasibility of this approach is justified through real driving data and recognition of turn manoeuvres with low computational complexity. In the Intelligent Transportation Systems (ITS) study conducted in BIB002 , driver decision-making is modelled stochastically in order to develop a human driver model. A FSM design guideline is proposed for a driver decision-making model, to maximise state observability and resolution while limiting the size of the state machine to realistic proportions. A graphical depiction of the state transition topology of the FSM design, proposed for a probabilistic decision making model at an intersection, is given in Figure 7 . The driver has to make a decision for instance to turn at the intersection, to stop at the intersection or to continue the original trajectory of the vehicle. This transition model can be adapted according to the needs of a particular application. 