deletions | additions       diff --git a/subsection_Behaviour_and_Safety_Measures__.tex b/subsection_Behaviour_and_Safety_Measures__.tex  index 8e0a7df..a88e9e0 100644  --- a/subsection_Behaviour_and_Safety_Measures__.tex  +++ b/subsection_Behaviour_and_Safety_Measures__.tex 
...
\subsubsection{Advanced Motion Prediction for TTC}  As stated previously, computing the  TTC requires motion prediction methods to identify collision course situations. The most common motion prediction method is constant velocity. Given the non-linear driving required to navigate the deflection induced by roundabout central islands and approaches, a more sophisticated collision-course motion  prediction model is used in this work instead: thediscretized motion pattern  motion prediction model based on discretized motion pattern  developed specifically to address the issues of modeling movement in complex environments \citep{St_Aubin_2014} i.e. $TTC_{cmp}$. It like roundabouts~\citep{St_Aubin_2014}. %It  should be noted, however, that $TTC_{cmp}$ is by no means specific to roundabouts. Another advantage of advanced motion prediction methods, e.g.\ using discretized motion patterns, is that hey are probabilistic, that is that they take into account the uncertainty about the future road user positions. They may therefore also predict multiple potential collision points with associated probabilities and TTC for any single instant of interaction between two road users. These multiple measures are simply aggregated into the expected TTC denoted $TTC_{cmp}$ \citep{Saunier_2010,St_Aubin_2014}.  Furthermore,as was discussed, collision-courses and  TTC are modeled and is  measured continuously, continuously for two road users,  unlikemeasures of  yPET which result results  in a single measure between any two road users. Furthermore, multiple collision courses might be modeled at any one instant, resulting in multiple potential measures and collision points for any single instant of interaction between two road users. Probabilistic collision-course modeling, as in the case of discretized motion patterns, handle issues of multiple collision-courses by aggregating these measures of $TTC_{cmp}$ via a weighted average of collision-course probability \citep{St_Aubin_2014}.  Regardless of prediction method, this still leaves continuous values of measure. The  TTCover the  time seriesof instantaneous interactions between any two road users. The general approach to handling this issue  is to represent the entire timeseries with usually aggregated into  a single instant-aggregated signel  value, typically the minimum (i.e. most severe) value at any instant in the timeseries \citep[e.g.][]{Laureshyn_2010}. This however is somewhat sensitive to noisy data and outliers, and as such a \nth{15}-centile value, $TTC_{15^{th}cmp}$, might may  be used instead \cite{St_Aubin_2016_thesis}. \subsubsection{Data Analysis and Aggregation}