@inproceedings {pub3172,
	title = {Rider Skill Identification by Probabilistic Segmentation into 
Motorcycle Maneuver Primitives},
	author = {Nils Magiera AND Herbert Jan{\ss}en AND Martin Ernst Heckmann AND Hermann Winner},
	year = {2016},
	abstract = {As a result of the static and dynamic instabilities 
of  a  Powered-Two-Wheeler  the  rider  performs  a  highly 
demanding  control  task.  Rider  safety  strongly  depends  on 
individual  abilities  and  skills  of  the  rider.  To  improve  the 
riders{\textquoteright} skill level and reduce riding errors, safety trainings are 
well established. Additionally  safety systems and recently also 
advanced  rider  assistance  systems  help  to  avoid  or  mitigate 
accidents.  While  conventional  rider  training  is  limited  to  a 
small  number  of  training  scenarios  in  a  controlled 
environment,  safety  systems  are  typically  limited  to  specific 
situations  (collision  warning)  or  physical  limits  of  the  vehicle 
(ABS). We propose a method to identify riding errors based on 
a statistical rider model for cornering scenarios and estimate a 
personal rider skill score. By this it is intended to extend rider 
skill  training  beyond  organized  events  and  towards  personal 
self-training. Automatic  scoring of cornering skill level  has to 
take  into  account  the  high  variability  in  speed  and  local 
curvature  as  well  as  the  variety  of  different  traffic  situations 
that may be encountered during a ride. We suggest to split the 
complex driving task - e.g. riding along a winding road - into 
smaller control tasks - e.g. roll-into-corner, stable lean and roll-
out-of-corner-  and  analyze  them  separately  first,  then  their 
sequence  and  transitions.  We  evaluate  various  approaches 
based  on  Hidden  Markov  Models  that  can  perform  this  task 
and  show  indicators  for  rider  skill  based  on  the  best 
segmentation model.   },
	publisher = {IEEE},
	booktitle = {2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC 2016)}
}