  The non-stationary characteristics of EEG signal and the individual
differences of brain-computer interfaces (BCIs) lead to poor performance in the
control process of the brain-controlled vehicles (BCVs). In this paper, by
combining steady-state visual evoked potential (SSVEP) interactive interface,
brain instructions generation module and vehicle lateral control module, a
probabilistic output model based on support vector machine (SVM) is proposed
for BCV lateral control to improve the driving performance. Firstly, a filter
bank common spatial pattern (FBCSP) algorithm is introduced into the brain
instructions generation module, which can improve the off-line decoding
performance. Secondly, a sigmod-fitting SVM (SF-SVM) is trained based on the
sigmod-fitting method and the lateral control module is developed, which can
produce all commands in the form of probability instead of specific single
command. Finally, a pre-experiment and two road-keeping experiments are
conducted. In the pre-experiment, the experiment results show that, the average
highest off-line accuracy among subjects is 95.64\%, while for those in the
online stage, the average accuracy is only 84.44\%. In the road-keeping
experiments, the task completion rate in the two designed scenes increased by
25.6\% and 20\%, respectively.
