Driving Behavior Prediction by Training a Hidden Markov Model

Abstract: Introducing automated vehicles in to traffic withhuman drivers, human behavior prediction is essential to obtainoperation safety. In this study, a human behavior estimationmodel has been developed. The estimations are based on aHidden Markov Model (HMM) using observations to determinethe driving style of surrounding vehicles. The model is trainedusing two different methods: Baum Welch training and Viterbitraining to improve the performance. Both training methods areevaluated by looking at time complexity and convergence. Themodel is implemented with and without training and tested fordifferent driving styles. Results show that training is essentialfor accurate human behavior prediction. Viterbi training is fasterbut more noise sensitive compared to Baum Welch training. Also,Viterbi training produces good results if training data reflects oncurrently observed driver, which is not always the case. BaumWelch training is more robust in such situations. Lastly, BaumWelch training is recommended to obtain operation safety whenintroducing automated vehicles into traffic.