A lane change prediction algorithm based on probabilistic modeling

Abstract

According to the previous research, lane-changes are a major cause of serious traffic accidents. Thus, it is essential to build an efficient prediction algorithm for vehicles lane change on Advanced Driving Assistance System (ADAS) to ensure a safe and comfort driving for host vehicle. At present, many methods for lane change prediction have been proposed. However, most of them require a lot of data training to have a good prediction performance. Considering the practical applicability of the prediction algorithm in ADAS, this paper proposes a prediction method by probabilistic modelling. This method combines two aspects lane change evidence. On the one hand, model the lane change probability caused by the target vehicle's driving context. On the other hand, model the lane change probability reflected by vehicle posture in the road. The evaluation of the whole algorithms was done by using simulation data and real lane change data. The results show that the algorithm performs well in predicting accuracy and reducing false alarms.