Automatic lane change extraction based on temporal patterns of symbolized driving behavioral data

This paper proposes a method of automatically extracting lane change situations from large-scale driving corpora. Naturalistic driving data stored in large-scale corpora has a potential of contributing for developing novel advanced driver-assistance systems based on estimated information about driver´s intent and/or potential risk of accidents. However, direct estimation of such kind of information from stream data is difficult. To address the issue, we apply an unsupervised symbolization method and topic representation to driving data. Driving stream data is converted to sequences of discrete symbols by a non-parametric symbolization method, and then the symbols are characterized by topics which represent typical distribution of driving behavior observed during the symbols. Because these symbols are separated on changing points of driving behavior, similar driving situations are effectively retrieved from sequences of the symbols. For evaluating effectiveness of the symbolization approach, we extract lane change situations based on the topic proportions and their temporal patterns. Distinctive elements of topic proportions and their temporal patterns for lane change situations are extracted by AdaBoost classifier. As a result, proposed approach outperforms baselines with neither topic proportions nor their temporal patterns in terms of extracting lane change situations. This result shows effectiveness of symbols with topic proportions for representing characteristics of driving situations.