Generating contextual description from driving behavioral data
Takashi Bando, Kazuhito Takenaka, Shogo Nagasaka, Tadahiro Taniguchi · 2014
This paper presents an automatic translation method from time-series driving behavior into natural language with contextual information. Nowadays, various advanced driver-assistance systems (ADASs) have been developed to reduce the number of traffic accidents and multiple ADASs are required to reduce further accidents. For such multiple ADASs, considering the context of driving and selecting appropriate assistance is key because the systems have to handle extremely complicated driving situations consisting of drivers (and their intents and maneuvers), environments (including other traffic participants such as vehicles and pedestrians), and vehicles dynamics. In this paper, time-series driving behavior is segmented into typical driving situation symbols, and the natural language expression of each situation is generated via the behavioral feature distribution observed in each situation. Owing to the symbolization of the driving behavior, the generated behavioral descriptions can be associated with their causes and results not on an actual time axis but on a situation-symbol axis as contextual descriptions, e.g., “letting up on gas pedal to pass tollgate.” The effectiveness of the proposed method was evaluated by using an actual data set of more than eight hours over a distance of 300 km in total. Although contextual expressions are very diverse even among human drivers, the proposed method obtained an agreement of more than 70%.